WO2021251510A1

WO2021251510A1 - Method for transmitting uplink data or downlink data in wireless communication system, and device therefor

Info

Publication number: WO2021251510A1
Application number: PCT/KR2020/007382
Authority: WO
Inventors: 김민석; 김수남; 김재환
Original assignee: 엘지전자 주식회사
Priority date: 2020-06-08
Filing date: 2020-06-08
Publication date: 2021-12-16

Abstract

Proposed are a method for transmitting uplink data or downlink data in a wireless communication system, and a device therefor. Particularly, the method performed by a terminal comprises the steps of: receiving, from a base station, control information for scheduling an uplink; and transmitting the uplink data to the base station on the basis of the control information, wherein the control information and uplink data are based on an OFDM symbol, the OFDM symbol comprises a plurality of definite sequence durations, the plurality of definite sequence durations are non-consecutively positioned in an OFDM symbol duration, and the number of the plurality of definite sequence durations and/or the length of each definite sequence duration can be defined on the basis of a channel state or data characteristics.

Description

Method and apparatus for transmitting uplink data or downlink data in a wireless communication system

The present specification relates to a wireless communication system, and more particularly, to a method for transmitting uplink data or downlink data, and an apparatus supporting the same.

Wireless communication systems are being widely deployed to provide various types of communication services such as voice and data. In general, a wireless communication system is a multiple access system that can support communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.).

Examples of the multiple access system include a Code Division Multiple Access (CDMA) system, a Frequency Division Multiple Access (FDMA) system, a Time Division Multiple Access (TDMA) system, a Space Division Multiple Access (SDMA), or an Orthogonal Frequency Division Multiple Access (OFDMA) system. , SC-FDMA (Single Carrier Frequency Division Multiple Access) system, IDMA (Interleave Division Multiple Access) system, and the like.

The present specification proposes an OFDM symbol structure including a plurality of discontinuous deterministic sequence (eg, Unique Word) sections and a method of generating the OFDM symbol structure.

In addition, the present specification proposes a method of controlling the number of a plurality of deterministic sequence sections and/or the length of each deterministic sequence section in an OFDM symbol based on a channel state or characteristics of data.

In addition, the present specification proposes a method of controlling the number of a plurality of deterministic sequence sections and/or the length of each deterministic sequence section in an OFDM symbol based on machine learning.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. will be able

The present specification proposes a method for transmitting uplink data in a wireless communication system. The method performed by the terminal includes receiving, from a base station, uplink scheduling control information, and transmitting the uplink data to the base station based on the control information, wherein the control information and the uplink The link data is based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence intervals, the plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval, and/or the number of the plurality of deterministic sequence intervals and/or At least one of the lengths of each deterministic sequence section may be defined based on a channel state or a characteristic of data.

Also, in the method of the present specification, a length in microseconds of each of the plurality of deterministic sequence sections may have a value greater than a delay spread length based on the channel state.

Also, in the method of the present specification, the channel state may be a state in which a phase noise value of a channel is higher than a preset phase noise value.

In addition, in the method of the present specification, the microsecond length of the first or last deterministic sequence section in the OFDM symbol has the same value as the delay spread length based on the channel state, and other deterministic The length of the sequence sections in microseconds may have a smaller value than the delay spread length based on the channel state.

Also, in the method of the present specification, the channel state may be a state in which a phase noise value of a channel is higher than a preset phase noise value and an intersymbol interference value of a channel is higher than a preset intersymbol interference value.

Also, in the method of the present specification, at least one of the number of the plurality of deterministic sequence sections and/or the microsecond length of the deterministic sequence section may be determined based on machine learning.

Also, in the method of the present specification, the definitive sequence section may be a section to which a unique word (UW) is mapped.

Also, in the method of the present specification, the control information may be received through a Physical Downlink Control Channel (PDCCH).

Also, in the method of the present specification, the uplink data may be transmitted through a Physical Uplink Shared Channel (PUSCH).

In addition, the terminal for transmitting uplink data in the wireless communication system of the present specification, one or more transceivers, one or more processors functionally connected to the one or more transceivers, and functionally connected to the one or more processors, one or more memories storing instructions for performing operations, the operations comprising: receiving control information for scheduling an uplink from a base station; and transmitting the uplink data based on the control information to the base station and transmitting the control information and the uplink data to an OFDM symbol, wherein the OFDM symbol includes a plurality of deterministic sequence intervals, wherein the plurality of deterministic sequence intervals are discontinuous within the OFDM symbol interval. and at least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section may be defined based on a channel state or a characteristic of data.

In addition, in the method for transmitting downlink data in the wireless communication system of the present specification, the method performed by the base station includes transmitting control information for scheduling downlink to a terminal, and the downlink based on the control information. transmitting link data to the terminal, wherein the control information and the downlink data are based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence intervals, and the plurality of deterministic sequence intervals are OFDM symbols It is discontinuously arranged within a section, and at least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section may be defined based on a channel state or a characteristic of data.

Also, in the method of the present specification, the control information may be transmitted through a Physical Downlink Control Channel (PDCCH).

Also, in the method of the present specification, the downlink data may be transmitted through a Physical Downlink Shared Channel (PDSCH).

In addition, the base station for transmitting downlink data in the wireless communication system of the present specification, one or more transceivers, one or more processors functionally connected to the one or more transceivers, and functionally connected to the one or more processors, one or more memories for storing instructions for performing operations, the operations comprising: transmitting downlink scheduling control information to the terminal; and transmitting the downlink data to the terminal based on the control information and transmitting the control information and the downlink data to an OFDM symbol, wherein the OFDM symbol includes a plurality of deterministic sequence intervals, wherein the plurality of deterministic sequence intervals are discontinuous within the OFDM symbol interval. and at least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section may be defined based on a channel state or a characteristic of data.

In addition, in an apparatus including one or more memories of the present specification and one or more processors operatively connected to the one or more memories, the one or more processors enable the apparatus to transmit control information for scheduling an uplink from a base station. and configured to transmit uplink data to the base station based on the control information, wherein the control information and the uplink data are based on an OFDM symbol, and the OFDM symbol includes a plurality of deterministic sequence intervals; A plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval, and at least one of the number of the plurality of deterministic sequence intervals and/or a length of each deterministic sequence interval may be defined based on a channel state or a characteristic of data. .

In addition, in a non-transitory computer readable medium (CRM) storing one or more instructions of the present specification, the one or more instructions executable by one or more processors may include, by a terminal, control information for scheduling an uplink. receive from a base station and transmit uplink data to the base station based on the control information, wherein the control information and the uplink data are based on an OFDM symbol, and the OFDM symbol includes a plurality of deterministic sequence intervals; The plurality of deterministic sequence intervals may be discontinuously disposed within an OFDM symbol interval, and at least one of the number of the plurality of deterministic sequence intervals and/or the length of each deterministic sequence interval may be defined based on a channel state or a characteristic of data. have.

According to the present specification, it is possible to estimate and compensate for phase noise and/or inter-symbol interference varying within an OFDM symbol by applying an OFDM symbol structure including a plurality of discontinuous deterministic sequence (eg, Unique Word) sections. can have an effect.

Further, according to the present specification, by controlling the number of a plurality of deterministic sequence sections and/or the length of each deterministic sequence section in an OFDM symbol based on a channel state or characteristics of data, a communication channel of phase noise and/or inter-symbol interference It has the effect of realizing a low-latency and high-reliability communication system even in (eg, a communication channel in the terahertz band).

In addition, according to the present specification, by controlling the number of a plurality of deterministic sequence sections and/or the length of each deterministic sequence section in the OFDM symbol based on machine learning, there is an effect that a low-latency and high-reliability communication system can be implemented. .

Effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention pertains from the description below.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description to help the understanding of the present invention, provide embodiments of the present invention, and together with the detailed description, explain the technical features of the present invention.

1 illustrates physical channels and general signal transmission used in a 3GPP system.

2 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

3 shows an example of a perceptron structure.

4 shows an example of a multilayer perceptron structure.

5 shows an example of a deep neural network.

6 shows an example of a convolutional neural network.

7 is a diagram illustrating an example of a filter operation in a convolutional neural network.

8 shows an example of a neural network structure in which a cyclic loop exists.

9 shows an example of an operation structure of a recurrent neural network.

10 shows an example of an electromagnetic spectrum.

11 is a diagram showing an example of THz communication application.

12 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

13 is a diagram illustrating an example of a method of generating an optical device-based THz signal.

14 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.

15 illustrates the structure of a photoinc source-based transmitter.

16 illustrates a structure of an optical modulator.

17 shows the phase noise difference between the PLL frequency and the reference frequency.

18 shows the magnitude of the phase noise and the change of the phase noise for each center frequency.

19 shows an example of an NR frame structure in which PTRS is inserted.

20 shows the LI-CPE method demonstrated in a free-running oscillator with 100 Hz spectral width in 6 OFDM symbols.

21 is a diagram for explaining a method of generating a UW-OFDM symbol.

22 shows various transmission data structures.

23 shows a UW-OFDM transmission structure.

24 shows an example of an OFDM symbol having a plurality of discontinuous UW intervals.

25 shows symbol structure 1.

26 shows symbol structure 2;

27 shows the phase noise estimation and compensation structure of symbol structure 2.

28 shows symbol structure 3;

29 shows the phase noise estimation and compensation structure of symbol structure 3;

30 is a flowchart illustrating a method of operating a terminal proposed in the present specification.

31 is a flowchart illustrating an operation method of a base station proposed in the present specification.

32 illustrates a communication system 10 applied to the present invention.

33 illustrates a wireless device applicable to the present invention.

34 illustrates a signal processing circuit for a transmission signal.

35 shows another example of a wireless device to which the present invention is applied.

36 illustrates a portable device to which the present invention is applied.

37 illustrates a vehicle or an autonomous driving vehicle to which the present invention is applied.

38 illustrates a vehicle to which the present invention is applied.

39 illustrates an XR device applied to the present invention.

40 illustrates a robot applied to the present invention.

41 illustrates an AI device applied to the present invention.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without these specific details.

In some cases, well-known structures and devices may be omitted or shown in block diagram form focusing on core functions of each structure and device in order to avoid obscuring the concept of the present invention.

In this specification, the base station has a meaning as a terminal node of a network that directly communicates with the terminal. A specific operation described as being performed by the base station in this document may be performed by an upper node of the base station in some cases. That is, it is obvious that various operations performed for communication with the terminal in a network including a plurality of network nodes including the base station may be performed by the base station or other network nodes other than the base station. 'Base station (BS: Base Station)' is a fixed station (fixed station), Node B, eNB (evolved-NodeB), BTS (base transceiver system), access point (AP: Access Point), gNB (general NB, generation NB) may be replaced by terms such as In addition, 'terminal' may be fixed or have mobility, and UE (User Equipment), MS (Mobile Station), UT (user terminal), MSS (Mobile Subscriber Station), SS (Subscriber Station), AMS ( Advanced Mobile Station), a wireless terminal (WT), a machine-type communication (MTC) device, a machine-to-machine (M2M) device, a device-to-device (D2D) device, and the like.

Hereinafter, downlink (DL: downlink) means communication from a base station to a terminal, and uplink (UL: uplink) means communication from a terminal to a base station. In the downlink, the transmitter may be a part of the base station, and the receiver may be a part of the terminal. In the uplink, the transmitter may be a part of the terminal, and the receiver may be a part of the base station.

Specific terms used in the following description are provided to help the understanding of the present invention, and the use of these specific terms may be changed to other forms without departing from the technical spirit of the present invention.

The following techniques can be used in various radio access systems such as CDMA, FDMA, TDMA, OFDMA, SC-FDMA, and the like. CDMA may be implemented with a radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA may be implemented with a radio technology such as Global System for Mobile communications (GSM)/General Packet Radio Service (GPRS)/Enhanced Data Rates for GSM Evolution (EDGE). OFDMA may be implemented with a radio technology such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, Evolved UTRA (E-UTRA), and the like. UTRA is part of the Universal Mobile Telecommunications System (UMTS). 3GPP (3rd Generation Partnership Project) Long Term Evolution (LTE) is a part of Evolved UMTS (E-UMTS) using E-UTRA and LTE-A (Advanced)/LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE/LTE-A/LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

Embodiments of the present invention may be supported by standard documents disclosed in at least one of IEEE 802, 3GPP, and 3GPP2, which are wireless access systems. That is, steps or parts not described in order to clearly reveal the technical spirit of the present invention among the embodiments of the present invention may be supported by the above documents. In addition, all terms disclosed in this document may be described by the standard document.

For clarity of explanation, although description is based on a 3GPP communication system (eg, LTE, NR, etc.), the technical spirit of the present invention is not limited thereto. LTE refers to technology after 3GPP TS 36.xxx Release 8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR refers to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and/or Release 18. "xxx" stands for standard document detail number. LTE/NR/6G may be collectively referred to as a 3GPP system. For background art, terms, abbreviations, etc. used in the description of the present invention, reference may be made to matters described in standard documents published before the present invention. For example, you can refer to the following documents:

3GPP LTE

- 36.211: Physical channels and modulation

- 36.212: Multiplexing and channel coding

- 36.213: Physical layer procedures

- 36.300: Overall description

- 36.331: Radio Resource Control (RRC)

3GPP NR

- 38.211: Physical channels and modulation

- 38.212: Multiplexing and channel coding

- 38.213: Physical layer procedures for control

- 38.214: Physical layer procedures for data

- 38.300: NR and NG-RAN Overall Description

- 38.331: Radio Resource Control (RRC) protocol specification

물리 채널 및 프레임 구조Physical Channels and Frame Structure

Physical channels and general signal transmission

1 illustrates physical channels and general signal transmission used in a 3GPP system. In a wireless communication system, a terminal receives information through a downlink (DL) from a base station, and the terminal transmits information through an uplink (UL) to the base station. Information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist according to the type/use of the information they transmit and receive.

When the terminal is powered on or newly enters a cell, the terminal performs an initial cell search operation, such as synchronizing with the base station (S11). To this end, the terminal receives a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station, synchronizes with the base station, and obtains information such as a cell ID. Thereafter, the terminal may receive a physical broadcast channel (PBCH) from the base station to obtain intra-cell broadcast information. On the other hand, the UE may receive a downlink reference signal (DL RS) in the initial cell search step to check the downlink channel state.

After the initial cell search, the UE receives a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to information carried on the PDCCH to obtain more specific system information. It can be done (S12).

On the other hand, when first accessing the base station or there is no radio resource for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (S13 to S16). To this end, the UE transmits a specific sequence as a preamble through a Physical Random Access Channel (PRACH) (S13 and S15), and a response message to the preamble through the PDCCH and the corresponding PDSCH ((Random Access (RAR)) Response) message) In the case of contention-based RACH, a contention resolution procedure may be additionally performed (S16).

After performing the procedure as described above, the UE performs PDCCH/PDSCH reception (S17) and Physical Uplink Shared Channel (PUSCH)/Physical Uplink Control Channel (Physical Uplink) as a general uplink/downlink signal transmission procedure. Control Channel (PUCCH) transmission (S18) may be performed. In particular, the UE may receive downlink control information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and different formats may be applied according to the purpose of use.

On the other hand, the control information that the terminal transmits to the base station through the uplink or the terminal receives from the base station includes a downlink/uplink ACK/NACK signal, a channel quality indicator (CQI), a precoding matrix index (PMI), and a rank indicator (RI). ) and the like. The UE may transmit the above-described control information such as CQI/PMI/RI through PUSCH and/or PUCCH.

상향링크 및 하향링크 채널의 구조Structures of uplink and downlink channels

Downlink Channel Structure

The base station transmits a related signal to the terminal through a downlink channel to be described later, and the terminal receives the related signal from the base station through a downlink channel to be described later.

(1) Physical Downlink Shared Channel (PDSCH)

PDSCH carries downlink data (eg, DL-shared channel transport block, DL-SCH TB), and modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM are available. applies. A codeword is generated by encoding the TB. A PDSCH can carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and modulation symbols generated from each codeword are mapped to one or more layers (Layer mapping). Each layer is mapped to a resource together with a demodulation reference signal (DMRS), is generated as an OFDM symbol signal, and is transmitted through a corresponding antenna port.

(2) Physical Downlink Control Channel (PDCCH)

The PDCCH carries downlink control information (DCI) and a QPSK modulation method is applied. One PDCCH is composed of 1, 2, 4, 8, 16 CCEs (Control Channel Elements) according to an Aggregation Level (AL). One CCE consists of six REGs (Resource Element Groups). One REG is defined as one OFDM symbol and one (P)RB.

The UE obtains DCI transmitted through the PDCCH by performing decoding (aka, blind decoding) on the set of PDCCH candidates. A set of PDCCH candidates decoded by the UE is defined as a PDCCH search space set. The search space set may be a common search space or a UE-specific search space. The UE may acquire DCI by monitoring PDCCH candidates in one or more search space sets configured by MIB or higher layer signaling.

Uplink Channel Structure

The terminal transmits a related signal to the base station through an uplink channel to be described later, and the base station receives the related signal from the terminal through an uplink channel to be described later.

(1) Physical Uplink Shared Channel (PUSCH)

PUSCH carries uplink data (eg, UL-shared channel transport block, UL-SCH TB) and/or uplink control information (UCI), and CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform (waveform) , DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) is transmitted based on the waveform. When the PUSCH is transmitted based on the DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. For example, when transform precoding is not possible (eg, transform precoding is disabled), the UE transmits a PUSCH based on the CP-OFDM waveform, and when transform precoding is possible (eg, transform precoding is enabled), the UE transmits the CP-OFDM PUSCH may be transmitted based on a waveform or a DFT-s-OFDM waveform. PUSCH transmission is dynamically scheduled by a UL grant in DCI, or based on higher layer (eg, RRC) signaling (and/or Layer 1 (L1) signaling (eg, PDCCH)) semi-statically. Can be scheduled (configured grant). PUSCH transmission may be performed on a codebook-based or non-codebook-based basis.

(2) Physical Uplink Control Channel (PUCCH)

The PUCCH carries uplink control information, HARQ-ACK and/or a scheduling request (SR), and may be divided into a plurality of PUCCHs according to the PUCCH transmission length.

6G 시스템 일반6G system general

6G (wireless) systems have (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to reduce energy consumption of battery-free IoT devices, (vi) ultra-reliable connections, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be in four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing an example of the requirements of the 6G system.

6G systems include Enhanced mobile broadband (eMBB), Ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, Tactile internet, High throughput, High network capacity, High energy efficiency, Low backhaul and It may have key factors such as access network congestion and enhanced data security.

6G systems are expected to have 50 times higher simultaneous wireless connectivity than 5G wireless communication systems. URLLC, a key feature of 5G, will become an even more important technology by providing an end-to-end delay of less than 1ms in 6G communication. 6G systems will have much better volumetric spectral efficiencies as opposed to frequently used areal spectral efficiencies. The 6G system can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices will not need to be charged separately in the 6G system. New network characteristics in 6G may be as follows.

- Satellites integrated network: 6G is expected to be integrated with satellites to provide a global mobile population. The integration of terrestrial, satellite and public networks into one wireless communication system is very important for 6G.

- Connected intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary, and will update the wireless evolution from “connected things” to “connected intelligence.” in each procedure of treatment).

- Seamless integration wireless information and energy transfer: The 6G wireless network will deliver power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

- Ubiquitous super 3D connectivity: access to networks and core network functions of drones and very low Earth orbiting satellites will create super 3D connectivity in 6G ubiquitous.

In the above new network characteristics of 6G, some general requirements may be as follows.

- Small cell networks: The idea of small cell networks was introduced to improve the received signal quality as a result of improved throughput, energy efficiency and spectral efficiency in cellular systems. As a result, small cell networks are essential characteristics for communication systems beyond 5G and Beyond 5G (5GB). Accordingly, the 6G communication system also adopts the characteristics of the small cell network.

- Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of 6G communication systems. A multi-tier network composed of heterogeneous networks improves overall QoS and reduces costs.

- High-capacity backhaul: A backhaul connection is characterized as a high-capacity backhaul network to support high-capacity traffic. High-speed fiber optics and free-space optics (FSO) systems may be possible solutions to this problem.

- Radar technology integrated with mobile technology: High-precision localization (or location-based service) through communication is one of the functions of the 6G wireless communication system. Therefore, the radar system will be integrated with the 6G network.

- Softwarization and virtualization: Softening and virtualization are two important features that underlie the design process in 5GB networks to ensure flexibility, reconfigurability and programmability. In addition, billions of devices can be shared in a shared physical infrastructure.

6G 시스템의 핵심 구현 기술Core implementation technology of 6G system

Artificial Intelligence

The most important and newly introduced technology for 6G systems is AI. AI was not involved in the 4G system. 5G systems will support partial or very limited AI. However, the 6G system will be AI-enabled for full automation. Advances in machine learning will create more intelligent networks for real-time communication in 6G. Incorporating AI into communications can simplify and enhance real-time data transmission. AI can use numerous analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.

Time-consuming tasks such as handovers, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in M2M, machine-to-human and human-to-machine communication. In addition, AI can be a rapid communication in BCI (Brain Computer Interface). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

Recently, attempts have been made to integrate AI with wireless communication systems, but these have been focused on the application layer and network layer, especially deep learning, in the field of wireless resource management and allocation. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission in the physical layer are appearing. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and It may include an allocation (allocation) and the like.

Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, and the like in a physical layer of a downlink (DL). In addition, machine learning may be used for antenna selection, power control, symbol detection, and the like in a MIMO system.

However, the application of DNN for transmission in the physical layer may have the following problems.

Deep learning-based AI algorithms require large amounts of training data to optimize training parameters. However, due to a limitation in acquiring data in a specific channel environment as training data, a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a wireless channel.

In addition, current deep learning mainly targets real signals. However, signals of the physical layer of wireless communication are complex signals. In order to match the characteristics of a wireless communication signal, further research on a neural network for detecting a complex domain signal is needed.

Hereinafter, machine learning will be described in more detail.

Machine learning refers to a set of actions that trains a machine to create a machine that can perform tasks that humans can or cannot do. Machine learning requires data and a learning model. In machine learning, data learning methods can be roughly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

Neural network learning is to minimize errors in output. Neural network learning repeatedly inputs training data into the neural network, calculates the output and target errors of the neural network for the training data, and backpropagates the neural network error from the output layer of the neural network to the input layer in the direction to reduce the error. ) to update the weight of each node in the neural network.

Supervised learning uses training data in which the correct answer is labeled in the training data, and in unsupervised learning, the correct answer may not be labeled in the training data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which categories are labeled for each of the training data. The labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. The calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation. A change amount of the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stage of learning a neural network, a high learning rate can be used to increase the efficiency by allowing the neural network to quickly obtain a certain level of performance, and in the late learning period, a low learning rate can be used to increase the accuracy.

The learning method may vary depending on the characteristics of the data. For example, when the purpose of accurately predicting data transmitted from a transmitter in a communication system is at a receiver, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

The learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called

The neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and Recurrent Boltzmann Machine (RNN) methods. have.

An artificial neural network is an example of connecting several perceptrons.

3 shows an example of a perceptron structure.

Referring to FIG. 3, when an input vector x=(x1,x2,...,xd) is input, each component is multiplied by a weight (W1,W2,...,Wd), and after summing all the results, The whole process of applying the activation function σ() is called a perceptron. The huge artificial neural network structure may extend the simplified perceptron structure shown in FIG. 3 to apply input vectors to different multidimensional perceptrons. For convenience of description, an input value or an output value is referred to as a node.

Meanwhile, the perceptron structure shown in FIG. 3 can be described as being composed of a total of three layers based on an input value and an output value. An artificial neural network in which H (d+1)-dimensional perceptrons exist between the 1st layer and the 2nd layer and K (H+1)-dimensional perceptrons exist between the 2nd layer and the 3rd layer can be expressed as shown in FIG. 4 . 4 shows an example of a multilayer perceptron structure.

The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all the layers located between the input layer and the output layer are called hidden layers. In the example of FIG. 4 , three layers are disclosed, but when counting the actual number of artificial neural network layers, the input layer is counted except for the input layer, so it can be viewed as a total of two layers. The artificial neural network is constructed by connecting the perceptrons of the basic blocks in two dimensions.

The aforementioned input layer, hidden layer, and output layer can be jointly applied in various artificial neural network structures such as CNN and RNN to be described later as well as multi-layer perceptron. As the number of hidden layers increases, the artificial neural network becomes deeper, and a machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning. Also, an artificial neural network used for deep learning is called a deep neural network (DNN).

The deep neural network shown in FIG. 5 is a multilayer perceptron composed of eight hidden layers + output layers. The multi-layered perceptron structure is referred to as a fully-connected neural network. In a fully connected neural network, a connection relationship does not exist between nodes located in the same layer, and a connection relationship exists only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of a number of hidden layers and activation functions, so it can be usefully applied to figure out the correlation between input and output. Here, the correlation characteristic may mean a joint probability of input/output.

'On the other hand, depending on how a plurality of perceptrons are connected to each other, various artificial neural network structures different from the above-described DNN can be formed.

In DNN, nodes located inside one layer are arranged in a one-dimensional vertical direction. However, in FIG. 6 , it can be assumed that the nodes are two-dimensionally arranged with w horizontally and h vertical nodes (convolutional neural network structure of FIG. 6 ). In this case, since a weight is added per connection in the connection process from one input node to the hidden layer, a total of hХw weights must be considered. Since there are hХw nodes in the input layer, a total of h2w2 weights are needed between two adjacent layers.

The convolutional neural network of FIG. 6 has a problem in that the number of weights increases exponentially according to the number of connections, so instead of considering the connection of all modes between adjacent layers, it is assumed that a filter with a small size exists in FIG. 7 As in Fig., the weighted sum and activation function calculations are performed on the overlapping filters.

One filter has a weight corresponding to the number corresponding to its size, and weight learning can be performed so that a specific feature on an image can be extracted and output as a factor. In FIG. 7 , a filter with a size of 3Х3 is applied to the upper left 3Х3 region of the input layer, and an output value obtained by performing weighted sum and activation function operations on the corresponding node is stored in z22.

The filter performs weighted sum and activation function operations while scanning the input layer by moving horizontally and vertically at regular intervals, and places the output value at the current filter position. Such a calculation method is similar to a convolution operation on an image in the field of computer vision, so a deep neural network with such a structure is called a convolutional neural network (CNN), and a hidden layer generated as a result of a convolution operation is called a convolutional layer. Also, a neural network having a plurality of convolutional layers is called a deep convolutional neural network (DCNN).

In the convolution layer, the number of weights can be reduced by calculating the weighted sum by including only nodes located in the region covered by the filter in the node where the filter is currently located. Due to this, one filter can be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which physical distance in a two-dimensional domain is an important criterion. Meanwhile, in CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through the convolution operation of each filter.

Meanwhile, there may be data whose sequence characteristics are important according to data properties. Considering the length variability and precedence relationship of the sequence data, one element in the data sequence is input at each timestep, and the output vector (hidden vector) of the hidden layer output at a specific time is input together with the next element in the sequence. A structure in which this method is applied to an artificial neural network is called a recurrent neural network structure.

8 shows an example of a neural network structure in which a cyclic loop exists.

Referring to FIG. 8 , a recurrent neural network (RNN) connects elements (x1(t), x2(t), ,..., xd(t)) of a certain gaze t on a data sequence to a fully connected neural network. In the input process, the immediately preceding time point t-1 is a structure in which the weighted sum and activation functions are applied by inputting the hidden vectors (z1(t1), z2(t1), ..., zH(t1)) together. The reason why the hidden vector is transferred to the next time point in this way is that information in the input vector at previous time points is considered to be accumulated in the hidden vector of the current time point.

9 shows an example of an operation structure of a recurrent neural network.

Referring to FIG. 9 , the recurrent neural network operates in a predetermined time sequence with respect to an input data sequence.

When the input vector at time 　1 (x1(t), x2(t), ,..., xd(t)) is input to the recurrent neural network, the hidden vector 　 (z1(1), z2(1),.. .,zH(1)) is input together with the input vector 　 (x1(2),x2(2),...,xd(2)) of the time point 　2 and the vector of the hidden layer 　 (z1( 2),z2(2) ,...,zH(2)) are determined. This process is repeatedly performed until time point 　2, time point 3, ,,, and time T.

On the other hand, when a plurality of hidden layers are arranged in a recurrent neural network, this is called a deep recurrent neural network (DRNN). The recurrent neural network is designed to be usefully applied to sequence data (eg, natural language processing).

As a neural network core used as a learning method, in addition to DNN, CNN, and RNN, Restricted Boltzmann Machine (RBM), deep belief networks (DBN), Deep Q-Network and It includes various deep learning techniques such as, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.

THz(Terahertz) 통신THz (Terahertz) communication

The data rate can be increased by increasing the bandwidth. This can be accomplished by using sub-THz communication with a wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, typically exhibit a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm. The 100GHz-300GHz band range (Sub THz band) is considered a major part of the THz band for cellular communication. If it is added to the sub-THz band and mmWave band, the 6G cellular communication capacity is increased. Among the defined THz bands, 300GHz-3THz is in the far-infrared (IR) frequency band. The 300GHz-3THz band is part of the broadband, but at the edge of the wideband, just behind the RF band. Thus, this 300 GHz-3 THz band shows similarities to RF. 10 shows an example of an electromagnetic spectrum.

The main characteristics of THz communication include (i) widely available bandwidth to support very high data rates, and (ii) high path loss occurring at high frequencies (high directional antennas are indispensable). The narrow beamwidth produced by the highly directional antenna reduces interference. The small wavelength of the THz signal allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This allows the use of advanced adaptive nesting techniques that can overcome range limitations.

Optical wireless technology

OWC technology is envisioned for 6G communications in addition to RF-based communications for all possible device-to-access networks. These networks connect to network-to-backhaul/fronthaul network connections. OWC technology has already been used since the 4G communication system, but will be used more widely to meet the needs of the 6G communication system. OWC technologies such as light fidelity, visible light communication, optical camera communication, and FSO communication based on a light band are well known technologies. Communication based on optical radio technology can provide very high data rates, low latency and secure communication. LiDAR can also be used for ultra-high-resolution 3D mapping in 6G communication based on wide bands.

FSO backhaul network

The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network. Thus, data transmission in an FSO system is similar to that of a fiber optic system. Therefore, FSO can be a good technology to provide backhaul connectivity in 6G systems along with fiber optic networks. Using FSO, very long-distance communication is possible even at distances of 10,000 km or more. FSO supports high-capacity backhaul connections for remote and non-remote areas such as sea, space, underwater, and isolated islands. FSO also supports cellular BS connectivity.

Massive MIMO technology

One of the key technologies to improve spectral efficiency is to apply MIMO technology. As MIMO technology improves, so does the spectral efficiency. Therefore, large-scale MIMO technology will be important in 6G systems. Since the MIMO technology uses multiple paths, a multiplexing technique and a beam generation and operation technique suitable for the THz band should also be considered important so that a data signal can be transmitted through one or more paths.

blockchain

Blockchain will become an important technology for managing large amounts of data in future communication systems. Blockchain is a form of distributed ledger technology, which is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. The blockchain is managed as a peer-to-peer network. It can exist without being managed by a centralized authority or server. Data on the blockchain is collected together and organized into blocks. Blocks are linked together and protected using encryption. Blockchain in nature perfectly complements IoT at scale with improved interoperability, security, privacy, reliability and scalability. Therefore, blockchain technology provides several features such as interoperability between devices, traceability of large amounts of data, autonomous interaction of different IoT systems, and large-scale connection stability of 6G communication systems.

3D Networking

The 6G system integrates terrestrial and public networks to support vertical expansion of user communications. 3D BS will be provided via low orbit satellites and UAVs. Adding a new dimension in terms of elevation and associated degrees of freedom makes 3D connections significantly different from traditional 2D networks.

quantum communication

In the context of 6G networks, unsupervised reinforcement learning of networks is promising. Supervised learning methods cannot label the massive amounts of data generated by 6G. Labeling is not required for unsupervised learning. Thus, this technique can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows networks to operate in a truly autonomous way.

drone

Unmanned Aerial Vehicles (UAVs) or drones will become an important element in 6G wireless communication. In most cases, high-speed data wireless connections are provided using UAV technology. A BS entity is installed in the UAV to provide cellular connectivity. UAVs have certain features not found in fixed BS infrastructure, such as easy deployment, strong line-of-sight links, and degrees of freedom with controlled mobility. During emergencies such as natural disasters, the deployment of terrestrial communications infrastructure is not economically feasible and sometimes cannot provide services in volatile environments. A UAV can easily handle this situation. UAV will become a new paradigm in the field of wireless communication. This technology facilitates the three basic requirements of wireless networks: eMBB, URLLC and mMTC. UAVs can also serve several purposes, such as improving network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, incident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.

Cell-free Communication

Tight integration of multiple frequencies and heterogeneous communication technologies is very important in 6G systems. As a result, users can seamlessly move from one network to another without having to make any manual configuration on the device. The best network is automatically selected from the available communication technologies. This will break the limitations of the cell concept in wireless communication. Currently, user movement from one cell to another causes too many handovers in high-density networks, causing handover failures, handover delays, data loss and ping-pong effects. 6G cell-free communication will overcome all of this and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies and different heterogeneous radios of devices.

Integration of wireless information and energy transmission

WIET uses the same fields and waves as wireless communication systems. In particular, the sensor and smartphone will be charged using wireless power transfer during communication. WIET is a promising technology for extending the life of battery-charging wireless systems. Therefore, devices without batteries will be supported in 6G communication.

Integration of sensing and communication

An autonomous wireless network is a function that can continuously detect dynamically changing environmental conditions and exchange information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

Consolidation of access backhaul networks

In 6G, the density of access networks will be enormous. Each access network is connected by backhaul connections such as fiber optic and FSO networks. To cope with a very large number of access networks, there will be tight integration between the access and backhaul networks.

Holographic Beamforming

Beamforming is a signal processing procedure that adjusts an antenna array to transmit a radio signal in a specific direction. A smart antenna or a subset of an advanced antenna system. Beamforming technology has several advantages such as high call-to-noise ratio, interference prevention and rejection, and high network efficiency. Hologram beamforming (HBF) is a new beamforming method that is significantly different from MIMO systems because it uses a software-defined antenna. HBF will be a very effective approach for efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

Big Data Analytics

Big data analytics is a complex process for analyzing various large data sets or big data. This process ensures complete data management by finding information such as hidden data, unknown correlations and customer propensity. Big data is gathered from a variety of sources such as videos, social networks, images and sensors. This technology is widely used to process massive amounts of data in 6G systems.

Large Intelligent Surface (LIS)

In the case of the THz band signal, the linearity is strong, so there may be many shaded areas due to obstructions. By installing LIS near these shaded areas, it becomes important to expand the communication area, strengthen communication stability and provide additional additional services. The LIS is an artificial surface made of electromagnetic materials, and can change the propagation of incoming and outgoing radio waves. LIS can be seen as an extension of massive MIMO, but the array structure and operation mechanism are different from those of massive MIMO. In addition, LIS has low power consumption in that it operates as a reconfigurable reflector with passive elements, that is, only passively reflects the signal without using an active RF chain. There are advantages to having Also, since each of the passive reflectors of the LIS must independently adjust the phase shift of the incoming signal, it can be advantageous for a wireless communication channel. By properly adjusting the phase shift via the LIS controller, the reflected signal can be gathered at the target receiver to boost the received signal power.

테라헤르츠(THz) 무선통신 일반Terahertz (THz) wireless communication general

THz wireless communication uses wireless communication using a THz wave having a frequency of approximately 0.1 to 10THz (1THz=1012Hz), and can mean terahertz (THz) band wireless communication using a very high carrier frequency of 100GHz or more. . THz wave is located between RF (Radio Frequency)/millimeter (mm) and infrared band, (i) It transmits non-metal/non-polar material better than visible light/infrared light, and has a shorter wavelength than RF/millimeter wave, so it has high straightness. Beam focusing may be possible. In addition, since the photon energy of the THz wave is only a few meV, it is harmless to the human body. The frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band with low propagation loss due to absorption of molecules in the air. The standardization discussion on THz wireless communication is being discussed centered on the IEEE 802.15 THz working group in addition to 3GPP, and the standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) may specify or supplement the content described in this specification. have. THz wireless communication may be applied to wireless recognition, sensing, imaging, wireless communication, THz navigation, and the like. 11 is a diagram showing an example of THz communication application.

As shown in FIG. 11 , a THz wireless communication scenario may be classified into a macro network, a micro network, and a nanoscale network. In the macro network, THz wireless communication can be applied to vehicle-to-vehicle connection and backhaul/fronthaul connection. THz wireless communication in micro networks is applied to indoor small cells, fixed point-to-point or multi-point connections such as wireless connections in data centers, and near-field communication such as kiosk downloading. can be

Table 2 below is a table showing an example of a technique that can be used in the THz wave.

THz wireless communication can be classified based on a method for generating and receiving THz. The THz generation method can be classified into an optical device or an electronic device-based technology. 12 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver. A method of generating THz using an electronic device is a method using a semiconductor device such as a Resonant Tunneling Diode (RTD), a local oscillator and a multiplier. There are a method using a compound semiconductor HEMT (High Electron Mobility Transistor)-based integrated circuit MMIC (Monolithic Microwave Integrated Circuits) method, and a method using a Si-CMOS-based integrated circuit. In the case of FIG. D2 , a doubler, tripler, or multiplier is applied to increase the frequency, and is radiated by the antenna through the subharmonic mixer. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, matches the desired harmonic frequency, and filters out all other frequencies. Also, beamforming may be implemented by applying an array antenna or the like to the antenna of FIG. 12 . In FIG. 12 , IF denotes an intermediate frequency, tripler, multipler denote a multiplier, PA Power Amplifier denotes, LNA denotes a low noise amplifier, and PLL denotes a phase lock circuit (Phase). -Locked Loop).

13 is a diagram illustrating an example of a method of generating an optical device-based THz signal, and FIG. 14 is a diagram illustrating an example of an optical device-based THz wireless communication transceiver.

Optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device. The optical element-based THz signal generation technology is a technology that generates a high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. In this technology, it is easier to increase the frequency compared to the technology using only electronic devices, it is possible to generate a high-power signal, and it is possible to obtain a flat response characteristic in a wide frequency band. In order to generate a THz signal based on an optical device, as shown in FIG. D3, a laser diode, a broadband optical modulator, and a high-speed photodetector are required. In the case of FIG. 13 , a THz signal corresponding to a difference in wavelength between the lasers is generated by multiplexing the light signals of two lasers having different wavelengths. In FIG. D3 , an optical coupler refers to a semiconductor device that uses light waves to transmit electrical signals to provide coupling with electrical isolation between circuits or systems, and UTC-PD (Uni-Traveling Carrier Photo-) Detector) is one of the photodetectors, which uses electrons as active carriers and reduces the movement time of electrons by bandgap grading. UTC-PD is capable of photodetection above 150GHz. In FIG. D4, EDFA (Erbium-Doped Fiber Amplifier) represents an erbium-doped optical fiber amplifier, PD (Photo Detector) represents a semiconductor device capable of converting an optical signal into an electrical signal, and OSA represents various optical communication functions (photoelectric It represents an optical module (Optical Sub Aassembly) in which conversion, electro-optical conversion, etc.) are modularized into one component, and DSO represents a digital storage oscilloscope.

The structure of the photoelectric converter (or photoelectric converter) will be described with reference to FIGS. 15 and 16 . 15 illustrates a structure of a photoinc source-based transmitter, and FIG. 16 illustrates a structure of an optical modulator.

In general, a phase of a signal may be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing electrical characteristics through a microwave contact or the like. Accordingly, an optical modulator output is formed as a modulated waveform. The photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal (nonlinear crystal), photoelectric conversion (O / E conversion) by a photoconductive antenna (photoconductive antenna), a bunch of electrons in the light beam (bunch of) THz pulses can be generated by, for example, emission from relativistic electrons. A terahertz pulse (THz pulse) generated in the above manner may have a length in units of femtoseconds to picoseconds. An O/E converter performs down conversion by using non-linearity of a device.

Considering the THz spectrum usage, a number of contiguous GHz bands for fixed or mobile service use for the terahertz system are used. likely to use According to the outdoor scenario standard, available bandwidth may be classified based on oxygen attenuation of 10^2 dB/km in a spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of several band chunks may be considered. As an example of the framework, if the length of a terahertz pulse (THz pulse) for one carrier is set to 50 ps, the bandwidth (BW) becomes about 20 GHz.

Effective down conversion from the IR band to the THz band depends on how the nonlinearity of the O/E converter is utilized. That is, in order to down-convert to a desired terahertz band (THz band), the O/E converter having the most ideal non-linearity for transfer to the terahertz band (THz band) is design is required. If an O/E converter that does not fit the target frequency band is used, there is a high possibility that an error may occur with respect to the amplitude and phase of the corresponding pulse.

In a single carrier system, a terahertz transmission/reception system may be implemented using one photoelectric converter. Although it depends on the channel environment, as many photoelectric converters as the number of carriers may be required in a far-carrier system. In particular, in the case of a multi-carrier system using several broadbands according to the above-described spectrum usage-related scheme, the phenomenon will become conspicuous. In this regard, a frame structure for the multi-carrier system may be considered. The down-frequency-converted signal based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame). The frequency domain of the specific resource region may include a plurality of chunks. Each chunk may be composed of at least one component carrier (CC).

In 6G, it is expected that the Terahertz band, which is easy to secure wideband frequency resources, will be used for large data transmission. In the case of the terahertz band, the path loss is large compared to the low frequency band, and the propagation characteristic has a large straightness. In order to overcome such a large path loss in the terahertz band, it is necessary to use narrow-width beamforming using ultra massive MIMO (multiple input and multiple output). In addition, due to the above characteristics, the delay spread of the radio channel is short in a line-of-sight (LOS) dominant environment.

On the other hand, a phase locked loop (PLL) is used to transmit and receive a low frequency baseband signal to a high frequency carrier wave (Carrier frequency). In the case of a reference oscillator used as a reference frequency of the PLL, phase noise exists due to the properties of the device. For a high frequency carrier (f _PLL) generated through the PLL it is determined from the phase noise (Phase Noise _Ref) of the reference oscillator (f _Ref) as shown in equation (1).

That is, when a high frequency such as terahertz is used, a phase noise difference and a phase noise become large as shown in FIG. 17 . 17 shows a phase noise difference between a PLL frequency and a reference frequency.

18A shows the magnitude of phase noise for each center frequency. In (a) of Figure 18, the lower solid line is

, the solid line above

represents the case where 18(b) shows a change in phase noise according to time for each center frequency.

In addition, when the phase noise increases, as shown in FIG. 18 , not only the influence of inter-carrier interference (ICI) but also the white phase noise (floor level) component increases in the frequency domain. . Even if the subcarrier (or subcarrier) spacing is sufficiently increased, ICI due to the floor level occurs. Also, the amount of phase change of the signal according to time may increase.

In addition, since the degree of phase change becomes severe within one OFDM symbol period, it is difficult to overcome phase noise through common phase error (CPE) compensation.

The proposed methods for removing phase noise are largely divided into a frequency domain-based compensation technique and a time domain-based compensation technique.

1) CPE compensation method using frequency domain reference signal

This technique is a technique used for estimating phase noise in a multi-carrier waveform like CP (Cyclic-Prefix)-OFDM.

Phase noise is represented by the CPE that causes a common phase rotation of all subcarriers in the OFDM signal. CPE affects all subcarriers equally, but shows low correlation between symbols. To this end, as shown in FIG. 19 (NR frame structure in which PTRS is inserted), a phase tracking reference signal (PTRS) is inserted with low density (sparse) on the frequency axis and high density (dense) on the time axis. used

For example, in order to estimate and compensate for phase noise, in-slot channel estimation is performed using a demodulation reference signal (DMRS) in the frequency domain. In the estimated channel, the effect of the phase noise is also reflected in the effect of the actual channel. And/or, channel estimation is performed in each OFDM symbol using PTRS in the frequency domain. And/or, a phase change amount due to phase noise between a symbol in which DMRS exists and a symbol in which PTRS exists is derived through comparison between a channel derived from DMRS and a channel derived from PTRS. And/or, the amount of phase change estimated through PTRS in the frequency domain is compensated for all subcarriers (or subcarriers) in the corresponding symbol.

As in the corresponding method, if PTRS is used, it is possible to estimate and compensate for one CPE value within a symbol interval. However, in the case of the terahertz band, the method has limitations because the phase noise changes rapidly within a symbol period.

2) Phase noise estimation and compensation technique using time domain reference signal

As a method for estimating and compensating for phase noise based on the time domain, single carrier-frequency domain equalization (SC-FDE), 802.11ad single carrier frame, NR discrete Fourier transform spread OFDM waveform (discrete Fourier transform) spread OFDM waveform and DFT-S-OFDM waveform). SC-FDE is a technique used to estimate phase noise in a single carrier waveform. The 802.11ad single carrier frame is a technique for estimating phase noise using a unique word (UW) used for a cyclic prefix (CP) that is periodically transmitted in the time domain. The NR DFT-S-OFDM waveform is a technique for estimating phase noise by inserting a PTRS within an OFDM symbol interval in the time domain.

After estimating the phase noise using the reference signal in the time domain by the above method, the phase noise estimated from the reference signal may be subjected to linear interpolation (LI) in the time domain to compensate for the phase noise. 20 shows the LI-CPE method demonstrated in a free-running oscillator with 100 Hz spectral width in 6 OFDM symbols.

In this method, since there is no time-domain reference signal in a multi-carrier waveform like CP-OFDM, it may be difficult to estimate the phase noise of the time-domain technique. In addition, as in UW-OFDM, when the UW exists in the CP, it is possible to estimate the phase noise based on the time domain, but it may be difficult to estimate the phase noise that changes within the OFDM symbol.

Accordingly, the present specification provides a method for generating a plurality of discontinuous UW sections for estimation and compensation of phase noise varying within an OFDM symbol section for UW-OFDM and/or a symbol structure consisting of discontinuous UW sections according to a channel environment and/or channel environment. We propose a method for determining and using

First, a method of generating a UW-OFDM symbol and a method of generating a UW-OFDM waveform will be described before describing a method of generating a plurality of discontinuous UW sections.

How to generate UW OFDM symbol

A Unique Word (UW) may be used as a deterministic sequence replacing the CP period of the existing CP-OFDM. In other words, UW may mean a specific sequence. In addition, UW can be used for phase noise and channel estimation in the time domain, synchronization acquisition, and the like.

21 is a diagram for explaining a method of generating a UW-OFDM symbol.

Referring to FIG. 21 , a UW-OFDM symbol may be generated in two steps in the time domain. First, as in Equation 2, a specific section of an OFDM symbol may have a form having a zero word.

here,

represents the OFDM symbol in the time domain,

represents the OFDM symbol in the frequency domain,

represents the data symbol, also,

is an N-point IDFT operation or operation expressed as a matrix.

next,

can be added to the specific section in the time domain as in Equation 3; As such, the UW-OFDM symbol has a structure composed of UW in a specific section and data in other sections.

here,

represents the OFDM symbol in the time domain,

represents UW.

22 shows various transmission data structures.

22 (a) shows the CP-OFDM structure, (b) shows the KSP-OFDM structure, (c) shows the UW-OFDM structure. Referring to FIG. 22, the UW-OFDM structure generated as described above is different from the CP-OFDM structure in that it is a deterministic sequence. In other words, the CP of the CP-OFDM structure may be a dynamic sequence. In addition, the UW-OFDM structure is similar to the KSP (known symbol padded)-OFDM structure in that it is a deterministic sequence, but is different in that the KSP-OFDM structure is not a DFT interval part.

How to generate UW-OFDM waveform

23 shows a UW-OFDM transmission structure.

Referring to FIG. 23 , data may be converted from serial to parallel in step S2310. And/or, in step S2320, redundancy may be added to the parallel data. In other words, some subcarriers may be used for redundancy transmission for generation of zero UWs. And/or, in step S2330 , zero subcarriers may be added to the parallel data. In other words, like the existing CP-OFDM, some subcarriers (or subcarriers) are not used for DC or guard band purposes.

And/or, in step S2340 , a frequency domain signal composed of data and a redundant signal may be converted into a time domain signal. Due to step S2320, some time domains after IFFT may have zero words. Steps S2320 to S2340 may be the operation of Equation (2).

And/or, in step S2350 , UW in the time domain may be added to the zero word domain. This may be the arithmetic operation of Equation (3). And/or, in step S2360, parallel data to which UW is added may be converted into serial data.

In this way, a UW-OFDM waveform can be generated.

Hereinafter, step S2320 will be described in more detail.

For zero word generation, subcarriers are not used only for data transmission, and some of them may be used for redundancy transmission purposes.

The systematic coding scheme is a scheme in which a specific subcarrier (or subcarrier) is dedicated and used for redundancy transmission. The non-systematic coding method is a method of transmitting redundancy by mixing it with a data subcarrier (or subcarrier).

redundancy

is the output data of step S2320 of FIG. 23 and may be expressed as Equation (4).

In Equations 4 to 10,

represents the OFDM symbol in the frequency domain,

represents the OFDM symbol in the time domain,

represents the data symbol,

represents data symbols in the frequency domain,

denotes redundant carriers in the frequency domain,

represents a zero padding matrix,

represents a spreading matrix for non-systematic coding,

represents a permutation matrix,

represents a redundant generation matrix that generates a redundancy based on data,

denotes a code generation matrix.

For the operation of step S2320

can be derived as follows.

First, an OFDM symbol in the frequency domain may be expressed as Equation (5).

An OFDM symbol in the time domain can be expressed as Equation (6).

Equation 6 for all

A condition to be satisfied with is as shown in Equation (7).

Equation 7 can be expressed as Equation 9 by Equation 8 below.

T may be derived as in Equation 10 by the second row of Equation 9.

here,

If is an identity matrix, it is a systematic coding scheme, otherwise it may be a non-systematic coding scheme.

In this case, if the zero word can be arranged at several points within the OFDM symbol interval, the phase noise that changes within the symbol interval can be estimated.

Hereinafter, a method of generating several discontinuous UW sections will be described.

How to create multiple discrete UW intervals

An OFDM symbol (time domain) having a plurality of discontinuous UW sections may be expressed as in Equation 11. Also, an OFDM symbol having a plurality of discontinuous UW sections expressed by Equation 11 may be represented as shown in FIG. 24 .

In Equations 11 to 17,

represents the OFDM symbol in the time domain,

represents data symbols in the frequency domain,

denotes redundant subcarriers in the frequency domain,

represents a zero padding matrix,

denotes a spreading matrix for non-systematic coding,

represents a permutation matrix,

denotes a redundant generation matrix that generates a redundancy based on data,

represents the i-th payload segment, D represents the number of payload segments,

denotes the j-th zero UW segment, and S denotes the number of UW segments.

A redundancy for generating a plurality of discontinuous UW sections may be generated as follows.

In Equation 11,

A relational expression for always generating a zero UW can be expressed as Equation (12) regardless of .

which satisfies Equation 12

In order to derive , Equation 12 may be divided into a necessary part and an unnecessary part, and may be substituted with the formula of a sub-matrix as shown in Equation 15. here,

is for generating zero UW

represents the part that does not affect the

is for generating zero UW

indicates the part that affects it. The matrix in Equation 12 has a total

There can be two equations and unknowns.

here,

,

to be.

Sub-matrix required for derivation

They can be segmented and concatenated.

In other words, Equation 12 can be expressed as Equation 14 by substituting Equation 13 on the left side and changing the rows of the matrix to distinguish necessary and unnecessary portions. sub-matrix

Is

Since there is no effect on derivation, matrix re-construction is possible.

Equation 14 can be expressed as simplified as Equation 15.

here,

,

to be.

In this case, the second row of Equation 15 may be expressed as one equation as in Equation 16.

Equation 16

It can be expressed as Equation 17 when expressed as an expression related to .

like this

When the redundancy is generated by , several discontinuous UW sections may be generated in the OFDM symbol.

UW-OFDM symbol structure using multiple discontinuous UW sections

The UW-OFDM symbol structure may have the following three symbol structures according to the channel environment.

Symbol Structure 1 - (ISI dominant case)

25 shows symbol structure 1. Symbol structure 1 may be used in a channel environment that is greatly affected by inter-symbol interference (ISI) due to the large influence of multiple delay spread. Symbol structure 1 transmits a UW signal in one period within an OFDM symbol (N _DFT). The length of the UW section may be set to a value similar to (or the same value) as the delay spread distribution (L). Here, the similar value may mean a value within the range of the + first specific value and the - second specific value based on the L value. The first specific value and the second specific value may be signaled or preset values. And/or, the length of the UW section may be a length in microseconds. For example, if L=3 and the length of the UW section is set to the same value as L, the length of the UW section may be 3 microseconds. L may mean time or the length of a time domain sample. For example, L may be a value in which the length of a delay spread is expressed by the number of time domain samples.

Symbol structure 1 may maintain a cyclic convolution characteristic within a reception DFT window.

Symbol Structure 2 - (PN dominant case)

26 shows symbol structure 2; Symbol structure 2 may be used in a channel environment having a small influence of ISI and a large influence of phase noise (PN).

In symbol structure 2, L is sufficiently small, and even if UW larger than L is transmitted in several sections, overhead due to UW transmission is not large. Symbol structure 2 may transmit a UW signal in several (S) sections within an _{OFDM symbol (N DFT ).} The length of one UW section may be set to a value greater than L. For example, when L is 3, the length of one UW section may be set to 4 microseconds. Symbol structure 2 may maintain a cyclic convolution characteristic within a receive DFT window. And/or, symbol structure 2 may be used to estimate the degree of inter-UW phase rotation in the time domain.

Referring to FIG. 27 , a channel estimator estimates a channel and a channel impulse response (CIR) based on a time domain or frequency domain pilot signal. A PN estimator performs channel compensation on a UW section based on a channel estimated through a pilot. The PN estimator may be able to estimate without CIR information according to a PN estimation method. The PN estimator may estimate the amount of phase change for each UW section using a UW sequence. The PN estimator may estimate the amount of phase change between UW sections through interpolation. The PN compensator compensates the previously derived amount of phase change for each time sample with respect to a payload in the time domain.

Symbol Structure 3 - (simultaneous ISI and PN case)

28 shows symbol structure 3; Symbol structure 3 can be used in a channel environment that is affected by both ISI and PN. Symbol structure 3 transmits a UW signal in several (S) sections within an OFDM symbol (N _DFT).

may be used to prevent ISI within the receive DFT window interval.

(The length of the UW section) may be set to a value similar to (or the same as L).

can be used to estimate the degree of phase rotation between UWs in the time domain.

(length of UW section) may be set to a value smaller than L.

For example, if L is 3,

is 3 microseconds,

can be set to 2 microseconds.

may maintain the cyclic convolution characteristic within the receive DFT window.

can be used to estimate the degree of phase rotation between UWs in the time domain. In addition, after the UW surrounding data sample compensates for the interference caused by the channel environment (FFT==>FDE(Frequency domain equalization)==>IFFT), phase noise estimation in the time domain through UW (phase noise estimation) may have to be performed. That is, reception complexity may increase.

When the terminal is located at a low SNR, it can be estimated that the data is modulated with a low modulation order, and in this case, since the phase noise is not a dominant situation, Pre-FDE-based phase noise estimation may be omitted. When the terminal is located at a high SNR, even if the reception complexity is increased, since the phase noise is a large section, the pre-FDE-based phase noise estimation and compensation process can be used for data demodulation.

29 shows the phase noise estimation and compensation structure of symbol structure 3; Referring to FIG. 29 , a channel estimator may estimate a channel based on a time domain or frequency domain pilot signal. A channel compensator may perform channel compensation in a frequency domain based on a channel estimated through a pilot. The PN estimator may estimate the amount of phase change for each UW section using a UW sequence. Also, the PN estimator may estimate the amount of phase change between UW sections through interpolation. The PN compensator may compensate the derived amount of phase change for each time sample with respect to the payload in the time domain.

For example, a specific symbol structure among UW-OFDM symbol structures 1 to 3 may be selected based on a channel environment, and data (or information) may be transmitted/received based on the specific symbol structure.

For example, the UE may measure the channel state based on Channel State Information (CSI)-Reference Signal (RS). And/or, the terminal may report CSI to the base station. And/or, the terminal receives control information (or information indicating a symbol structure) on the number of a plurality of definitive sequence sections and/or the length of each definitive sequence section from the base station, and according to the information, the discontinuous plurality Control information and/or uplink data may be transmitted/received based on an OFDM symbol (eg, symbol structure 2) including deterministic sequence sections of .

As another example, the UE may measure the channel state based on the RS. And/or, the terminal may transmit CSI to the base station. And/or, the terminal is based on an OFDM symbol (eg, symbol structure 2) of a specific number of a plurality of deterministic sequence sections and/or a length of each specific deterministic sequence section according to a promise and/or setting for a measured channel state. Information and/or data may be transmitted and received. And/or, the base station is an OFDM symbol (eg, symbol structure 2) of the number of a plurality of specific deterministic sequence sections and/or the length of each specific deterministic sequence section according to a promise and/or configuration for a channel state measured based on CSI. ) based on the information and/or data may be transmitted and received.

And/or, the terminal and/or the base station may transmit/receive data (or information) according to one of the symbol structures 1 to 3 according to the channel environment (or channel state) by AI.

And/or, the terminal and/or the base station may perform machine learning to perform the above operation. For example, the terminal and/or the base station may learn from information indicating the channel environment (or state) in which a specific symbol structure is labeled. The leveled information is input to the neural network, and an error can be calculated by comparing the output of the neural network with the label of the training data. The calculated error is backward propagated in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation. And/or, the terminal and/or the base station may perform the above operation through unsupervised learning and/or reinforcement learning.

The terminal and/or the base station may reliably determine a symbol structure through learning based on the deep neural network structure of FIG. 5 and transmit/receive data or information. And/or, the terminal and/or the base station may perform machine learning based on the convolutional neural network structure of FIGS. 6 to 7 and/or the recurrent neural network structure of FIG. 8 .

Referring to FIG. 30 , first, the terminal (1000/2000 in FIGS. 32 to 41 ) may receive uplink scheduling control information from the base station ( S3001 ). For example, the control information may be downlink control information (DCI).

For example, the operation of the terminal receiving the control information in step S3001 may be implemented by the apparatus of FIGS. 32 to 41 to be described below. For example, referring to FIG. 33 , one or more processors 1020 may control one or more memories 1040 and/or one or more RF units 1060 , etc. to receive control information, and one or more RF units. 1060 may receive control information.

And/or, the terminal (1000/2000 in FIGS. 32 to 41) may transmit uplink data to the base station based on the control information (S3002).

For example, the operation of the terminal transmitting uplink data in step S3002 may be implemented by the apparatus of FIGS. 32 to 41 to be described below. For example, referring to FIG. 33 , one or more processors 1020 may control one or more memories 1040 and/or one or more RF units 1060 and the like to transmit uplink data, and one or more RF units. The unit 1060 may transmit uplink data.

In particular, the control information and/or uplink data is based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence intervals, the plurality of deterministic sequence intervals are discontinuously arranged within the OFDM symbol interval, the plurality of deterministic sequence intervals At least one of the number of sections and/or the length of each deterministic sequence section may be defined based on a channel state or a characteristic of data. For example, a plurality of discontinuous deterministic sequence sections may be generated by the operations of steps S2310 to S2360 of FIG. 23 . And/or, the OFDM symbol may include only one deterministic sequence period based on the channel state. And/or, the microsecond unit length of the deterministic sequence period may have the same value as the length of the delay spread. For example, when the length of the delay spread is 3, the length of the deterministic sequence period may be 3 microseconds. For example, the position of the deterministic sequence interval may be arranged at the beginning or the end of an OFDM symbol, or may be arranged at a specific position according to a channel state and a preset appointment. For example, the OFDM symbol structure may be as shown in FIG. 25 . For example, in this case, the channel state may be a state having an inter-symbol interference value higher than a preset inter-symbol interference (ISI) value.

For example, the UE may measure the channel state based on Channel State Information (CSI)-Reference Signal (RS). And/or, the terminal may report CSI to the base station. And/or, the terminal receives control information on the number of a plurality of deterministic sequence sections and/or the length of each deterministic sequence section from the base station, and according to the information, an OFDM symbol including a plurality of discontinuous deterministic sequence sections Control information and/or uplink data may be transmitted/received based on

As another example, the UE may measure the channel state based on the RS. And/or, the terminal may transmit CSI to the base station. And/or, the terminal transmits/receives information and/or data based on the OFDM symbol of a specific number of a plurality of deterministic sequence sections and/or the length of each specific deterministic sequence section according to a promise and/or setting for the measured channel state. can do. And/or, the base station is information and/or based on the OFDM symbol of the number and/or length of each specific deterministic sequence section according to the promise and/or configuration of the channel state measured based on the CSI. Alternatively, data may be transmitted/received. For example, a length in microseconds of each of a plurality of deterministic sequence sections may have a larger value than a length of a delay spread based on a channel state. For example, when the length L of the delay spread is 3, the length of each of the plurality of deterministic sequence intervals may be 4 microseconds. And/or, the lengths of each of the plurality of definitive sequence sections may be different values according to a preset appointment. For example, in this case, the channel state may be a state in which the phase noise value of the channel is higher than the preset phase noise value. And/or, the number of the plurality of definitive sequence sections may be determined to be a specific number according to the length of the definitive sequence sections, or may have a specific number according to a preset appointment based on a channel state.

As another example, the length in microseconds of the first or last deterministic sequence section in the OFDM symbol has the same value as the length of delay spread based on the channel state, and the microsecond length of other deterministic sequence sections is It may have a value smaller than the length of the delay spread based on the channel state. For example, when the length of the delay spread is 3, the length of the first or last deterministic sequence section may be 3 microseconds, and the length of other deterministic sequence sections may be 1 microsecond. For example, in this case, the channel state may be a state in which the phase noise value of the channel is higher than the preset phase noise value and the intersymbol interference value of the channel is higher than the preset intersymbol interference value. And/or, the number of the plurality of definitive sequence sections may be determined to be a specific number according to the length of the definitive sequence sections, or may have a specific number according to a preset appointment based on a channel state.

As another example, a length in microseconds of each of the deterministic sequence intervals has a value equal to, a small value, or a large value as a length of a delay spread based on a channel condition, and/or a plurality of deterministic sequence intervals The number may be determined to be a specific number according to the length of the definitive sequence sections, or may have a specific number according to a preset appointment based on a channel state. And/or, the lengths of the deterministic sequence sections may have different values based on the channel state.

And/or, at least one of the number of deterministic sequence sections and/or the length of each deterministic sequence section may be defined according to data characteristics.

For example, high modulation order control in which the transmitted and received control information or data has a large phase noise value of a channel or a large required Signal to Interference plus Noise Ratio (SINR) value. In the case of information or data, the number of deterministic sequence sections in an OFDM symbol may be defined as a value greater than a preset value. Here, the data characteristic may mean a modulation order of control information or data. For example, a high modulation order may mean a modulation order higher than a preset modulation order.

And/or, at least one of the number of the plurality of deterministic sequence sections and/or the microsecond length of the deterministic sequence section may be determined based on machine learning. For example, the terminal and/or the base station may learn from information indicating the channel environment (or state) in which at least one of the number of a plurality of definitive sequence sections and/or the microsecond length of the definitive sequence section is labeled. The leveled information is input to the neural network, and an error can be calculated by comparing the output of the neural network with the label of the training data. The calculated error is backward propagated in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation.

Based on such machine learning, the terminal and/or the base station determines at least one of the number of a plurality of deterministic sequence sections and/or the microsecond length of the deterministic sequence section based on machine learning, and based on the data (or information ) can be sent and received. And/or, the terminal and/or the base station may perform the above operation through unsupervised learning and/or reinforcement learning.

And/or, the definitive sequence section may be a section to which a unique word (UW) is mapped. And/or, the definitive sequence section may be a section in which a specific sequence is matched.

And/or, the control information may be received through a Physical Downlink Control Channel (PDCCH).

And/or, uplink data may be transmitted through a physical uplink shared channel (PUSCH).

Although the above examples have been described focusing on the operation of the terminal, it goes without saying that the base station can also transmit and receive information or data based on the OFDM symbol including a plurality of discontinuous deterministic sequence sections as described above.

The above examples are mainly described with an OFDM symbol including a plurality of discontinuous deterministic sequence sections in the uplink data transmission/reception procedure between the UE and the base station, but are not limited thereto, and the downlink data transmission/reception procedure, random access (Random Access) ), of course, can be applied to all communication procedures such as procedures.

For example, an OFDM symbol including a plurality of discontinuous deterministic sequence sections may be applied to transmission/reception of a higher layer signal, a physical channel signal (eg, PRACH, etc.), a synchronization signal, and an uplink/downlink reference signal.

Since the operation of the terminal described with reference to FIG. 30 is the same as that of the terminal described with reference to FIGS. 1 to 29 , a detailed description other than that is omitted.

The above-described signaling and operation may be implemented by an apparatus (eg, FIGS. 32 to 41 ) to be described below. For example, the above-described signaling and operation may be processed by one or more processors 1010 and 2020 of FIGS. 32 to 41 , and the above-described signaling and operation may be performed by at least one processor (eg: 1010, 2020) may be stored in the memory (eg, 1040, 2040) in the form of an instruction/program (eg, instruction, executable code) for driving.

For example, an apparatus comprising one or more memories and one or more processors operatively coupled to the one or more memories, wherein the one or more processors enable the apparatus to receive control information for scheduling an uplink from a base station; configured to transmit uplink data to the base station based on the control information, wherein the control information and the uplink data are based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence intervals, and the plurality of deterministic sequence intervals is an OFDM symbol interval It is discontinuously arranged in , and at least one of the number of a plurality of deterministic sequence sections and/or the length of each deterministic sequence section may be defined based on a channel state or a characteristic of data.

As another example, in a non-transitory computer readable medium (CRM) storing one or more instructions, the one or more instructions executable by one or more processors allows the terminal to transmit control information for scheduling uplink from the base station. receive and transmit uplink data to the base station according to the control information, wherein the control information and the uplink data are based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence intervals, and the plurality of deterministic sequence intervals are OFDM Discontinuously arranged within a symbol interval, at least one of the number of a plurality of deterministic sequence intervals and/or a length of each deterministic sequence interval may be defined based on a channel state or a characteristic of data.

Referring to FIG. 31 , first, the base station (1000/2000 in FIGS. 32 to 41 ) may transmit downlink scheduling control information to the terminal ( S3101 ). For example, the control information may be downlink control information (DCI).

For example, the operation of the base station transmitting control information in step S3101 may be implemented by the apparatus of FIGS. 32 to 41 to be described below. For example, referring to FIG. 33 , one or more processors 1020 may control one or more memories 1040 and/or one or more RF units 1060 , etc. to transmit control information, and one or more RF units. 1060 may transmit control information.

And/or, the base station (1000/2000 in FIGS. 32 to 41) may transmit downlink data to the terminal based on the control information (S3102).

For example, the operation of the base station transmitting downlink data in step S3102 may be implemented by the apparatus of FIGS. 32 to 41 to be described below. For example, referring to FIG. 33 , one or more processors 1020 may control one or more memories 1040 and/or one or more RF units 1060 and the like to transmit downlink data, and one or more RF units. The unit 1060 may transmit downlink data.

In particular, the control information and/or downlink data is based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence intervals, the plurality of deterministic sequence intervals are discontinuously arranged within the OFDM symbol interval, the plurality of deterministic sequence intervals At least one of the number of sections and/or the length of each deterministic sequence section may be defined based on a channel state or a characteristic of data. For example, a plurality of discontinuous deterministic sequence sections may be generated by the operations of steps S2310 to S2360 of FIG. 23 .

And/or, the OFDM symbol may include only one deterministic sequence period based on the channel state. And/or, the microsecond unit length of the deterministic sequence period may have the same value as the length of the delay spread. For example, when the length of the delay spread is 3, the length of the deterministic sequence period may be 3 microseconds. For example, the position of the deterministic sequence interval may be arranged at the beginning or the end of an OFDM symbol, or may be arranged at a specific position according to a channel state and a preset appointment. For example, the OFDM symbol structure may be as shown in FIG. 25 . For example, in this case, the channel state may be a state having an inter-symbol interference value higher than a preset inter-symbol interference (ISI) value.

As another example, the UE may measure the channel state based on the RS. And/or, the terminal may transmit CSI to the base station. And/or, the terminal transmits/receives information and/or data based on the OFDM symbol of a specific number of a plurality of deterministic sequence sections and/or the length of each specific deterministic sequence section according to a promise and/or setting for the measured channel state. can do. And/or, the base station is information and/or based on the OFDM symbol of the number and/or length of each specific deterministic sequence section according to the promise and/or configuration of the channel state measured based on the CSI. Alternatively, data may be transmitted/received.

For example, a length in microseconds of each of a plurality of deterministic sequence sections may have a value greater than a length of a delay spread based on a channel state. For example, when the length L of the delay spread is 3, the length of each of the plurality of deterministic sequence intervals may be 4 microseconds. And/or, the lengths of each of the plurality of definitive sequence sections may be different values according to a preset appointment. For example, in this case, the channel state may be a state in which the phase noise value of the channel is higher than the preset phase noise value. And/or, the number of the plurality of definitive sequence sections may be determined to be a specific number according to the length of the definitive sequence sections, or may have a specific number according to a preset appointment based on a channel state.

And/or, the control information may be transmitted through a Physical Downlink Control Channel (PDCCH).

And/or, downlink data may be transmitted through a physical downlink shared channel (PUSCH).

Although the above examples have been described focusing on the operation of the base station, it goes without saying that the terminal can also transmit and receive information or data based on the OFDM symbol including a plurality of discontinuous deterministic sequence sections as described above.

Since the operation of the base station described with reference to FIG. 31 is the same as the operation of the base station described with reference to FIGS. 1 to 30 , a detailed description other than that is omitted.

For example, in an apparatus including one or more memories and one or more processors operatively connected to the one or more memories, the one or more processors transmit, by the apparatus, control information for scheduling a downlink to a terminal; , configured to transmit downlink data to the terminal based on the control information, wherein the control information and the downlink data are based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence sections, and the plurality of deterministic sequence sections includes an OFDM symbol Discontinuously disposed within the interval, at least one of the number of a plurality of deterministic sequence intervals and/or the length of each deterministic sequence interval may be defined based on a channel state or a characteristic of data.

As another example, in a non-transitory computer readable medium (CRM) for storing one or more instructions, the one or more instructions executable by one or more processors transmits, by a base station, control information for scheduling a downlink to a terminal. transmit, and transmit downlink data to the terminal based on the control information, wherein the control information and the downlink data are based on an OFDM symbol, the OFDM symbol includes a plurality of deterministic sequence sections, and the plurality of deterministic sequence sections are OFDM Discontinuously arranged within a symbol interval, at least one of the number of a plurality of deterministic sequence intervals and/or a length of each deterministic sequence interval may be defined based on a channel state or a characteristic of data.

본 발명이 적용되는 통신 시스템 예Communication system example to which the present invention is applied

Although not limited thereto, the various descriptions, functions, procedures, suggestions, methods, and/or operation flowcharts of the present invention disclosed in this document may be applied to various fields requiring wireless communication/connection (eg, 5G) between devices. have.

Hereinafter, it will be exemplified in more detail with reference to the drawings. In the following drawings/descriptions, the same reference numerals may represent the same or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise indicated.

32 illustrates a communication system 10 applied to the present invention.

Referring to FIG. 32 , the communication system 10 applied to the present invention includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)), and may be referred to as a communication/wireless/5G device. Although not limited thereto, the wireless device may include a robot 1000a, a vehicle 1000b-1, 1000b-2, an eXtended Reality (XR) device 1000c, a hand-held device 1000d, and a home appliance 1000e. ), an Internet of Things (IoT) device 1000f, and an AI device/server 4000 . For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous driving vehicle, a vehicle capable of performing inter-vehicle communication, and the like. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (eg, a drone). XR devices include AR (Augmented Reality)/VR (Virtual Reality)/MR (Mixed Reality) devices, and include a Head-Mounted Device (HMD), a Head-Up Display (HUD) provided in a vehicle, a television, a smartphone, It may be implemented in the form of a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like. The portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), a computer (eg, a laptop computer), and the like. Home appliances may include a TV, a refrigerator, a washing machine, and the like. The IoT device may include a sensor, a smart meter, and the like. For example, the base station and the network may be implemented as a wireless device, and a specific wireless device 2000a may operate as a base station/network node to other wireless devices.

The wireless devices 1000a to 1000f may be connected to the network 3000 through the base station 2000 . Artificial intelligence (AI) technology may be applied to the wireless devices 1000a to 1000f , and the wireless devices 1000a to 1000f may be connected to the AI server 4000 through the network 300 . The network 3000 may be configured using a 3G network, a 4G (eg, LTE) network, or a 5G (eg, NR) network. The wireless devices 1000a to 1000f may communicate with each other through the base station 2000/network 3000, or may directly communicate (e.g. sidelink communication) without using the base station/network. For example, the vehicles 1000b-1 and 1000b-2 may perform direct communication (e.g. Vehicle to Vehicle (V2V)/Vehicle to everything (V2X) communication). In addition, the IoT device (eg, a sensor) may directly communicate with another IoT device (eg, a sensor) or other wireless devices (1000a to 1000f).

Wireless communication/

connection

1500a, 1500b, and 1500c may be performed between the wireless devices 1000a to 1000f/base station 2000 and the base station 2000/base station 2000 . Here, the wireless communication/connection includes uplink/downlink communication (1500a), sidelink communication (1500b) (or D2D communication), and communication between base stations (1500c) (eg relay, IAB (Integrated Access Backhaul)), such as various wireless connections. This can be done through technology (eg 5G NR) Wireless communication/connection (1500a, 1500b, 1500c) allows a wireless device and a base station/radio device, and a base station and a base station to transmit/receive wireless signals to each other. For example, the wireless communication/

connection

1500a, 1500b, and 1500c may transmit/receive signals through various physical channels.To this end, based on various proposals of the present invention, At least some of various configuration information setting processes, various signal processing processes (eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.), resource allocation processes, etc. may be performed.

본 발명이 적용되는 무선 기기 예Examples of wireless devices to which the present invention is applied

33 illustrates a wireless device applicable to the present invention.

Referring to FIG. 33 , the first wireless device 1000 and the second wireless device 2000 may transmit/receive wireless signals through various wireless access technologies (eg, LTE, NR). Here, {first wireless device 1000, second wireless device 2000} is {wireless device 1000x, base station 2000} of FIG. 32 and/or {wireless device 1000x, wireless device 1000x) } can be matched.

The first wireless device 1000 may include one or more processors 1020 and one or more memories 1040 , and may further include one or more transceivers 1060 and/or one or more antennas 1080 in addition. The processor 1020 controls the memory 1040 and/or the transceiver 1060 and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. For example, the processor 1020 may process information in the memory 1040 to generate first information/signal, and then transmit a wireless signal including the first information/signal through the transceiver 1060 . In addition, the processor 1020 may receive the wireless signal including the second information/signal through the transceiver 1060 , and then store information obtained from signal processing of the second information/signal in the memory 1040 . The memory 1040 may be connected to the processor 1020 and may store various information related to the operation of the processor 1020 . For example, memory 1040 provides instructions for performing some or all of the processes controlled by processor 1020 , or for performing descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including Here, the processor 1020 and the memory 1040 may be a part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 1060 may be coupled to the processor 1020 , and may transmit and/or receive wireless signals via one or more antennas 1080 . The transceiver 1060 may include a transmitter and/or a receiver. The transceiver 1060 may be used interchangeably with a radio frequency (RF) unit. In the present invention, a wireless device may refer to a communication modem/circuit/chip.

The second wireless device 2000 may include one or more processors 2020 , one or more memories 2040 , and may further include one or more transceivers 2060 and/or one or more antennas 2080 . The processor 2020 controls the memory 2040 and/or the transceiver 2060 and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. For example, the processor 2020 may process information in the memory 2040 to generate third information/signal, and then transmit a wireless signal including the third information/signal through the transceiver 2060 . In addition, the processor 2020 may receive the radio signal including the fourth information/signal through the transceiver 2060 , and then store information obtained from signal processing of the fourth information/signal in the memory 2040 . The memory 2040 may be connected to the processor 2020 and may store various information related to the operation of the processor 2020 . For example, memory 2040 provides instructions for performing some or all of the processes controlled by processor 2020, or for performing descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including Here, the processor 2020 and the memory 2040 may be a part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 2060 may be coupled to the processor 2020 , and may transmit and/or receive wireless signals through one or more antennas 2080 . Transceiver 2060 may include a transmitter and/or receiver. Transceiver 2060 may be used interchangeably with an RF unit. In the present invention, a wireless device may refer to a communication modem/circuit/chip.

Hereinafter, hardware elements of the

wireless devices

1000 and 2000 will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or

more processors

1020 , 2020 . For example, one or

more processors

1020 , 2020 may implement one or more layers (eg, functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). The one or

more processors

1020 and 2020 are configured to process one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods and/or operation flowcharts disclosed herein. can create The one or

more processors

1020 , 2020 may generate messages, control information, data, or information according to the description, function, procedure, proposal, method, and/or operational flowcharts disclosed herein. The one or

more processors

1020 and 2020 generate a signal (eg, a baseband signal) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and/or methods disclosed in this document. , may be provided to one or

more transceivers

1060 and 2060 . The one or

more processors

1020 , 2020 may receive signals (eg, baseband signals) from one or

more transceivers

1060 , 2060 , and the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed herein. PDUs, SDUs, messages, control information, data, or information may be acquired according to the fields.

The one or

more processors

1020 and 2020 may be referred to as a controller, microcontroller, microprocessor, or microcomputer. The one or

more processors

1020 and 2020 may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or

more processors

1020 and 2020. The descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like. The descriptions, functions, procedures, suggestions, methods, and/or flow charts disclosed in this document provide that firmware or software configured to perform is included in one or

more processors

1020 and 2020 or stored in one or

more memories

1040 and 2040. It may be driven by the

above processors

1020 and 2020. The descriptions, functions, procedures, proposals, methods, and/or flowcharts of operations disclosed herein may be implemented using firmware or software in the form of code, instructions, and/or a set of instructions.

One or

more memories

1040 and 2040 may be coupled to one or

more processors

1020 and 2020 and may store various types of data, signals, messages, information, programs, codes, instructions, and/or instructions. One or

more memories

1040 and 2040 may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer readable storage media, and/or combinations thereof. One or

more memories

1040 , 2040 may be located inside and/or external to one or

more processors

1020 , 2020 . In addition, one or

more memories

1040 and 2040 may be connected to one or

more processors

1020 and 2020 through various technologies such as wired or wireless connection.

The one or

more transceivers

1060 and 2060 may transmit user data, control information, radio signals/channels, etc. mentioned in the methods and/or operation flowcharts of this document to one or more other devices. One or

more transceivers

1060, 2060 may receive, from one or more other devices, user data, control information, radio signals/channels, etc. referred to in the descriptions, functions, procedures, suggestions, methods, and/or flow charts, etc. disclosed herein. have. For example, one or

more transceivers

1060 and 2060 may be connected to one or

more processors

1020 and 2020, and may transmit and receive wireless signals. For example, one or

more processors

1020 , 2020 may control one or

more transceivers

1060 , 2060 to transmit user data, control information, or wireless signals to one or more other devices. In addition, one or

more processors

1020 and 2020 may control one or

more transceivers

1060 and 2060 to receive user data, control information, or radio signals from one or more other devices. Also, one or

more transceivers

1060, 2060 may be coupled with one or

more antennas

1080, 2080, and one or

more transceivers

1060, 2060 may be connected via one or

more antennas

1080, 2080 to the descriptions, functions, and functions disclosed herein. , may be set to transmit and receive user data, control information, radio signals/channels, etc. mentioned in procedures, proposals, methods and/or operation flowcharts. In this document, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports). One or more transceivers (1060, 2060) in order to process the received user data, control information, radio signals/channels, etc. using one or more processors (1020, 2020), the received radio signals/channels, etc. from the RF band signal It can be converted into a baseband signal. The one or

more transceivers

1060 and 2060 may convert user data, control information, radio signals/channels, etc. processed using one or

more processors

1020 and 2020 from a baseband signal to an RF band signal. To this end, one or

more transceivers

1060 , 2060 may include (analog) oscillators and/or filters.

본 발명이 적용되는 신호 처리 회로 예Example of a signal processing circuit to which the present invention is applied

34 illustrates a signal processing circuit for a transmission signal.

Referring to FIG. 34 , the signal processing circuit 10000 may include a scrambler 10100, a modulator 10200, a layer mapper 10300, a precoder 10400, a resource mapper 10500, and a signal generator 10600. have. Although not limited thereto, the operations/functions of FIG. 27 may be performed by the

processors

1020 and 2020 and/or the

transceivers

1060 and 2060 of FIG. 33 . The hardware element of FIG. 34 may be implemented in the

processors

1020 and 2020 and/or the

transceivers

1060 and 2060 of FIG. 33 . For example, blocks 10100 to 10600 may be implemented in the

processors

1020 and 2020 of FIG. 33 . In addition, blocks 10100 to 10500 may be implemented in the

processors

1020 and 2020 of FIG. 33 , and block 10600 may be implemented in the

transceivers

1060 and 2060 of FIG. 33 .

The codeword may be converted into a wireless signal through the signal processing circuit 10000 of FIG. 34 . Here, the codeword is a coded bit sequence of an information block. The information block may include a transport block (eg, a UL-SCH transport block, a DL-SCH transport block). The radio signal may be transmitted through various physical channels (eg, PUSCH, PDSCH).

Specifically, the codeword may be converted into a scrambled bit sequence by the scrambler 10100 . A scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device, and the like. The scrambled bit sequence may be modulated into a modulation symbol sequence by the modulator 10200 . The modulation method may include pi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying (m-PSK), m-Quadrature Amplitude Modulation (m-QAM), and the like. The complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 10300 . Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 10400 (precoding). The output z of the precoder 10400 may be obtained by multiplying the output y of the layer mapper 10300 by the precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transport layers. Here, the precoder 10400 may perform precoding after performing transform precoding (eg, DFT transform) on the complex modulation symbols. Also, the precoder 10400 may perform precoding without performing transform precoding.

The resource mapper 10500 may map modulation symbols of each antenna port to a time-frequency resource. The time-frequency resource may include a plurality of symbols (eg, a CP-OFDMA symbol, a DFT-s-OFDMA symbol) in the time domain and a plurality of subcarriers in the frequency domain. The signal generator 10600 generates a radio signal from the mapped modulation symbols, and the generated radio signal may be transmitted to another device through each antenna. To this end, the signal generator 10600 may include an Inverse Fast Fourier Transform (IFFT) module and a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, and the like. .

The signal processing process for the received signal in the wireless device may be configured in reverse of the signal processing process 10100 to 10600 of FIG. 34 . For example, the wireless device (eg, 1000 and 2000 in FIG. 33 ) may receive a wireless signal from the outside through an antenna port/transceiver. The received radio signal may be converted into a baseband signal through a signal restorer. To this end, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a Fast Fourier Transform (FFT) module. Thereafter, the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a descrambling process. The codeword may be restored to the original information block through decoding. Accordingly, the signal processing circuit (not shown) for the received signal may include a signal restorer, a resource de-mapper, a post coder, a demodulator, a descrambler, and a decoder.

본 발명이 적용되는 무선 기기 활용 예Examples of application of wireless devices to which the present invention is applied

The wireless device may be implemented in various forms according to use-examples/services.

Referring to FIG. 35 ,

wireless devices

1000 and 2000 correspond to

wireless devices

1000 and 2000 of FIG. 33 , and include various elements, components, units/units, and/or modules. (module) can be composed. For example, the

wireless devices

1000 and 2000 may include a communication unit 1100 , a control unit 1200 , a memory unit 1300 , and an additional element 1400 . The communication unit may include a communication circuit 1120 and transceiver(s) 1140 . For example, the communication circuit 1120 may include one or

more processors

1020 and 2020 and/or one or

more memories

1040 and 2040 of FIG. 33 . For example, transceiver(s) 1140 may include one or

more transceivers

1060 , 2060 and/or one or

more antennas

1080 , 2080 of FIG. 33 . The control unit 1200 is electrically connected to the communication unit 1100 , the memory unit 1300 , and the additional element 1400 , and controls general operations of the wireless device. For example, the controller 1200 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 1300 . In addition, the control unit 1200 transmits the information stored in the memory unit 1300 to the outside (eg, another communication device) through the communication unit 1100 through a wireless/wired interface, or externally through the communication unit 1100 (eg, Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 1300 .

The additional element 1400 may be variously configured according to the type of wireless device. For example, the additional element 1400 may include at least one of a power unit/battery, an input/output unit (I/O unit), a driving unit, and a computing unit. Although not limited thereto, wireless devices include robots (FIGS. 32 and 1000a), vehicles (FIGS. 32, 1000b-1, and 1000b-2), XR devices (FIGS. 32 and 1000c), portable devices (FIGS. 32 and 1000d), and home appliances. (FIG. 32, 1000e), IoT device (FIG. 32, 1000f), digital broadcasting terminal, hologram device, public safety device, MTC device, medical device, fintech device (or financial device), security device, climate/environment device, It may be implemented in the form of an AI server/device ( FIGS. 32 and 4000 ), a base station ( FIGS. 32 and 2000 ), and a network node. The wireless device may be mobile or used in a fixed location depending on the use-example/service.

In FIG. 35 , various elements, components, units/units, and/or modules in the

wireless devices

1000 and 2000 may be all interconnected through a wired interface, or at least some of them may be wirelessly connected through the communication unit 1100 . For example, in the

wireless devices

1000 and 2000 , the control unit 1200 and the communication unit 1100 are connected by wire, and the control unit 1200 and the first unit (eg, 1300 , 1400 ) are connected to the communication unit 1100 through the communication unit 1100 . It can be connected wirelessly. In addition, each element, component, unit/unit, and/or module within the

wireless device

1000 , 2000 may further include one or more elements. For example, the controller 1200 may be configured with one or more processor sets. For example, the controller 1200 may be configured as a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like. As another example, the memory unit 1300 may include random access memory (RAM), dynamic RAM (DRAM), read only memory (ROM), flash memory, volatile memory, and non-volatile memory. volatile memory) and/or a combination thereof.

36 illustrates a portable device to which the present invention is applied.

The portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), and a portable computer (eg, a laptop computer). A mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

Referring to FIG. 36 , the portable device 1000 includes an antenna unit 1080, a communication unit 1100, a control unit 1200, a memory unit 1300, a power supply unit 1400a, an interface unit 1400b, and an input/output unit 1400c. ) may be included. The antenna unit 1080 may be configured as a part of the communication unit 1100 . Blocks 1100 to 1300/1400a to 1400c correspond to blocks 1100 to 1300/1400 of FIG. 28, respectively.

The communication unit 1100 may transmit and receive signals (eg, data, control signals, etc.) with other wireless devices and base stations. The controller 1200 may control components of the portable device 1000 to perform various operations. The controller 1200 may include an application processor (AP). The memory unit 1300 may store data/parameters/programs/codes/commands necessary for driving the portable device 1000 . Also, the memory unit 1300 may store input/output data/information. The power supply unit 1400a supplies power to the portable device 1000 and may include a wired/wireless charging circuit, a battery, and the like. The interface unit 1400b may support the connection between the portable device 1000 and other external devices. The interface unit 1400b may include various ports (eg, an audio input/output port and a video input/output port) for connection with an external device. The input/output unit 1400c may receive or output image information/signal, audio information/signal, data, and/or information input from a user. The input/output unit 1400c may include a camera, a microphone, a user input unit, a display unit 1400d, a speaker, and/or a haptic module.

For example, in the case of data communication, the input/output unit 1400c obtains information/signals (eg, touch, text, voice, image, video) input from the user, and the obtained information/signals are stored in the memory unit 1300 . can be saved. The communication unit 1100 may convert the information/signal stored in the memory into a wireless signal, and transmit the converted wireless signal directly to another wireless device or to a base station. Also, after receiving a radio signal from another radio device or a base station, the communication unit 1100 may restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 1300 , it may be output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 1400c.

37 illustrates a vehicle or an autonomous driving vehicle to which the present invention is applied. The vehicle or autonomous driving vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, and the like.

Referring to FIG. 37 , the vehicle or autonomous driving vehicle 1000 includes an antenna unit 1080, a communication unit 1100, a control unit 1200, a driving unit 1400a, a power supply unit 1400b, a sensor unit 1400c, and autonomous driving. A unit 1400d may be included. The antenna unit 1080 may be configured as a part of the communication unit 1100 . Blocks 1100/1300/1400a to 1400d correspond to blocks 1100/1300/1400 of FIG. 35, respectively.

The communication unit 1100 may transmit/receive signals (eg, data, control signals, etc.) to/from external devices such as other vehicles, base stations (eg, base stations, roadside units, etc.), servers, and the like. The controller 1200 may perform various operations by controlling elements of the vehicle or the autonomous driving vehicle 1000 . The controller 1200 may include an Electronic Control Unit (ECU). The driving unit 1400a may cause the vehicle or the autonomous driving vehicle 1000 to run on the ground. The driving unit 1400a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like. The power supply unit 1400b supplies power to the vehicle or the autonomous driving vehicle 1000 and may include a wired/wireless charging circuit, a battery, and the like. The sensor unit 1400c may obtain vehicle status, surrounding environment information, user information, and the like. The sensor unit 1400c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, and a vehicle forward movement. / may include a reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, and the like. The autonomous driving unit 1400d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set. technology can be implemented.

For example, the communication unit 1100 may receive map data, traffic information data, and the like from an external server. The autonomous driving unit 1400d may generate an autonomous driving route and a driving plan based on the acquired data. The controller 1200 may control the driving unit 1400a to move the vehicle or the autonomous driving vehicle 1000 along the autonomous driving path (eg, speed/direction adjustment) according to the driving plan. During autonomous driving, the communication unit 1100 may non/periodically acquire the latest traffic information data from an external server and acquire surrounding traffic information data from nearby vehicles. Also, during autonomous driving, the sensor unit 1400c may acquire vehicle state and surrounding environment information. The autonomous driving unit 1400d may update the autonomous driving route and driving plan based on the newly acquired data/information. The communication unit 1100 may transmit information about a vehicle location, an autonomous driving route, a driving plan, and the like to an external server. The external server may predict traffic information data in advance using AI technology or the like based on information collected from the vehicle or autonomous driving vehicles, and may provide the predicted traffic information data to the vehicle or autonomous driving vehicles.

38 illustrates a vehicle to which the present invention is applied. The vehicle may also be implemented as a means of transportation, a train, an air vehicle, a ship, and the like.

Referring to FIG. 38 , the vehicle 1000 may include a communication unit 1100 , a control unit 1200 , a memory unit 1300 , an input/output unit 1400a , and a position measurement unit 1400b . Here, blocks 1100 to 1300/1400a to 1400b correspond to blocks 1100 to 1300/1400 of FIG. 35, respectively.

The communication unit 1100 may transmit/receive signals (eg, data, control signals, etc.) with other vehicles or external devices such as a base station. The controller 1200 may control components of the vehicle 1000 to perform various operations. The memory unit 1300 may store data/parameters/programs/codes/commands supporting various functions of the vehicle 1000 . The input/output unit 1400a may output an AR/VR object based on information in the memory unit 1300 . The input/output unit 1400a may include a HUD. The position measuring unit 1400b may acquire position information of the vehicle 1000 . The location information may include absolute location information of the vehicle 1000 , location information within a driving line, acceleration information, and location information with a neighboring vehicle. The location measuring unit 1400b may include a GPS and various sensors.

For example, the communication unit 1100 of the vehicle 1000 may receive map information, traffic information, and the like from an external server and store it in the memory unit 1300 . The position measuring unit 1400b may obtain vehicle position information through GPS and various sensors and store it in the memory unit 130 . The controller 120 may generate a virtual object based on map information, traffic information, and vehicle location information, and the input/output unit 1400a may display the generated virtual object on a window inside the vehicle ( 14100 and 14200 ). In addition, the controller 1200 may determine whether the vehicle 1000 is normally operating within the driving line based on the vehicle location information. When the vehicle 1000 abnormally deviates from the driving line, the controller 1200 may display a warning on the vehicle window through the input/output unit 1400a. Also, the control unit 1200 may broadcast a warning message regarding driving abnormality to surrounding vehicles through the communication unit 1100 . Depending on the situation, the controller 1200 may transmit the location information of the vehicle and information on driving/vehicle abnormalities to the related organizations through the communication unit 1100 .

39 illustrates an XR device applied to the present invention. The XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smart phone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.

Referring to FIG. 39 , the XR device 1000a may include a communication unit 1100, a control unit 1200, a memory unit 1300, an input/output unit 1400a, a sensor unit 1400b, and a power supply unit 1400c. . Here, blocks 1100 to 1300/1400a to 1400c correspond to blocks 1100 to 1300/1400 of FIG. 35, respectively.

The communication unit 1100 may transmit/receive signals (eg, media data, control signals, etc.) to/from external devices such as other wireless devices, portable devices, or media servers. Media data may include images, images, and sounds. The controller 1200 may control components of the XR device 1000a to perform various operations. For example, the controller 1200 may be configured to control and/or perform procedures such as video/image acquisition, (video/image) encoding, and metadata generation and processing. The memory unit 1300 may store data/parameters/programs/codes/commands necessary for driving the XR device 1000a/creating an XR object. The input/output unit 1400a may obtain control information, data, etc. from the outside, and may output the generated XR object. The input/output unit 1400a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 1400b may obtain an XR device state, surrounding environment information, user information, and the like. The sensor unit 1400b may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. have. The power supply unit 1400c supplies power to the XR device 100a, and may include a wired/wireless charging circuit, a battery, and the like.

As an example, the memory unit 1300 of the XR device 1000a may include information (eg, data, etc.) necessary for generating an XR object (eg, AR/VR/MR object). The input/output unit 1400a may obtain a command to operate the XR device 1000a from the user, and the controller 1200 may drive the XR device 1000a according to the user's driving command. For example, when the user wants to watch a movie or news through the XR device 1000a, the controller 1200 transmits the content request information to another device (eg, the mobile device 1000b) through the communication unit 1300 or can be sent to the media server. The communication unit 130 may download/stream contents such as movies and news from another device (eg, the portable device 1000b) or a media server to the memory unit 1300 . The controller 120 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, and metadata generation/processing for the content, and is acquired through the input/output unit 1400a/sensor unit 1400b It is possible to generate/output an XR object based on information about one surrounding space or a real object.

Also, the XR device 1000a is wirelessly connected to the portable device 1000b through the communication unit 1100 , and the operation of the XR device 1000a may be controlled by the portable device 1000b. For example, the portable device 1000b may operate as a controller for the XR device 1000a. To this end, the XR device 1000a may obtain 3D location information of the portable device 1000b, and then generate and output an XR object corresponding to the portable device 1000b.

40 illustrates a robot applied to the present invention. Robots can be classified into industrial, medical, home, military, etc. depending on the purpose or field of use.

Referring to FIG. 40 , the robot 1000 may include a communication unit 1100 , a control unit 1200 , a memory unit 1300 , an input/output unit 1400a , a sensor unit 1400b , and a driving unit 1400c . Here, blocks 1100 to 1300/1400a to 1400c correspond to blocks 1100 to 1300/1400 of FIG. 35, respectively.

The communication unit 1100 may transmit/receive signals (eg, driving information, control signals, etc.) to and from external devices such as other wireless devices, other robots, or control servers. The controller 1200 may control components of the robot 1000 to perform various operations. The memory unit 1300 may store data/parameters/programs/codes/commands supporting various functions of the robot 1000 . The input/output unit 1400a may obtain information from the outside of the robot 100 and may output information to the outside of the robot 1000 . The input/output unit 1400a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 1400b may obtain internal information, surrounding environment information, user information, and the like of the robot 1000 . The sensor unit 1400b may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a radar, and the like. The driving unit 1400c may perform various physical operations such as moving a robot joint. In addition, the driving unit 1400c may make the robot 1000 travel on the ground or fly in the air. The driving unit 1400c may include an actuator, a motor, a wheel, a brake, a propeller, and the like.

41 illustrates an AI device applied to the present invention. AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented in any possible device or the like.

Referring to FIG. 41 , the AI device 1000 includes a communication unit 1100 , a control unit 1200 , a memory unit 1300 , an input/output unit 1400a/1400b , a learning processor unit 1400c and a sensor unit 1400d. may include Blocks 1100 to 1300/1400a to 1400d correspond to blocks 1100 to 1300/1400 of FIG. 35, respectively.

The communication unit 1100 uses wired/wireless communication technology to communicate with other AI devices (eg, FIGS. 32, 1000x, 2000, 4000) or external devices such as the AI server 2000 and wired/wireless signals (eg, sensor information, user input, learning). models, control signals, etc.). To this end, the communication unit 1100 may transmit information in the memory unit 1300 to an external device or transmit a signal received from the external device to the memory unit 1300 .

The controller 1200 may determine at least one executable operation of the AI device 1000 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the controller 1200 may control the components of the AI device 1000 to perform the determined operation. For example, the control unit 1200 may request, search, receive, or utilize the data of the learning processor unit 1400c or the memory unit 1300, and may be predicted or preferred among at least one executable operation. Components of the AI device 1000 may be controlled to execute the operation. In addition, the control unit 1200 collects history information including user feedback on the operation contents or operation of the AI device 1000 and stores it in the memory unit 1300 or the learning processor unit 1400c, or the AI server ( 32 and 4000) may be transmitted to an external device. The collected historical information may be used to update the learning model.

The memory unit 1300 may store data supporting various functions of the AI device 1000 . For example, the memory unit 1300 may store data obtained from the input unit 1400a , data obtained from the communication unit 1100 , output data of the learning processor unit 1400c , and data obtained from the sensing unit 1400 . Also, the memory unit 1300 may store control information and/or software codes necessary for the operation/execution of the control unit 1200 .

The input unit 1400a may acquire various types of data from the outside of the AI device 1000 . For example, the input unit 1200 may obtain training data for model learning, input data to which the learning model is applied, and the like. The input unit 1400a may include a camera, a microphone, and/or a user input unit. The output unit 1400b may generate an output related to sight, hearing, or touch. The output unit 1400b may include a display unit, a speaker, and/or a haptic module. The sensing unit 1400 may obtain at least one of internal information of the AI device 1000 , surrounding environment information of the AI device 1000 , and user information by using various sensors. The sensing unit 1400 may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. have.

The learning processor unit 1400c may train a model composed of an artificial neural network by using the training data. The learning processor unit 1400c may perform AI processing together with the learning processor unit of the AI server ( FIGS. 32 and 4000 ). The learning processor unit 1400c may process information received from an external device through the communication unit 1100 and/or information stored in the memory unit 1300 . Also, the output value of the learning processor unit 1400c may be transmitted to an external device through the communication unit 1100 and/or stored in the memory unit 1300 .

The embodiments described above are those in which elements and features of the present invention are combined in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, it is also possible to configure an embodiment of the present invention by combining some elements and/or features. The order of operations described in the embodiments of the present invention may be changed. Some features or features of one embodiment may be included in another embodiment, or may be replaced with corresponding features or features of another embodiment. It is obvious that claims that are not explicitly cited in the claims can be combined to form an embodiment or included as a new claim by amendment after filing.

Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, an embodiment of the present invention provides one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), FPGAs ( field programmable gate arrays), a processor, a controller, a microcontroller, a microprocessor, and the like.

In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of modules, procedures, functions, etc. that perform the functions or operations described above. The software code may be stored in the memory and driven by the processor. The memory may be located inside or outside the processor, and may transmit/receive data to and from the processor by various known means.

It is apparent to those skilled in the art that the present invention may be embodied in other specific forms without departing from the essential characteristics of the present invention. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included in the scope of the present invention.

Although the method of transmitting uplink data in the wireless communication system of the present specification has been mainly described as an example applied to the 3GPP LTE/LTE-A system and the 5G system (New RAT system), it is possible to apply it to various wireless communication systems in addition do.

Claims

A method for transmitting uplink data in a wireless communication system, the method performed by a terminal comprising:

Receiving uplink scheduling control information from a base station; and

Comprising the step of transmitting the uplink data to the base station based on the control information,

The control information and the uplink data are based on OFDM symbols,

The OFDM symbol includes a plurality of deterministic sequence intervals,

The plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval,

wherein at least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section is defined based on a channel state or a characteristic of data.
According to claim 1,

A length in microseconds of each of the plurality of deterministic sequence sections has a value greater than a length of a delay spread based on the channel state.
The method of claim 2, wherein the channel state is a state in which a phase noise value of a channel is higher than a preset phase noise value.
According to claim 1,

The length in microseconds of the first or last deterministic sequence period in the OFDM symbol has the same value as the length of delay spread based on the channel state,

In addition, the microsecond length of the deterministic sequence sections has a value smaller than the length of the delay spread based on the channel state.
5. The method of claim 4, wherein the channel state is a state in which a phase noise value of a channel is higher than a preset phase noise value and an intersymbol interference value of a channel is higher than a preset intersymbol interference value.
According to claim 1,

At least one of the number of the plurality of deterministic sequence intervals and/or the length in microseconds of the deterministic sequence interval is determined based on machine learning.
According to claim 1,

The deterministic sequence section is a section to which a unique word (UW) is mapped.
According to claim 1,

The control information is received through a Physical Downlink Control Channel (PDCCH).
According to claim 1,

The uplink data is transmitted through a physical uplink shared channel (Physical Uplink Shared Channel, PUSCH).
A terminal for transmitting uplink data in a wireless communication system, comprising:

one or more transceivers;

one or more processors operatively coupled to the one or more transceivers;

one or more memories operatively coupled to the one or more processors and storing instructions for performing operations;

The actions are

Receiving uplink scheduling control information from a base station; and

Comprising the step of transmitting the uplink data to the base station based on the control information,

The control information and the uplink data are based on OFDM symbols,

The OFDM symbol includes a plurality of deterministic sequence intervals,

The plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval,

At least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section is defined based on a channel state or a characteristic of data.
A method for transmitting downlink data in a wireless communication system, the method performed by a base station comprising:

transmitting control information for scheduling downlink to a terminal; and

Comprising the step of transmitting the downlink data to the terminal based on the control information,

The control information and the downlink data are based on OFDM symbols,

The OFDM symbol includes a plurality of deterministic sequence intervals,

The plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval,

wherein at least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section is defined based on a channel state or a characteristic of data.
11. The method of claim 10,

A length in microseconds of each of the plurality of deterministic sequence sections has a value greater than a length of a delay spread based on the channel state.
The method of claim 12 , wherein the channel state is a state in which a phase noise value of a channel is higher than a preset phase noise value.
11. The method of claim 10,

The length in microseconds of the first or last deterministic sequence period in the OFDM symbol has the same value as the length of delay spread based on the channel state,

In addition, the microsecond length of the deterministic sequence sections has a value smaller than the length of the delay spread based on the channel state.
15. The method of claim 14, wherein the channel state is a state in which a phase noise value of a channel is higher than a preset phase noise value and an intersymbol interference value of a channel is higher than a preset intersymbol interference value.
11. The method of claim 10,

At least one of the number of the plurality of deterministic sequence intervals and/or the length in microseconds of the deterministic sequence interval is determined based on machine learning.
11. The method of claim 10,

The deterministic sequence section is a section to which a unique word (UW) is mapped.
11. The method of claim 10,

The control information is transmitted through a Physical Downlink Control Channel (PUCCH).
11. The method of claim 10,

The downlink data is transmitted through a physical downlink shared channel (Physical Downlink Shared Channel, PDSCH).
A base station for transmitting downlink data in a wireless communication system, comprising:

one or more transceivers;

one or more processors operatively coupled to the one or more transceivers;

one or more memories operatively coupled to the one or more processors and storing instructions for performing operations;

The actions are

transmitting control information for scheduling downlink to a terminal; and

Comprising the step of transmitting the downlink data to the terminal based on the control information,

The control information and the downlink data are based on OFDM symbols,

The OFDM symbol includes a plurality of deterministic sequence intervals,

The plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval,

At least one of the number of the plurality of deterministic sequence intervals and/or the length of each deterministic sequence interval is defined based on a channel state or a characteristic of data.
An apparatus comprising one or more memories and one or more processors operatively coupled to the one or more memories, the apparatus comprising:

The one or more processors enable the device to

Receive control information for scheduling uplink from a base station,

configured to transmit uplink data to the base station based on the control information;

The control information and the uplink data are based on OFDM symbols,

The OFDM symbol includes a plurality of deterministic sequence intervals,

The plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval,

wherein at least one of the number of the plurality of deterministic sequence sections and/or the length of each deterministic sequence section is defined based on a channel state or a characteristic of data.
A non-transitory computer readable medium (CRM) storing one or more instructions, comprising:

The one or more instructions executable by the one or more processors causes the terminal to

Receive control information for scheduling uplink from a base station,

To transmit uplink data to the base station based on the control information,

The control information and the uplink data are based on OFDM symbols,

The OFDM symbol includes a plurality of deterministic sequence intervals,

The plurality of deterministic sequence intervals are discontinuously arranged within an OFDM symbol interval,

At least one of the number of the plurality of deterministic sequence segments and/or the length of each deterministic sequence segment is defined based on a channel state or a characteristic of data.