WO2023070339A1 - Détection de préambule wi-fi - Google Patents

Détection de préambule wi-fi Download PDF

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Publication number
WO2023070339A1
WO2023070339A1 PCT/CN2021/126526 CN2021126526W WO2023070339A1 WO 2023070339 A1 WO2023070339 A1 WO 2023070339A1 CN 2021126526 W CN2021126526 W CN 2021126526W WO 2023070339 A1 WO2023070339 A1 WO 2023070339A1
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Prior art keywords
samplings
power normalization
preamble
correlation
normalization level
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PCT/CN2021/126526
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English (en)
Inventor
Qiang FENG
Wenyi Xu
Chenhui YE
Tao Tao
Jiaqi QUAN
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Nokia Shanghai Bell Co., Ltd.
Nokia Solutions And Networks Oy
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Priority to PCT/CN2021/126526 priority Critical patent/WO2023070339A1/fr
Priority to CN202180103683.1A priority patent/CN118202765A/zh
Publication of WO2023070339A1 publication Critical patent/WO2023070339A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/0006—Assessment of spectral gaps suitable for allocating digitally modulated signals, e.g. for carrier allocation in cognitive radio
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2602—Signal structure
    • H04L27/26025—Numerology, i.e. varying one or more of symbol duration, subcarrier spacing, Fourier transform size, sampling rate or down-clocking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2666—Acquisition of further OFDM parameters, e.g. bandwidth, subcarrier spacing, or guard interval length
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2668—Details of algorithms
    • H04L27/2669—Details of algorithms characterised by the domain of operation
    • H04L27/2671—Time domain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2668—Details of algorithms
    • H04L27/2673—Details of algorithms characterised by synchronisation parameters
    • H04L27/2675—Pilot or known symbols
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L7/00—Arrangements for synchronising receiver with transmitter
    • H04L7/04—Speed or phase control by synchronisation signals
    • H04L7/041—Speed or phase control by synchronisation signals using special codes as synchronising signal
    • H04L7/042—Detectors therefor, e.g. correlators, state machines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/10—Small scale networks; Flat hierarchical networks
    • H04W84/12—WLAN [Wireless Local Area Networks]

Definitions

  • Embodiments of the present disclosure generally relate to the field of telecommunication and in particular to devices, methods, apparatuses and computer readable storage media of Wi-Fi preamble detection.
  • NR-U NR-unlicensed
  • LTE-U Long Term Evolution-unlicensed
  • NR-U Compared with Wi-Fi, Long Term Evolution-unlicensed (LTE-U) and NR-U are expected to bring benefits in range and link budget, spectral efficiency and capacity, configurable Quality of Service (QoS) , mobility, interoperability, high to low-rate scaling, spectrum options and security.
  • QoS Quality of Service
  • NR-U has significantly different end-use targets compared with 4G networks, which is developed with a focus on commercial and industrial uses, including the Industrial Internet of Things (IIoT) , automation, and machine-to-machine communications.
  • IIoT Industrial Internet of Things
  • example embodiments of the present disclosure provide a solution of Wi-Fi preamble detection.
  • a method comprises obtaining, at a first device, a plurality of samplings associated with a signal transmitted from a second device, respective sample periods of the plurality of samplings being overlapped with each other; determining a power normalization level associated with the plurality of samplings by performing an autocorrelation operation for the plurality of samplings; and detecting a preamble in the plurality of samplings at least based on the power normalization level.
  • a first device comprises at least one processor; and at least one memory including computer program codes; the at least one memory and the computer program codes are configured to, with the at least one processor, cause the first device at least to obtain, at a first device, a plurality of samplings associated with a signal transmitted from a second device, respective sample periods of the plurality of samplings being overlapped with each other; determine a power normalization level associated with the plurality of samplings by performing an autocorrelation operation for the plurality of samplings; and detect a preamble in the plurality of samplings at least based on the power normalization level.
  • an apparatus comprising means for obtaining, at a first device, a plurality of samplings associated with a signal transmitted from a second device, respective sample periods of the plurality of samplings being overlapped with each other; means for determining a power normalization level associated with the plurality of samplings by performing an autocorrelation operation for the plurality of samplings; and means for detecting a preamble in the plurality of samplings at least based on the power normalization level.
  • a computer readable medium having a computer program stored thereon which, when executed by at least one processor of a device, causes the device to carry out the method according to the first aspect.
  • FIG. 1 illustrates an example environment in which example embodiments of the present disclosure can be implemented
  • FIG. 2 shows an example of Wi-Fi training structure according to some example embodiments of the present disclosure
  • FIG. 3 shows a block diagram of an example system for the Wi-Fi preamble detection according to some example embodiments of the present disclosure
  • FIG. 4 shows an example of data sampling according to some example embodiments of the present disclosure
  • FIG. 5 shows an example of power normalization according to some example embodiments of the present disclosure
  • FIG. 6 shows a block diagram of an example module for pattern recognition according to some example embodiments of the present disclosure
  • FIG. 7A -7D show example simulation results of Wi-Fi preamble detection according to some example embodiments of the present disclosure
  • FIG. 8 shows a flowchart of an example method of Wi-Fi preamble detection according to some example embodiments of the present disclosure
  • FIG. 9 shows a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure.
  • FIG. 10 shows a block diagram of an example computer readable medium in accordance with some embodiments of the present disclosure.
  • references in the present disclosure to “one embodiment, ” “an embodiment, ” “an example embodiment, ” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an example embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
  • circuitry may refer to one or more or all of the following:
  • circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware.
  • circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.
  • the term “communication network” refers to a network following any suitable communication standards, such as fifth generation (5G) systems, Long Term Evolution (LTE) , LTE-Advanced (LTE-A) , Wideband Code Division Multiple Access (WCDMA) , High-Speed Packet Access (HSPA) , Narrow Band Internet of Things (NB-IoT) and so on.
  • 5G fifth generation
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • WCDMA Wideband Code Division Multiple Access
  • HSPA High-Speed Packet Access
  • NB-IoT Narrow Band Internet of Things
  • the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the future fifth generation (5G) new radio (NR) communication protocols, and/or any other protocols either currently known or to be developed in the future.
  • suitable generation communication protocols including, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the future fifth generation (5G) new radio (NR) communication protocols, and/or any other protocols either currently known or to be developed in the future.
  • Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the
  • the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives services therefrom.
  • the network device may refer to a base station (BS) or an access point (AP) , for example, a node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a NR Next Generation NodeB (gNB) , a Remote Radio Unit (RRU) , a radio header (RH) , a remote radio head (RRH) , a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology.
  • BS base station
  • AP access point
  • NodeB or NB node B
  • eNodeB or eNB evolved NodeB
  • gNB Next Generation NodeB
  • RRU Remote Radio Unit
  • RH radio header
  • RRH remote radio head
  • relay a
  • a RAN split architecture comprises a gNB-CU (Centralized unit, hosting RRC, SDAP and PDCP) controlling a plurality of gNB-DUs (Distributed unit, hosting RLC, MAC and PHY) .
  • a relay node may correspond to DU part of the IAB node.
  • terminal device refers to any end device that may be capable of wireless communication.
  • a terminal device may also be referred to as a communication device, user equipment (UE) , a subscriber station (SS) , a portable subscriber station, a mobile station (MS) , or an access terminal (AT) .
  • UE user equipment
  • SS subscriber station
  • MS mobile station
  • AT access terminal
  • the terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA) , portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , USB dongles, smart devices, wireless customer-premises equipment (CPE) , an Internet of Things (IoT) device, a watch or other wearable, a head-mounted display (HMD) , a vehicle, a drone, a medical device and applications (e.g., remote surgery) , an industrial device and applications (e.g., a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts) , a consumer electronics device, a device operating on commercial and/
  • the terminal device may also correspond to Mobile Termination (MT) part of the integrated access and backhaul (IAB) node (a.k.a. a relay node) .
  • MT Mobile Termination
  • IAB integrated access and backhaul
  • the terms “terminal device” , “communication device” , “terminal” , “user equipment” and “UE” may be used interchangeably.
  • a user equipment apparatus such as a cell phone or tablet computer or laptop computer or desktop computer or mobile IoT device or fixed IoT device
  • This user equipment apparatus can, for example, be furnished with corresponding capabilities as described in connection with the fixed and/or the wireless network node (s) , as appropriate.
  • the user equipment apparatus may be the user equipment and/or or a control device, such as a chipset or processor, configured to control the user equipment when installed therein. Examples of such functionalities include the bootstrapping server function and/or the home subscriber server, which may be implemented in the user equipment apparatus by providing the user equipment apparatus with software configured to cause the user equipment apparatus to perform from the point of view of these functions/nodes.
  • FIG. 1 shows an example communication network 100 in which embodiments of the present disclosure can be implemented.
  • the communication network 100 may comprise a Wi-Fi device 120 (hereinafter may also be referred to as a second device) and a network device 110 (hereinafter may also be referred to as a first device 110. )
  • the communication network 100 may also comprise a terminal device 130.
  • the network device 110 may communicate with the terminal device 130.
  • the Wi-Fi device may also communicate with the terminal device 130.
  • the communication network 100 may include any suitable number of devices. It is to be understood that the communication network 100 may also comprise any suitable type of devices.
  • NR-U is developed with a focus on commercial and industrial uses, including the IIoT, automation, and machine-to-machine communications.
  • LTE-U and NR-U are expected to bring benefits in range and link budget, spectral efficiency and capacity, configurable QoS, mobility, interoperability, high to low rate scaling, spectrum options and, security.
  • LTE-U LTE Licensed-Assisted Access
  • LAA LAA Licensed-Assisted Access
  • NR-U NR-U
  • LTE-U, LTE Licensed-Assisted Access (LAA) and NR-U are located in the unlicensed 5GHz frequency band, where ubiquitous Wi-Fi devices are already there. Since Wi-Fi devices are widespread in such bands, coexistence fairness with Wi-Fi and other exiting technologies in 5GHz unlicensed bands are considered when developing LAA and NR-U specification and system.
  • Wi-Fi is using random back-off and channel sensing as mechanism to coexist with other networks.
  • LAA and NR-U used similar mechanism, i.e., listen before talk (LBT) , when operating in unlicensed band.
  • LBT listen before talk
  • a device or a base station wants to perform transmission, it has to firstly detect the received energy level, i.e., Clear Channel Assessment (CCA) in a certain period.
  • CCA Clear Channel Assessment
  • 3GPP defines the meaning of fair coexistence between LTE and Wi-Fi in the 5 GHz as “the capability of an LAA network not to impact Wi-Fi networks active on a carrier more than an additional Wi-Fi network operating on the same carrier, in terms of throughput and latency” .
  • 3GPP delivered many simulation results to prove it could be a good friend to Wi-Fi, The Wi-Fi companies may always challenge the coexistence performance in a real deployment.
  • Wi-Fi preamble In the NR-U study item phase, some Wi-Fi companies request NR-U system to detect Wi-Fi preamble. 3GPP companies refuse to do it due to huge implementation overhead.
  • the main challenge of detecting Wi-Fi preamble by cellular system is, for example, the Wi-Fi sample rates and OFDM sub-carrier spacings are not compatible with 3GPP.
  • another challenge is the Wi-Fi system is un-synchronized with the cellular system.
  • the frequency error between Wi-Fi and base station is another issue.
  • the frequency error for Wi-Fi at 5.8G is 20ppm, nearly ⁇ 120KHz.
  • the cellular system can detect Wi-Fi presence, for example, based on the Wi-Fi preamble, with a low complexity and high accuracy.
  • the solution of the present disclosure proposes a mechanism of Wi-Fi preamble detection.
  • the network device may obtain a plurality of samplings associated with a signal transmitted from the Wi-Fi device. Respective sample periods of the plurality of samplings may be overlapped with each other.
  • the network device may determine a power normalization level associated with the plurality of samplings by performing an autocorrelation operation for the plurality of samplings.
  • the network device may detect a preamble in the plurality of samplings at least based on the power normalization level. In this way, the Wi-Fi preamble can be detected by the cellular system with a low complexity and high accuracy.
  • FIG. 2 shows an example of Wi-Fi training structure according to some example embodiments of the present disclosure.
  • FIG. 2 shows an example of Wi-Fi training structure according to some example embodiments of the present disclosure.
  • a Wi-Fi frame 200 may comprise a L-STF 210 and a L-LTF 220.
  • the L-STF may consists of 10 repeats of a known sequence (such as t2 211) with a total 8 ⁇ s duration.
  • Wi-Fi may use the ‘preamble’ signals (L-STF 210 and L-LTF 220) for signal detection and frequency offset estimation.
  • a Wi-Fi device does not transmit if a Wi-Fi preamble is received at an energy level above a specified threshold in a 20 MHz channel.
  • FIG. 3 shows a block diagram of an example system 300 for the Wi-Fi preamble detection according to some example embodiments of the present disclosure.
  • the system 300 can be implemented at the network device 110.
  • the process may involve the network device 110 and the Wi-Fi device 120.
  • the network device 110 may capture a plurality of samplings associated with the signal.
  • the respective sample periods of the captured plurality of samplings are overlapped with each other.
  • the respective sample periods of the captured plurality of samplings each may be longer than the length of two L-STF frames.
  • FIG. 4 shows an example of data sampling according to some example embodiments of the present disclosure.
  • 3 captured signal samplings are shown in FIG. 4, namely samplings 401-403, each of the respective sample periods 411, 412 and 413 of the signal samplings 401-403 T is longer than the length of two L-STF frames.
  • the overlapping sampling may guarantee at least a whole cycle of autocorrelation patter is sampled.
  • the feature extraction and pattern recognition are executed to judge whether there is a L-STF frame.
  • the network device 110 may perform an autocorrelation operation to complete a feature generation, by which the peak pattern with autocorrelation for non-integer cycle signal can be generated.
  • the autocorrelation with fixed offset may be applied as below:
  • L may be represented as the correlation window length
  • D may be represented as the fixed offset
  • r may be represented as the sampling
  • n may be represent as an autocorrelation corresponding to n th sampling
  • k ⁇ 0, L-1 ⁇ .
  • the fixed offset D may be represented as:
  • fs bs may be represented the sample rate of the network device 110
  • fs wifi may be represented the sample rate of the Wi-Fi device 120
  • Circle may be represented the each known sequence length in L-STF frame and round () means the operation round to the nearest integer.
  • the correlation length L may be represented as:
  • fs bs may be represented the sample rate of the network device 110
  • fs wifi may be represented the sample rate of the Wi-Fi device 120
  • Circle may be represented the each known sequence length in L-STF frame
  • N may be represented the repeated numbers of the known the sequence in L-STF and the value of N may be 10 and round () means the operation round to the nearest integer
  • the total power in the correlation window length may be calculate as below:
  • an autocorrelation result can be normalized by:
  • the final result is normalized to [0 ⁇ 1] .
  • the network device 110 may determine that the capture samplings are the L-STF frame. That is, the Wi-Fi preamble is detected. If the calculated autocorrelation result m n is less than a threshold, the network device 110 may further identify the shape.
  • the network device 110 may perform pattern recognition.
  • the network device 110 may perform pattern recognition based on a Machine Learning (ML) module and determine whether the Wi-Fi preamble is detected based on the output of the ML module.
  • the calculated autocorrelation result i.e., the power normalization level obtained by the block 320 can be used as input of the ML module and the output of the ML module may be represented as the preamble detection result, i.e., whether the Wi-Fi preamble is present.
  • the ML module may be well trained based on a set of history power normalization levels and may characterize a correlation between a set of history power normalization levels and the corresponding preamble detection results.
  • the ML module may learn the peak pattern with a Neural Network (NN) and the fully connected NN is applied to judge if there is the peak pattern or not.
  • NN Neural Network
  • the NN for pattern recognition can be constructed as the entity of pattern recognition block 330.
  • FIG. 6 shows a block diagram of an example module for pattern recognition according to some example embodiments of the present disclosure.
  • the module 600 for pattern recognition may comprise feature extraction portion 610, which is used for learning the peak pattern, and a classifier 620, which is used for judge if there is the peak pattern or not.
  • the input 601 of the module 600 can be the power normalization level obtained by the block 320.
  • the output 602 of the module 600 can be “1” or “0” , where “1” may represent the Wi-Fi preamble is present while “0” may represent the Wi-Fi preamble is not present.
  • the Wi-Fi preamble can be detected by the cellular system with a low complexity and high accuracy, because the detection speed can be faster than the energy detection algorithms and the conversion of the data sample rate from network device to Wi-Fi device is not required due to the applied autocorrelation and power normalization algorithms, and therefore the frequency error can be defended.
  • FIG. 7A -7D show example simulation results of Wi-Fi preamble detection according to some example embodiments of the present disclosure.
  • the Frequency Error module can be used for simulating the frequency error between Wi-Fi device and network device.
  • the wlanTGnChannel and AWGN module are used to simulate the Wi-Fi TGn SISO channel fading modle with added AWGN noise.
  • the base band sample rate of the network device is 30.72M. So the D in Equation (2) is 25 and L in Equation (3) is 221. Frequency band is 5.8G, the maximal frequency error of Wi-Fi system is less than ⁇ 120 KHz.
  • FIG. 7A shows the output of feature exaction procedure under different SNR values.
  • the peak value is smaller when the SNR is lower.
  • the ML is used to identify the peak pattern under the low SNR cases.
  • FIGs. 7B and 7C show the ratios of the peak are bigger than the threshold, in which FIG. 7B is represented for the case where the threshold is 0.25, while FIG. 7C is represented for the case where the threshold is 0.35.
  • the known issue is how to choose the suitable threshold.
  • the result comparison between the ML based detection algorithm and the threshold based algorithm can be shown in FIG. 7D.
  • For each SNR there are five different frequency offset settings, namely -120 KHz, -60 KHz, 0 Hz, 60 KHz and 120 KHz.
  • For each frequency error case 1000 data sets are tested.
  • Threshold 0.25 (corresponds to curve 712)
  • Threshold 0.35 (corresponds to curve 713)
  • the threshold of the ML based algorithm (corresponds to curve 711) is set to 0.4.
  • FIG. 8 shows a flowchart of an example method 800 of relaxation compensation according to some example embodiments of the present disclosure.
  • the method 800 can be implemented at the first device 110 as shown in FIG. 1.
  • the method 800 will be described with reference to FIG. 1.
  • the first device obtains a plurality of samplings associated with a signal transmitted from a second device, respective sample periods of the plurality of samplings being overlapped with each other.
  • each of the plurality of samplings has a sample period longer than a length of two short training frames.
  • the first device determines a power normalization level associated with the plurality of samplings by performing an autocorrelation operation for the plurality of samplings .
  • the first device may determine an offset associated with the autocorrelation operation based on a first sample rate of the first device, a second sample rate of the second device and a length of a sequence in a short training frame associated with the preamble; determine a correlation window length based on the first sample rate, the second sample rate, the length of the sequence in the short training frame and a repeated numbers of the sequence in the short training frame; and determine the power normalization level based on the offset, the correlation window length and the plurality of samplings.
  • the first device detects a preamble in the plurality of samplings at least based on the power normalization level.
  • the first device may obtain a correlation between a set of history power normalization levels and the corresponding preamble detection results and detect the preamble based on the power normalization level and the correlation.
  • the first device may obtain the correlation by according to a neural network model with the set of history power normalization levels as an input.
  • the first device may determine that the preamble is detected in the plurality of samplings.
  • the first device is a network device
  • the second device is a Wi-Fi device.
  • the receiving device may obtain the correlation by according to a neural network model with the plurality of history constellations as an input.
  • the receiving device may recovery original data from received data based on the second set of estimated channel coefficients, the original data being transmitted from the transmitting device being distorted due to at least of one of an intrinsic feature of the transmitting device, an intrinsic feature of the receiving device, or characteristics of the channel.
  • an apparatus capable of performing the method 800 may comprise means for performing the respective steps of the method 800.
  • the means may be implemented in any suitable form.
  • the means may be implemented in a circuitry or software module.
  • the apparatus comprises means for obtaining, at a first device, a plurality of samplings associated with a signal transmitted from a second device, respective sample periods of the plurality of samplings being overlapped with each other; means for determining a power normalization level associated with the plurality of samplings by performing an autocorrelation operation for the plurality of samplings; and means for detecting a preamble in the plurality of samplings at least based on the power normalization level.
  • FIG. 9 is a simplified block diagram of a device 900 that is suitable for implementing embodiments of the present disclosure.
  • the device 900 may be provided to implement the communication device, for example the first device 110 as shown in FIG. 1.
  • the device 900 includes one or more processors 910, one or more memories 940 coupled to the processor 910, and communication modules 940 coupled to the processor 910.
  • the communication module 940 is for bidirectional communications.
  • the communication module 940 has one or more communication interfaces to facilitate communication with one or more other modules or devices.
  • the communication interfaces may represent any interface that is necessary for communication with other network elements.
  • the communication module 940 may include at least one antenna.
  • the processor 910 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital reference signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples.
  • the device 900 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.
  • the memory 920 may include one or more non-volatile memories and one or more volatile memories.
  • the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 924, an electrically programmable read only memory (EPROM) , a flash memory, a hard disk, a compact disc (CD) , a digital video disk (DVD) , and other magnetic storage and/or optical storage.
  • the volatile memories include, but are not limited to, a random access memory (RAM) 922 and other volatile memories that will not last in the power-down duration.
  • a computer program 930 includes computer executable instructions that are executed by the associated processor 910.
  • the program 930 may be stored in the ROM 920.
  • the processor 910 may perform any suitable actions and processing by loading the program 930 into the RAM 920.
  • the embodiments of the present disclosure may be implemented by means of the program 930 so that the device 900 may perform any process of the disclosure as discussed with reference to FIGs. 2 to 8.
  • the embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.
  • the program 930 may be tangibly contained in a computer readable medium which may be included in the device 900 (such as in the memory 920) or other storage devices that are accessible by the device 900.
  • the device 900 may load the program 930 from the computer readable medium to the RAM 922 for execution.
  • the computer readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like.
  • FIG. 10 shows an example of the computer readable medium 1000 in form of CD or DVD.
  • the computer readable medium has the program 930 stored thereon.
  • various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, device, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
  • the present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium.
  • the computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the method 800 as described above with reference to FIG. 8.
  • program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or split between program modules as desired in various embodiments.
  • Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.
  • Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented.
  • the program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
  • the computer program codes or related data may be carried by any suitable carrier to enable the device, device or processor to perform various processes and operations as described above.
  • Examples of the carrier include a reference signal, computer readable medium, and the like.
  • the computer readable medium may be a computer readable reference signal medium or a computer readable storage medium.
  • a computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Selon des modes de réalisation, la présente invention concerne des dispositifs, des procédés, des appareils et des supports de stockage lisibles par ordinateur pour la détection de préambule Wi-Fi. Le procédé comprend l'obtention, au niveau d'un premier dispositif, d'une pluralité d'échantillonnages associés à un signal émis par un second dispositif, des périodes d'échantillon respectives de la pluralité d'échantillonnages se chevauchant ; la détermination d'un niveau de normalisation de puissance associé à la pluralité d'échantillonnages par réalisation d'une opération d'autocorrélation pour la pluralité d'échantillonnages ; et la détection d'un préambule dans la pluralité d'échantillonnages au moins sur la base du niveau de normalisation de puissance. De cette manière, le préambule Wi-Fi peut être détecté par le système cellulaire avec une faible complexité et une grande précision.
PCT/CN2021/126526 2021-10-26 2021-10-26 Détection de préambule wi-fi WO2023070339A1 (fr)

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PCT/CN2021/126526 WO2023070339A1 (fr) 2021-10-26 2021-10-26 Détection de préambule wi-fi
CN202180103683.1A CN118202765A (zh) 2021-10-26 2021-10-26 Wi-fi前导码检测

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PCT/CN2021/126526 WO2023070339A1 (fr) 2021-10-26 2021-10-26 Détection de préambule wi-fi

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Citations (4)

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Publication number Priority date Publication date Assignee Title
US20160248555A1 (en) * 2015-02-20 2016-08-25 Qualcomm Incorporated Superposition coding based preamble designs for co-existing radio access technologies
CN107294894A (zh) * 2016-03-31 2017-10-24 中国科学院上海高等研究院 一种前导信号的生成、发送、接收方法和系统
WO2019066201A1 (fr) * 2017-09-26 2019-04-04 Samsung Electronics Co., Ltd. Appareil et procédé de génération et de détection de symbole de préambule
WO2020204449A1 (fr) * 2019-03-29 2020-10-08 Samsung Electronics Co., Ltd. Procédé et appareil de fonctionnement d'équipement à base de trame pour nr sans licence

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US20160248555A1 (en) * 2015-02-20 2016-08-25 Qualcomm Incorporated Superposition coding based preamble designs for co-existing radio access technologies
CN107294894A (zh) * 2016-03-31 2017-10-24 中国科学院上海高等研究院 一种前导信号的生成、发送、接收方法和系统
WO2019066201A1 (fr) * 2017-09-26 2019-04-04 Samsung Electronics Co., Ltd. Appareil et procédé de génération et de détection de symbole de préambule
WO2020204449A1 (fr) * 2019-03-29 2020-10-08 Samsung Electronics Co., Ltd. Procédé et appareil de fonctionnement d'équipement à base de trame pour nr sans licence

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