CN112752322B

CN112752322B - 5G millimeter wave cell searching and accessing method based on terminal capability

Info

Publication number: CN112752322B
Application number: CN201911055581.8A
Authority: CN
Inventors: 王磊; 张诺亚
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2019-10-31
Filing date: 2019-10-31
Publication date: 2023-03-31
Anticipated expiration: 2039-10-31
Also published as: CN112752322A

Abstract

The disclosure relates to a 5G millimeter wave cell searching and accessing method based on terminal capability. A communication method for a first communication device side, comprising: constructing a first beam search codebook set for a first plurality of received beams; training and learning the first beam search codebook group by using a machine learning algorithm, and calculating a first weight combination of vector parameters of the first beam search codebook group; if the first communication device moves from the first location to the second location and the communication quality falls below a first threshold, then: predicting a second beam search codebook group based on the position information of the second position, the vector parameter of the first beam search codebook group, and the first weight combination using a machine learning algorithm; and searching a cell where a second beam matching the second position is located based on the predicted second beam search codebook group as a first target cell, wherein the communication quality achieved by the second beam is greater than a first threshold value.

Description

5G millimeter wave cell searching and accessing method based on terminal capability

Technical Field

The present disclosure relates to a communication apparatus and a communication method, and more particularly, to a communication apparatus and a communication method for 5G millimeter wave cell search and access.

Background

The application of the 5G millimeter wave technology can realize 5G ultra-high-speed and ultra-dense area coverage and improve the cell capacity. And the millimeter wave base station adopts different codebook sets to perform beamforming on the cell broadcast beam according to different coverage scenes. When the terminal is in a multi-base station overlapping coverage area, the terminal usually needs a long time to be matched and attached to the optimal cell, and the time for initially attaching to the network is relatively long.

With the development of terminal hardware and AI technology, the terminal self-learning capability is becoming one of the basic capabilities of mobile terminals. Therefore, there is a need for 5G millimeter wave cell search and access techniques based on mobile terminal capabilities.

Disclosure of Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this summary is not an exhaustive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

The present disclosure relates to a communication method for a first communication device side, including: receiving, at a first location, a first plurality of beams transmitted by a first plurality of network devices and communicating using a first beam of the first plurality of beams, wherein a quality of communication achieved by the first beam is greater than a first threshold; constructing a first beam search codebook set for a first plurality of received beams; training and learning the first beam search codebook group by using a machine learning algorithm and calculating a first weight combination of vector parameters of the first beam search codebook group; if the first communication device moves from the first location to the second location and the communication quality falls below a first threshold, then: predicting a second beam search codebook group based on the position information of the second position, the vector parameter of the first beam search codebook group, and the first weight combination using a machine learning algorithm; and searching a cell where a second beam matching the second position is located based on the predicted second beam search codebook group as a first target cell, wherein the communication quality achieved by the second beam is greater than a first threshold value.

The present disclosure relates to an electronic device for a first communication device side, comprising: one or more processors, and one or more memories having executable instructions stored thereon, which when executed by the one or more processors, cause the one or more processors to perform the above-described communication method for the first communication device side.

The present disclosure relates to a non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by one or more processors, cause the one or more processors to perform the above-described communication method for a first communication device side.

Other features of the present invention and advantages thereof will become more apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.

The present disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

fig. 1A is a diagram illustrating a current terminal search cell broadcast beam pattern;

fig. 1B is a diagram illustrating a current terminal locking a cell broadcast beam and performing network attachment;

fig. 2A to 2C are flowcharts illustrating a cell search and access method according to an exemplary embodiment of the present disclosure;

fig. 3A and 3B are diagrams illustrating a cell search and access method according to an exemplary embodiment of the present disclosure; and

fig. 4 is a block diagram illustrating an application example according to an exemplary embodiment of the present disclosure.

Detailed Description

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that in this specification and the drawings, the same reference numerals are used in common between different drawings to denote the same portions or portions having the same functions, and therefore, a repetitive description thereof will be omitted. In this specification, like reference numerals and letters are used to designate like items, and therefore, once an item is defined in one drawing, further discussion thereof is not required in subsequent drawings.

For convenience of understanding, the positions, sizes, ranges, and the like of the respective structures shown in the drawings and the like do not sometimes indicate actual positions, sizes, ranges, and the like. Therefore, the disclosed invention is not limited to the positions, dimensions, ranges, etc. disclosed in the drawings and the like.

The description will be made in the following order.

1. Overview of wireless communication techniques applicable to embodiments of the present disclosure

2. Examples of the invention

3. Application example

4. Conclusion

To facilitate a better understanding of the technical solutions according to the present disclosure, some wireless communication technologies applicable to the embodiments of the present disclosure are briefly described below.

The base station and the UE have multiple antennas supporting MIMO technology. The use of MIMO technology enables base stations and UEs to exploit the spatial domain to support spatial multiplexing, beamforming, and transmit diversity. Spatial multiplexing may be used to transmit different data streams simultaneously on the same frequency. These data streams may be transmitted to a single UE to increase data rates (which may be classified as SU-MIMO technology) or to multiple UEs to increase the overall system capacity (which may be classified as MU-MIMO technology). This is achieved by spatially precoding each data stream (i.e., applying amplitude scaling and phase adjustments at baseband) and then transmitting each spatially precoded stream over multiple transmit antennas on the Downlink (DL) from the base station to the UE. The spatially precoded data streams arrive at the UE(s) with different spatial signatures, which enables each of the UE(s) to receive the data streams via its multiple antennas and recover one or more data streams destined for the UE. On the Uplink (UL) from the UEs to the base station, each UE transmits spatially precoded data streams through its multiple antennas, which enables the base station to receive the data streams through its antennas and identify the source of each spatially precoded data stream.

In addition to spatial precoding at baseband, the phases of the multiple antennas connected to each radio frequency link can be adjusted to focus the transmit/receive energy of the respective radio frequency link in a particular direction using beamforming to improve signal transmit/receive strength. The beams mentioned in the following embodiments of the present disclosure are formed mainly in this way.

Next, radio protocol architecture for user plane and control plane in LTE (long term evolution), NR (new radio) is explained. The radio protocol architecture for the UE and the eNB, gNB is shown with three layers: layer 1, layer 2 and layer 3. Layer 1 (L1 layer) is the lowest layer and implements various physical layer signal processing functions. The L1 layer will be referred to herein as the physical layer. Layer 2 (L2 layer) is above the physical layer and is responsible for the link above the physical layer between the UE and the eNB, gNB.

In the user plane, the L2 layer includes a Medium Access Control (MAC) sublayer, a Radio Link Control (RLC) sublayer, and a Packet Data Convergence Protocol (PDCP) sublayer, which are terminated at the eNB, gNB, on the network side. The UE may also have several upper layers above the L2 layer, including a network layer (e.g., IP layer) that terminates at a PDN gateway on the network side, and an application layer that terminates at the other end of the connection (e.g., far end UE, server, etc.).

The PDCP sublayer provides multiplexing between different radio bearers and logical channels. The PDCP sublayer also provides header compression for upper layer data packets to reduce radio transmission overhead, security by ciphering the data packets, and handover support for UEs between enbs, gnbs. The RLC sublayer provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to hybrid automatic repeat request (HARQ). The MAC sublayer provides multiplexing between logical channels and transport channels. The MAC sublayer is also responsible for allocating the various radio resources (e.g., resource blocks) in one cell among the UEs. The MAC sublayer is also responsible for HARQ operations.

In the control plane, the radio protocol architecture for the UE and eNB, gNB is substantially the same for the physical and L2 layers, with the difference that there is no header compression function for the control plane. The control plane also includes a Radio Resource Control (RRC) sublayer in layer 3 (L3 layer). The RRC sublayer is responsible for obtaining radio resources (i.e., radio bearers) and for configuring the lower layers using RRC signaling between the eNB, the gNB, and the UE.

Various signal processing functions of the L1 layer (i.e., physical layer) implemented by the base station side are briefly described. These signal processing functions include coding and interleaving to facilitate Forward Error Correction (FEC) for the UE and mapping to signal constellations based on various modulation schemes (e.g., binary Phase Shift Keying (BPSK), quadrature Phase Shift Keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols are then split into parallel streams. Each stream is then used, along with a reference signal, to generate a physical channel that carries a stream of time-domain symbols. The symbol stream is spatially precoded to produce a plurality of spatial streams. The channel estimates may be used to determine coding and modulation schemes and for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the UE. Each spatial stream is then provided to a different antenna via a separate transmitter. Each transmitter modulates an RF carrier with a respective spatial stream for transmission.

At the UE, each receiver receives a signal through its respective antenna. Each receiver recovers information modulated onto a Radio Frequency (RF) carrier and provides the information to various signal processing functions of the L1 layer. Spatial processing is performed on this information at the L1 layer to recover any spatial streams destined for the UE. If multiple spatial streams are destined for the UE, they may be combined into a single symbol stream. The symbol stream is then converted from the time domain to the frequency domain. Each symbol, as well as the reference signal, is recovered and demodulated by determining the most likely signal constellation points transmitted by the eNB, gNB. These soft decisions may be based on channel estimates. These soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the eNB, the gNB on the physical channel. These data and control signals are then provided to higher layer processing.

2. Examples of the embodiments

The beam scanning process in a wireless communication system is described below in conjunction with fig. 1A. Fig. 1A is a diagram illustrating a current terminal search cell broadcast beam pattern.

As shown in fig. 1A, when identifying a cell broadcast beam, terminal 1100 needs to scan broadcast beams from

multiple base stations

1200, 1300, 1400, 1500 \8230, m, etc. and select an optimal beam according to signal strength.

The following describes a process of beam locking and network attachment in a wireless communication system with reference to fig. 1B. Fig. 1B is a diagram illustrating a current terminal locking a broadcast beam and performing network attachment.

As shown in fig. 1B, the terminal 1100 locks the beam with the strongest signal, requests to access the cell where the beam is located, and performs network attachment. For example, as shown in fig. 1B, terminal 1100 accesses the cell of base station 1300.

Based on the cell search and access methods shown in fig. 1A and fig. 1B, it can be found that the method for searching cell broadcast beams by the current terminal mainly includes:

the terminal scans each broadcast beam periodically sent by a plurality of cells at different angles and selects an optimal beam according to the signal intensity, thereby acquiring cell synchronization information and demodulation information and initiating network attachment.

Since the weight design of cell broadcast beams depends on the cell coverage scenario, a set of weights is usually chosen statically manually for a cell during the network deployment phase. However, the manual configuration has a certain subjectivity, so when there are multiple beam weights in multiple coverage scenarios, the terminal needs to spend more time scanning and comparing the beams, and can select the cell where the optimal beam is located to perform network attachment.

Therefore, a technology capable of quickly selecting an optimal beam and accessing to a cell where the optimal beam is located is needed.

In view of the above, through intensive research, the inventor of the present application has proposed a novel cell search and access method, which is particularly suitable for improving cell search and access capabilities of a terminal in a multi-broadcast beam superposition coverage scenario, and solving the problems of low network search efficiency of the terminal in a millimeter wave cell and low network attachment speed of the terminal in a complex coverage scenario.

In some embodiments, the technical solution of the present disclosure constructs, by the terminal, a terminal-side beam search codebook group using information such as terminal position information, beam angle information, and SSB beam strength as vector parameters for the searched cells; training the beam search codebook group by using a machine learning algorithm (such as a genetic algorithm, an ant colony algorithm, a neural network algorithm and the like), learning and calculating the optimal weight combination of the vector parameters of the beam search codebook group; when the position of the terminal changes, predicting a new terminal side beam search codebook group based on the constructed terminal side beam search codebook group, new position information and the optimal weight combination; and then searching a codebook group based on the predicted terminal side beam to quickly search an optimal beam and access a cell where the optimal beam is located.

Exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. That is, the communication apparatus and the communication method herein are shown by way of example to illustrate embodiments of the structures and methods in the present disclosure. Those skilled in the art will understand, however, that they are merely illustrative of ways in which the invention may be practiced and not exhaustive. Furthermore, the figures are not necessarily to scale, some features may be exaggerated to show details of particular components.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values.

A communication apparatus and a communication method according to an exemplary embodiment of the present disclosure will be described in detail below with reference to fig. 2A, 2B, 2C, 3A, and 3B by taking 5G millimeter wave cell search and access as an example. Those skilled in the art will appreciate that the present invention is not limited to the steps shown in the figures, but rather, can implement a method of cell fast search and access based thereon.

Fig. 2A to 2C are flowcharts illustrating a cell search and access method according to an exemplary embodiment of the present disclosure.

As shown in fig. 2A, in a multi-coverage scenario, a terminal searches for and receives multiple beams transmitted from multiple base stations and selects an optimal beam for communication in step 201. The optimal beam has an optimal communication quality and is greater than a predetermined threshold.

In some embodiments, the evaluation parameters of the communication quality include Reference Signal Received Power (RSRP), signal-to-noise ratio (SNR), signal-to-interference-and-noise ratio (SINR).

It is next transferred to step 202. At step 202, the terminal constructs a terminal-side beam search codebook set for the searched beams.

In some embodiments, the terminal constructs the terminal-side beam search codebook group with position information of the terminal, beam angle information transmitted by a plurality of base stations, SSB (SS/PBCH Block) beam strength information transmitted by a plurality of base stations, and the like as vector parameters.

It is next transferred to step 203. In step 203, the terminal trains, learns and calculates the optimal weight combination of the vector parameters of the beam search codebook set on the constructed terminal side by using a machine learning algorithm.

In some embodiments, the machine learning algorithm comprises a genetic algorithm, an ant colony algorithm, a neural network algorithm, or the like.

Furthermore, although not shown, in some embodiments, the above steps 201 to 203 are not necessarily performed in order. For example, steps 202 and 203 may be performed first, and then step 201 may be performed.

Next, a transition is made to step 204. At step 204, the terminal confirms whether its location has changed and whether the communication quality has degraded.

If the location of the terminal has not changed or the communication quality has not decreased, the terminal continues to select the optimal beam selected in step 201 for communication.

If the location of the terminal is changed and the communication quality is degraded, the process proceeds to step 205.

At step 205, the terminal predicts a new beam search codebook group based on the location information of the changed location, the vector parameters of the constructed terminal-side beam search codebook group, and the calculated optimal weight combination using a machine learning algorithm.

It is next transferred to step 206. In step 206, the terminal searches and selects a beam of a codebook having the best communication quality among the beams as an optimal beam based on the predicted beam search codebook group, and selects a cell to which the optimal beam belongs as an optimal target cell. Further, the communication quality achieved by the optimal beam is greater than a predetermined threshold.

It is next transferred to step 207. At step 207, the terminal initiates a random access request to the selected optimal target cell.

It is next transferred to step 208. At step 208, the terminal confirms whether the optimal target cell accepts the access request.

If the optimal target cell accepts the access request of the terminal, it goes to step 213 as follows.

If the optimal target cell rejects the access request of the terminal, the process goes to step 209 (see fig. 2B) as follows.

At step 209, the terminal initiates an access request using the cell where the second best beam matching the changed position is located as the second best target cell, where the communication quality achieved by the second best beam is less than that achieved by the best beam but greater than a predetermined threshold.

It is next transferred to step 210. At step 210, the terminal confirms whether the second best target cell accepts the access request.

If the second best target cell accepts the access request of the terminal, the process goes to step 213 as follows.

If the second best target cell rejects the access request of the terminal, the process goes to step 211 (see fig. 2C) as follows.

In step 211, the terminal uses the cell where the sub-second best beam matching the changed position is located as a sub-second best target cell to initiate an access request, where the communication quality achieved by the sub-second best beam is less than that achieved by the optimal beam and the sub-second best beam, but greater than a predetermined threshold.

It next goes to step 212. At step 212, the terminal confirms whether the next best target cell accepts the access request.

If the next best target cell accepts the access request of the terminal, the procedure goes to step 213 as follows.

If the next best target cell refuses the access request of the terminal, the step 201 is returned.

At step 213, the terminal camps on the selected cell and initiates service until a cell handover is required.

In addition, although not shown, in some embodiments, when the location of the terminal is changed again and the communication quality is degraded after step 213, the terminal may perform steps 205-212 described above again. That is, the cell search and access method according to the embodiment of the present invention may be performed cyclically according to the change in the location of the terminal and the communication quality.

Additionally, although not shown, in some embodiments, steps 209 through 212 may be omitted. That is, when the optimal target cell rejects the access request of the terminal, it returns to step 201 directly.

In addition, although not shown, in some embodiments, steps 211 through 212 may be omitted. That is, when the second best target cell rejects the access request of the terminal, it returns to step 201 directly.

As described above in conjunction with fig. 2A to 2C, a terminal device according to an embodiment of the present invention may predict a beam search codebook group based on a constructed beam search codebook group and new location information using a machine learning algorithm after a location change, and select a preferred beam based on the predicted beam search codebook group. When the cell where the beam selected based on the predicted beam search codebook group is located rejects the terminal access or the communication quality of the selected beam is lower than a predetermined threshold, the terminal may update the search of the base station and update the received beam, further reselect the beam having the optimal communication quality for communication, and update the learning result of the beam.

In order to more fully understand the present invention, specific examples of a cell search and access method according to exemplary embodiments of the present disclosure will be described in detail below, taking fig. 3A and 3B as examples. Note that this example is not intended to be construed as limiting the invention. For example, the present invention is not limited to the specific configuration shown in fig. 3A and 3B, but is applicable to all cell searches and accesses having the same requirements or design considerations. The cell search and access procedures described above in connection with fig. 2A to 2C may also be applied to the corresponding features.

Fig. 3A and 3B are diagrams illustrating communication between a base station and a terminal according to an exemplary embodiment of the present disclosure.

As shown in fig. 3A, when at location a, terminal 3100 searches for and receives beams transmitted from

base stations

3200, 3300, 3400, 3500 \8230 \8230nand selects an optimal beam for communication. The optimal beam has an optimal communication quality. As shown in fig. 3A, terminal 3100 selects a cell of base station 3300 to access and camp on.

Further, the terminal 3100 constructs a terminal-side beam search codebook group for the searched beams.

In some embodiments, the terminal 3100 constructs a terminal-side beam search codebook set with terminal location information, base station transmitted beam angle information, and base station transmitted SSB beam strength information, etc. as vector parameters.

In addition, the terminal 3100 trains, learns, and calculates an optimal weight combination of vector parameters of the beam search codebook group on the constructed terminal side by using a machine learning algorithm.

Furthermore, although not shown, in some embodiments, the terminal 3100 may also construct and train, learn, and compute a terminal-side beam search codebook set before selecting a beam with the best communication quality for communication.

As shown in fig. 3B, when the terminal 3100 moves from the position a to the position B and the communication quality deteriorates, the terminal 3100 predicts a new beam search codebook group based on the position information of the position B, the vector parameters of the already-constructed terminal-side beam search codebook group, and the calculated optimal weight combinations by using a machine learning algorithm.

Further, the terminal 3100 searches and selects a beam of a codebook having the best communication quality among them as an optimal beam based on the predicted beam search codebook group, and selects a cell to which the optimal beam belongs as an optimal target cell. Further, the communication quality achieved by the optimal beam is greater than a predetermined threshold.

In addition, the terminal 3100 initiates a random access request to the selected optimal target cell, and confirms whether the optimal target cell accepts the access request.

If the optimal target cell accepts the access request of the terminal 3100 (as shown in fig. 3B), the terminal 3100 camps on the optimal target cell and initiates a service until a cell handover is required.

If the optimal target cell rejects the access request of the terminal 3100, the terminal 3100 initiates the access request using a cell in which the second best beam matching the position B is located as the second best target cell (not shown in the figure), wherein the communication quality achieved by the second best beam is less than that achieved by the optimal beam but greater than a predetermined threshold.

Next, the terminal 3100 confirms whether the suboptimal target cell accepts the access request.

If the second best target cell receives the access request of the terminal, the terminal 3100 camps on the second best target cell and initiates a service until cell handover is required (not shown in the figure).

If the second best target cell rejects the access request of the terminal, the terminal 3100 initiates the access request using the cell in which the second best beam matching the position B is located as the second best target cell (not shown in the figure), wherein the communication quality achieved by the second best beam is less than that achieved by the best beam and the second best beam, but greater than a predetermined threshold.

Next, the terminal 3100 confirms whether or not the next best target cell accepts the access request.

If the next best target cell accepts the access request of the terminal 3100, the terminal 3100 camps on the next best target cell and initiates a service until a cell handover is required (not shown in the figure).

If the next best target cell rejects the access request of the terminal 3100, the terminal 3100 updates the search for the base station and updates the received beam, thereby reselecting the beam having the best communication quality for communication (not shown in the figure).

In addition, the terminal 3100 may also construct a terminal-side beam search codebook group for the updated beam, and train, learn, and calculate a weight combination of vector parameters of the new beam search codebook group using a machine learning algorithm.

In addition, although not shown, in some embodiments, when the location of the terminal 3100 is changed again and the communication quality is degraded, the terminal 3100 may predict a new beam search codebook set again, and then search for and select an optimal target cell. That is, the terminal 3100 according to an embodiment of the present invention can cyclically perform the above-described cell search and access procedure according to the change in the terminal location and communication quality.

In addition, although not shown, in some embodiments, when the optimal target cell rejects the access request of the terminal 3100, the terminal 3100 may not select the second optimal target cell any more, but directly update the search for the base station and update the received beam, and thus select the optimal beam for communication.

Additionally, although not shown, in some embodiments, when the secondary target cell rejects the access request of the terminal 3100, the terminal 3100 may not select the secondary target cell, but directly update the search for the base station and update the received beam, and thus select the optimal beam for communication.

In addition, those skilled in the art will appreciate that the present disclosure includes other communication steps of the 5G millimeter wave cell search and access method in addition to those shown.

As shown in fig. 2A, fig. 2B, fig. 2C, fig. 3A, and fig. 3B, based on the service cell and neighbor cell data information obtained by the terminal self-learning, the millimeter wave cell coverage scenario is optimized, the millimeter wave cell coverage is improved, and then the rapid network search and the access camping in the millimeter wave cell are realized on the premise of not changing the network side deployment strategy.

Note that the term "location information" as used herein may be relative location information of the terminal with respect to the base station, or may be absolute location information (such as GPS information) of the terminal, i.e., the meaning of "location information" depends on a specific application scenario. However, whether the "location information" is the relative location information of the terminal with respect to the base station or the absolute location information of the terminal, the location relationship of the terminal with the base station should be explicitly indicated.

As used herein, the word "exemplary" means "serving as an example, instance, or illustration," and not as a "model" that is to be replicated accurately. Any implementation exemplarily described herein is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the detailed description.

As used herein, the term "substantially" is intended to encompass any minor variation resulting from design or manufacturing imperfections, device or component tolerances, environmental influences, and/or other factors. The word "substantially" also allows for differences from a perfect or ideal situation due to parasitic effects, noise, and other practical considerations that may exist in a practical implementation.

In addition, "first," "second," and like terms may also be used herein for reference purposes only, and thus are not intended to be limiting. For example, the terms "first," "second," and other such numerical terms referring to structures or elements do not imply a sequence or order unless clearly indicated by the context.

It will be further understood that the terms "comprises/comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In the present disclosure, the term "providing" is used broadly to encompass all ways of obtaining an object, and thus "providing an object" includes, but is not limited to, "purchasing," "preparing/manufacturing," "arranging/setting," "installing/assembling," and/or "ordering" the object, and the like.

Those skilled in the art will appreciate that the boundaries between the above described operations merely illustrative. Multiple operations may be combined into a single operation, single operations may be distributed in additional operations, and operations may be performed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments. However, other modifications, variations, and alternatives are also possible. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

3. Application example

Fig. 4 is a block diagram showing an example of a schematic configuration of a smartphone 400 to which the technology of the present disclosure can be applied. The smartphone 400 includes a processor 401, memory 402, storage 403, an external connection interface (I/F) 404, a camera 406, sensors 407, a microphone 408, an input device 409, a display device 410, a speaker 411, a wireless communication interface (I/F) 412, one or more antenna switches 415, one or more antennas 416, a bus 417, a battery 418, and an auxiliary controller 419.

The processor 401 may be, for example, a CPU or a system on a chip (SoC), and controls functions of an application layer and another layer of the smartphone 400. The memory 402 includes a RAM and a ROM, and stores data and programs executed by the processor 401. The storage device 403 may include a storage medium such as a semiconductor memory and a hard disk. The external connection interface 404 is an interface for connecting an external device such as a memory card and a Universal Serial Bus (USB) device to the smartphone 400.

The camera 406 includes an image sensor such as a Charge Coupled Device (CCD) and a Complementary Metal Oxide Semiconductor (CMOS), and generates a captured image. The sensors 407 may include a group of sensors such as a measurement sensor, a gyro sensor, a geomagnetic sensor, and an acceleration sensor. The microphone 408 converts sound input to the smartphone 400 into an audio signal. The input device 409 includes, for example, a touch sensor, a keypad, a keyboard, a button, or a switch configured to detect a touch on the screen of the display device 410, and receives an operation or information input from a user. The display device 410 includes a screen, such as a Liquid Crystal Display (LCD) and an Organic Light Emitting Diode (OLED) display, and displays an output image of the smartphone 400. The speaker 411 converts an audio signal output from the smartphone 400 into sound.

The wireless communication interface 412 supports any cellular communication scheme (such as LTE and LTE-advanced) and performs wireless communication. The wireless communication interface 412 may generally include, for example, a BB processor 413 and RF circuitry 414. The BB processor 413 may perform, for example, encoding/decoding, modulation/demodulation, and multiplexing/demultiplexing, and perform various types of signal processing for wireless communication. Meanwhile, the RF circuit 414 may include, for example, a mixer, a filter, and an amplifier, and transmits and receives a wireless signal via the antenna 416. The wireless communication interface 412 may be one chip module on which the BB processor 413 and the RF circuit 414 are integrated. As shown in fig. 4, the wireless communication interface 412 may include a plurality of BB processors 413 and a plurality of RF circuits 414. Although fig. 4 shows an example in which the wireless communication interface 412 includes a plurality of BB processors 413 and a plurality of RF circuits 414, the wireless communication interface 412 may also include a single BB processor 413 or a single RF circuit 414.

Further, the wireless communication interface 412 may support another type of wireless communication scheme, such as a short-range wireless communication scheme, a near field communication scheme, and a wireless Local Area Network (LAN) scheme, in addition to the cellular communication scheme. In this case, the wireless communication interface 412 may include a BB processor 413 and RF circuits 414 for each wireless communication scheme.

Each of the antenna switches 415 switches a connection destination of the antenna 416 between a plurality of circuits (e.g., circuits for different wireless communication schemes) included in the wireless communication interface 412.

Each of the antennas 416 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for the wireless communication interface 412 to transmit and receive wireless signals. As shown in fig. 4, the smartphone 400 may include multiple antennas 416. Although fig. 4 illustrates an example in which the smartphone 400 includes multiple antennas 416, the smartphone 400 may also include a single antenna 416.

Further, the smartphone 400 may include an antenna 416 for each wireless communication scheme. In this case, the antenna switch 415 may be omitted from the configuration of the smartphone 400.

The bus 417 connects the processor 401, memory 402, storage device 403, external connection interface 404, camera 406, sensor 407, microphone 408, input device 409, display device 410, speaker 411, wireless communication interface 412, and auxiliary controller 419 to each other. The battery 418 provides power to the various blocks of the smartphone 400 shown in fig. 4 via a feed line, which is partially shown in the figure as a dashed line. The auxiliary controller 419 operates the minimum necessary functions of the smartphone 400, for example, in a sleep mode.

In the smartphone 400 shown in fig. 4, the communication method described with reference to fig. 2A, 2B, 2C, 3A, and 3B may be implemented by the processor 401 or the auxiliary controller 419.

4. Conclusion

The invention sinks the self calculation capability of the terminal, realizes the detection and learning of a communication physical layer and improves the communication capability of the terminal.

The millimeter wave technology is one of the essential points of the 5G technology, the wave beam forming, wave beam searching and other technologies of the 5G millimeter wave at the terminal side are not completely mature, emerging terminal hardware and the 5G millimeter wave technology are combined, the communication capability of the terminal is improved, and meanwhile the network side coverage and data throughput efficiency can be improved.

The reinforcement of the learning capability based on the terminal side has no additional requirements on the speed of beam search and cell access on the millimeter wave wireless access network and the core network.

In addition, the communication device and the communication method according to the embodiments of the present invention have beneficial effects on further optimizing the access procedure of the terminal in the mm-wave cell, and on selecting the beam of the terminal in the service connection state in applications such as mimo. In addition, information such as a terminal side search codebook and the like can be provided to a network manager according to network operation and maintenance requirements, so that automatic beam optimization of the millimeter wave wireless network is realized.

In addition, embodiments of the present disclosure may also include the following examples:

(1) A communication method for a first communication device side, comprising:

receiving, at a first location, a first plurality of beams transmitted by a first plurality of network devices and communicating using a first beam of the first plurality of beams, wherein a quality of communication achieved by the first beam is greater than a first threshold;

constructing a first beam search codebook set for a first plurality of received beams;

training and learning the first beam search codebook group by using a machine learning algorithm and calculating a first weight combination of vector parameters of the first beam search codebook group;

if the first communication device moves from the first location to the second location and the communication quality falls below a first threshold, then:

predicting a second beam search codebook group based on the location information of the second location, the vector parameter of the first beam search codebook group, and the first weight combination using a machine learning algorithm;

and searching a cell where a second beam matching the second position is located based on the predicted second beam search codebook group as a first target cell, wherein the communication quality achieved by the second beam is greater than a first threshold value.

(2) The method of (1), wherein:

the first communication device initiates an access request to the first target cell.

(3) The method of (2), wherein:

and if the first target cell refuses the access of the first communication equipment, the first communication equipment uses a cell where a third beam matched with the second position is located as a second target cell to initiate an access request, wherein the communication quality realized by the third beam is smaller than that realized by the second beam but larger than the first threshold.

(4) The method of (3), wherein:

and if the second target cell refuses the access of the first communication equipment, the first communication equipment uses a cell where a fourth beam matched with the second position is located as a third target cell to initiate an access request, wherein the communication quality realized by the fourth beam is smaller than the communication quality realized by the second beam and the third beam and is larger than the first threshold.

(5) The method of (4), wherein:

if the third target cell refuses the first communication equipment to access or the communication quality achieved by the fourth beam is less than the first threshold value, then:

the first communication device receiving, at a second location, a second plurality of beams transmitted by a second plurality of network devices and communicating using a first beam of the second plurality of beams, wherein a quality of communication achieved by the first beam of the second plurality of beams is greater than a second threshold;

constructing a third beam search codebook set for the received second plurality of beams;

and training and learning the third beam search codebook group by using a machine learning algorithm and calculating a second weight combination of the vector parameters of the third beam search codebook group.

(6) The method of (5), wherein:

if the first communication device moves from the second location to the third location and the communication quality falls below a second threshold, then:

predicting a fourth beam search codebook group based on the position information of the third position, the vector parameter of the third beam search codebook group, and the second weight combination by using a machine learning algorithm;

and searching a cell in which a fifth beam matching the third position is located as a fourth target cell based on the predicted fourth beam search codebook group, wherein the communication quality achieved by the fifth beam is greater than a second threshold.

(7) The method of (6), wherein:

the first communication device performs the method of any one of (2) to (4).

(8) The method of (1), wherein:

the vector parameters include location information of the first communication device, beam angle information transmitted by the first plurality of network devices, and SSB beam strength information transmitted by the first plurality of network devices.

(9) The method of (5), wherein:

the vector parameters include location information of the first communication device, beam angle information transmitted by the second plurality of network devices, and SSB beam strength information transmitted by the second plurality of network devices, and the like.

(10) The method of (1), wherein:

the machine learning algorithm comprises a genetic algorithm, an ant colony algorithm, a neural network algorithm and the like.

(11) The method of (1), wherein:

the communication quality includes Reference Signal Received Power (RSRP), signal-to-noise ratio (SNR), and signal-to-interference-and-noise ratio (SINR).

(12) The method of (1), wherein:

the first communication device is a terminal device and the first plurality of network devices are base stations.

(13) The method of (5), wherein:

the second plurality of network devices are base stations.

(14) An electronic device for a first communication device side, comprising:

one or more processors, and

one or more memories having executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform the method of any one of (1) through (13).

(15) A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any one of (1) through (13).

Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. The various embodiments disclosed herein may be combined in any combination without departing from the spirit and scope of the present disclosure. Those skilled in the art will also appreciate that various modifications might be made to the embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A communication method for a first communication device side, comprising:

training and learning the first beam search codebook group by using a machine learning algorithm, and calculating a first weight combination of vector parameters of the first beam search codebook group;

predicting a second beam search codebook group based on the position information of the second position, the vector parameter of the first beam search codebook group, and the first weight combination using a machine learning algorithm;

2. The method of claim 1, wherein:

the first communication device initiates an access request to a first target cell.

3. The method of claim 2, wherein:

4. The method of claim 3, wherein:

5. The method of claim 4, wherein:

if the third target cell rejects the first communication device to access or the communication quality achieved by the fourth beam is less than the first threshold, then:

6. The method of claim 5, wherein:

and searching a cell where a fifth beam matching the third position is located as a fourth target cell based on the predicted fourth beam search codebook group, wherein the realized communication quality of the fifth beam is greater than a second threshold.

7. The method of claim 1, wherein:

8. The method of claim 5, wherein:

the vector parameters include location information of the first communication device, beam angle information transmitted by the second plurality of network devices, and SSB beam strength information transmitted by the second plurality of network devices.

9. The method of claim 1, wherein:

the machine learning algorithm comprises a genetic algorithm, an ant colony algorithm and a neural network algorithm.

10. The method of claim 1, wherein:

the communication quality comprises reference signal receiving power, signal-to-noise ratio and signal-to-interference-and-noise ratio.

11. The method of claim 1, wherein:

12. The method of claim 5, wherein:

the second plurality of network devices are base stations.

13. An electronic device for a first communication device side, comprising:

one or more processors, and

one or more memories having stored thereon executable instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of any one of claims 1-12.

14. A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 1-12.