Communication method and apparatus

Abstract

A communication method and apparatus, which can enable sequence sets generated by different devices to have better cross-ambiguity performance. In the method, a RAN node can acquire, from another device, a training condition, such as an initialization sequence and a loss function, used by another RAN node when generating a sequence set in an optimum manner, so that training can be performed on the basis of the training condition to obtain a sequence set. Since a certain RAN node can acquire a training condition used by another RAN node and determine a sequence set on the basis of a training condition identical or similar to the training condition, it can be ensured that a plurality of RAN nodes use an identical or similar training condition. In addition, since similar or identical sequence sets can be generated on the basis of an identical or similar training condition and different sequences in the same sequence set have relatively low cross-ambiguity sidelobes, different sequences in the similar or identical sequence sets also have relatively low cross-ambiguity sidelobes. Therefore, interference between terminals served by two RAN nodes can be reduced.

Description

Communication method and apparatus

[0001] The present application claims priority from the Chinese patent application No. 202411260796.4 filed on September 9, 2024, and entitled "Communication method and apparatus", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] Embodiments of the present application relate to the field of communication, in particular to a communication method and apparatus. BACKGROUND

[0003] In a communication, sensing or positioning scenario, a reference signal is usually generated based on a sequence with good correlation performance. The autocorrelation performance of the sequence can reflect the resolution capability of multiple paths or multiple targets, and the cross-correlation performance between sequences can reflect the interference suppression capability between multiple devices.

[0004] At present, the sequences adopted in the 3rd generation partnership project (3GPP) standard include Zadoff-Chu sequences (ZC sequences for short), Gold sequences and the like, which are all obtained based on an analytical construction method. However, the ambiguity performance of these sequences is poor. The ambiguity performance of a sequence can be understood as the correlation performance of the sequence considering Doppler frequency offset. Therefore, in the case of high Doppler frequency offset, the influence of poor ambiguity performance of these sequences is more obvious. SUMMARY

[0005] In the current communication system, the sequence obtained based on the analytical construction method has poor ambiguity performance. In a possible design, in addition to determining the sequence based on the analytical construction method, a sequence set with good ambiguity performance can also be generated based on an artificial intelligence (AI) optimization method. However, if each device optimizes to obtain a sequence set respectively, it is difficult to ensure that the sequence sets obtained by different devices have good mutual ambiguity performance. Therefore, the present application provides a communication method and apparatus, which can enable the sequence sets generated by different devices to have good mutual ambiguity performance.

[0006] In a first aspect, a communication method is provided. The method can be performed by a first communication device, or by a component of the first communication device, such as a processor, a chip, or a chip system of the first communication device, or by a logic module or software that can implement all or part of the functions of the first communication device. For example, the first communication device can be a radio access network (RAN) node. The method includes receiving first information indicating a first training condition, determining a first sequence set according to the first training condition, and transmitting and / or receiving a reference signal according to a first sequence in the first sequence set, wherein the first training condition includes a first initialization sequence and a first loss function.

[0007] According to the scheme, the first communication device can obtain a training condition from another communication device (e.g., a second communication device), so that the first communication device can determine a sequence set according to the training condition, and transmit and / or receive a reference signal according to a sequence in the sequence set. For example, when the second communication device is a core network device, it can be considered that the core network device configures the training condition to the RAN node, so that the core network can configure the same or similar training condition to adjacent RAN nodes. When the second communication device is a RAN node, it can be considered that multiple RAN nodes exchange the training condition, so that the multiple RAN nodes can use the same or similar training condition. The same training condition can refer to the same initialization sequence and loss function, and the similar training condition can refer to the same initialization sequence and similar loss function. Since the same or similar training condition can generate similar or same sequence sets, and different sequences in the same sequence set have low mutual ambiguity sidelobes, different sequences in the similar or same sequence sets also have low mutual ambiguity sidelobes, so that the multiple sequence sets generated by different devices have good mutual ambiguity performance, and the interference between devices using the multiple sequence sets can be reduced.

[0008] In a possible design, the method further includes receiving second information, where the second information indicates a parameter of the first loss function.

[0009] In a possible design, the method further includes receiving third information, where the third information indicates an expected value of the first loss function, and / or indicates an expected value of at least one index of the first loss function.

[0010] In a possible design, the first training condition further includes a first optimizer. The method further includes receiving fourth information, where the fourth information indicates a parameter of the first optimizer.

[0011] In a possible design, the method further includes transmitting fifth information, where the fifth information indicates a second training condition, and the second training condition is a recommended training condition for the first communication device.

[0012] In a possible design, the fifth information further indicates a training parameter associated with the second training condition; the training parameter associated with the second training condition includes a parameter of a second loss function and / or a parameter of a second optimizer, the second loss function being a loss function included in the second training condition, and the second optimizer being an optimizer included in the second training condition.

[0013] In a possible design, the method further includes: receiving sixth information, the sixth information indicating a training condition recommended by the terminal and / or a training parameter; and determining, according to the sixth information, the second training condition and / or the training parameter associated with the second training condition.

[0014] In a possible design, the method further includes: sending seventh information, the seventh information indicating the first training condition. Further, the seventh information further indicates a training parameter associated with the first training condition.

[0015] In a second aspect, a communication method is provided. The method can be performed by a second communication device, or by a component of the second communication device, e.g., a processor, a chip, or a chip system, etc. of the second communication device, or by a logic module or software that can implement all or part of the function of the second communication device. Exemplarily, the second communication device can be a core network device or a RAN node. The method includes: determining a first training condition, the first training condition including a first initialization sequence and a first loss function; and sending first information, the first information indicating the first training condition.

[0016] Based on the scheme, the second communication device can indicate a training condition to the first communication device, so that the first communication device can determine a sequence set according to the training condition, and thus send and / or receive a reference signal according to a sequence in the sequence set. In the case where the second communication device is a core network device, it can be considered that the core network device configures the training condition to a RAN node, and thus the same or similar training condition can be configured to adjacent RAN nodes. In the case where the second communication device is a RAN node, it can be considered that the training condition is exchanged between multiple RAN nodes, and thus the same or similar training condition can be used by the multiple RAN nodes. Since the same or similar training condition can generate similar or same sequence sets, and different sequences in the same sequence set have low mutual ambiguity sidelobes, different sequences in the similar or same sequence sets also have low mutual ambiguity sidelobes, thereby reducing interference between terminals served by the multiple RAN nodes.

[0017] In a possible design, the method further includes: sending second information, the second information indicating a parameter of the first loss function.

[0018] In a possible design, the method further includes: sending third information, where the third information indicates an expected value of the first loss function, and / or indicates an expected value of at least one indicator of the first loss function.

[0019] In a possible design, the first training condition further includes a first optimizer; and the method further includes: sending fourth information, where the fourth information indicates a parameter of the first optimizer.

[0020] In a possible design, determining the first training condition includes: receiving fifth information, where the fifth information indicates a second training condition, and the second training condition is a recommended training condition for the first communication device; and determining the first training condition according to the fifth information.

[0021] In a possible design, the fifth information further indicates a training parameter associated with the second training condition. The method further includes: determining a training parameter associated with the first training condition according to the training parameter associated with the second training condition; and sending eighth information, where the eighth information indicates the training parameter associated with the first training condition.

[0022] In a possible design, determining the first training condition includes: receiving ninth information, where the ninth information indicates the first training condition.

[0023] In a third aspect, a communication method is provided, which can be performed by a terminal, or by a component of the terminal, e.g., a processor, a chip, or a chip system, etc., of the terminal, or by a logic module or software that can implement all or part of the functions of the terminal. Hereinafter, the terminal is taken as an example, and the method includes: receiving seventh information indicating a first training condition, determining a first sequence set according to the first training condition, and receiving and / or sending a reference signal according to a first sequence in the first sequence set, where the first training condition includes a first initialization sequence and a first loss function.

[0024] Based on this scheme, the RAN node can indicate the training condition to the terminal, so that the terminal can generate a sequence set based on the training condition, thereby enabling the RAN node and the terminal to generate similar or identical sequence sets based on the same or similar training condition, and further enabling the RAN node and the terminal to perform subsequent sensing or communication based on the sequence set. On the one hand, since the RAN node does not need to send the sequence set generated by the RAN node to the terminal, signaling overhead can be reduced. On the other hand, since the RAN node and the terminal can generate similar or identical sequence sets based on the same or similar training condition, they can use similar or identical sequences for sensing or communication, thereby reducing signaling overhead while having little impact on sensing performance or communication performance.

[0025] In a possible design, the seventh information further indicates a training parameter associated with the first training condition. The training parameter associated with the first training condition includes a parameter of the first loss function and / or a parameter of the first optimizer.

[0026] In a possible design, the method further includes: sending sixth information, where the sixth information indicates a training condition and / or a training parameter recommended by the terminal.

[0027] With reference to the first aspect or the second aspect or the third aspect, in a possible design, the first training condition is one of the predefined at least one training condition.

[0028] Based on this possible design, the predefined at least one training condition is such that, in subsequent interactions of the devices, the training condition can be indicated by an index of the training condition, thereby reducing signaling overhead.

[0029] With reference to the first aspect or the second aspect or the third aspect, in a possible design, the first initialization sequence is one of a Zadoff-Chu (ZC) sequence, a sequence obtained by extending a ZC sequence, a Gold sequence, or an M sequence.

[0030] Based on this possible design, the initialization sequence can be set to a non-random, reproducible ZC sequence, a Gold sequence, or the like, thereby improving reproducibility of the sequence set.

[0031] With reference to the first aspect or the second aspect or the third aspect, in a possible design, the first initialization sequence is a Gold sequence, and the first information further indicates an initialization parameter of the Gold sequence; or the first initialization sequence is an M sequence, and the first information further indicates an initialization parameter of the M sequence.

[0032] With reference to the first aspect or the second aspect or the third aspect, in a possible design, the first loss function is related to at least one of the following indicators: a self-mobility peak-to-sidelobe level (APSL), a cross-mobility peak-to-sidelobe level (CPSL), a self-mobility integrated sidelobe level (AISL), a cross-mobility integrated sidelobe level (CISL), or a peak-to-average power ratio (PAPR).

[0033] Based on this possible design, the loss function is determined using the APSL, the CPSL, the AISL, the CISL, or the PAPR related to the mobility performance, which can enable different sequences in the sequence set obtained through final optimization to have better cross-mobility performance, thereby enabling similar sequence sets obtained based on the same or similar training conditions to also have better cross-mobility performance.

[0034] In a possible design of the first aspect or the second aspect or the third aspect, the parameter of the first loss function includes at least one of the following corresponding to the at least one index: a weight, a constraint value, a Doppler frequency offset range, a time delay range, or a sequence transmission duration.

[0035] In a possible design of the first aspect or the second aspect or the third aspect, the first information further indicates a parameter of the first loss function.

[0036] In a possible design of the first aspect or the second aspect or the third aspect, the first training condition further includes a first optimizer.

[0037] In a possible design of the first aspect or the second aspect or the third aspect, the parameter of the first optimizer includes at least one of the following: a learning rate, a learning rate decay value, or a momentum parameter.

[0038] In a possible design of the first aspect or the second aspect or the third aspect, the first information further indicates a parameter of the first optimizer.

[0039] In a fourth aspect, a communication method is provided. The method can be performed by a RAN node, or by a component of the RAN node, for example, a processor, a chip, or a chip system of the RAN node, or by a logic module or software that can implement all or part of the functions of the RAN node. The method is described below with the RAN node as an example, and includes: receiving first information, the first information indicating a training condition and / or a training parameter recommended by a terminal, the training condition recommended by the terminal including an initialization sequence and / or a loss function, and the training parameter recommended by the terminal including a parameter of the loss function; determining, according to the first information, a first training condition and / or a training parameter associated with the first training condition, the first training condition including a first initialization sequence and a first loss function; and sending second information. The second information indicates the first training condition and / or the training parameter associated with the first training condition, or the second information indicates a sequence set obtained according to the first training condition, or the second information indicates a first sequence, the first sequence being one of the sequence set obtained according to the first training condition.

[0040] Based on the scheme, the terminal can report its recommended or expected training condition to the RAN node, so that the RAN node can reasonably select the training condition to generate the sequence set, thereby improving the adaptability of the sequence set, and further improving the performance when subsequent communication or sensing is performed based on the sequence in the sequence set. In addition, in the case where the RAN node indicates the training condition to the terminal, the terminal can generate the sequence set based on the training condition, so that the RAN node and the terminal can generate similar or identical sequence sets (such as the first sequence set and the second sequence set being identical or similar) based on the same or similar training condition, and further perform subsequent sensing or communication based on the sequence set. On the one hand, since the first sequence set generated by the RAN node does not need to be sent to the terminal, the signaling overhead can be reduced. On the other hand, since the RAN node and the terminal can generate similar or identical sequence sets based on the same or similar training condition, they can use similar or identical sequences for sensing or communication, thereby reducing the signaling overhead while having little impact on the sensing performance or communication performance.

[0041] In a fifth aspect, a communication method is provided, which can be performed by a terminal, or by a component of the terminal, such as a processor, a chip, or a chip system of the terminal, or by a logic module or software that can realize all or part of the functions of the terminal. Hereinafter, the terminal is taken as an example, and the method comprises: sending first information, the first information indicating a training condition and / or a training parameter recommended by the terminal, the training condition recommended by the terminal including an initialization sequence and / or a loss function, and the training parameter recommended by the terminal including a parameter of the loss function; and receiving second information. The second information indicates a first training condition and / or a training parameter associated with the first training condition, or the second information indicates a sequence set obtained according to the first training condition, or the second information indicates a first sequence, the first sequence being one sequence in the sequence set obtained according to the first training condition; and the first training condition including a first initialization sequence and a first loss function. The technical effects brought by the fifth aspect can refer to the technical effects brought by the first aspect, which will not be repeated here.

[0042] In combination with the fourth aspect or the fifth aspect, in a possible design, the training condition recommended by the terminal and the first training condition are identical or different; or the training parameter recommended by the terminal and the training parameter associated with the first training condition are identical or different.

[0043] In a sixth aspect, a communication method is provided, which includes: determining, by a second communication device, a first training condition, and sending, by the second communication device, first information indicating the first training condition to a first communication device. The first communication device receives the first information, and determines a first sequence set according to the first training condition, so as to send and / or receive a reference signal according to a first sequence in the first sequence set. The first training condition includes a first initialization sequence and a first loss function. Exemplarily, the second communication device is a core network device or a RAN node, and the first communication device is a RAN node. The sixth aspect can bring the technical effects as described in the first aspect, which will not be repeated here.

[0044] In a possible design, the second communication device determining the first training condition includes: receiving, by the second communication device, fifth information from at least one first communication device, the fifth information from a first communication device indicating a training condition recommended by the first communication device; and determining, by the second communication device, the first training condition according to the fifth information from the at least one first communication device.

[0045] In a possible design, the second communication device determining the first training condition includes: receiving, by the second communication device, ninth information from a fourth communication device, the ninth information indicating the first training condition. Exemplarily, the fourth communication device and the first communication device are different RAN nodes.

[0046] In a seventh aspect, a communication device is provided, which is configured to implement various methods. The communication device includes modules, units, or means corresponding to the modules, units, or means for implementing the methods, which can be implemented by hardware, software, or by executing corresponding software by hardware. The hardware or software includes one or more modules or units corresponding to the functions.

[0047] In some possible designs, the communication device can include a processing module and a transceiver module. The processing module can be configured to implement the processing functions in any of the aspects and any possible implementation manners thereof. The transceiver module can include a receiving module and a sending module, which are configured to implement the receiving function and the sending function in any of the aspects and any possible implementation manners thereof.

[0048] In some possible designs, the transceiver module can be composed of a transceiver circuit, a transceiver, a transceiver, or a communication interface.

[0049] In an eighth aspect, a communication device is provided, which includes a processor and a memory. The memory is configured to store computer instructions, which, when executed by the processor, cause the communication device to perform the method in any of the aspects and any possible design thereof.

[0050] In a ninth aspect, a communication apparatus is provided, which comprises: a processor and a communication interface; the communication interface is configured to communicate with a module outside the communication apparatus; the processor is configured to execute computer programs or instructions to enable the communication apparatus to perform the method described in any of the aspects and any possible design thereof.

[0051] In a tenth aspect, a communication apparatus is provided, which comprises: at least one processor; the processor is configured to execute computer programs or instructions stored in a memory to enable the communication apparatus to perform the method described in any of the aspects and any possible design thereof. The memory can be coupled with the processor, or can be independent of the processor.

[0052] In an eleventh aspect, a communication apparatus (for example, the communication apparatus can be a chip or a chip system) is provided, which comprises a processor configured to implement the functions described in any of the aspects and any possible design thereof.

[0053] In some possible designs, the communication apparatus comprises a memory configured to store necessary program instructions and data.

[0054] In some possible designs, when the apparatus is a chip system, the apparatus can be composed of a chip, or can comprise a chip and other discrete devices.

[0055] The communication apparatus described in the seventh aspect to the eleventh aspect can be the first communication apparatus in the first aspect or the sixth aspect, or a device included in the first communication apparatus, such as a chip or a chip system; or the communication apparatus can be the second communication apparatus in the second aspect or the sixth aspect, or a device included in the second communication apparatus, such as a chip or a chip system; or the communication apparatus can be the terminal in the third aspect or the fifth aspect, or a device included in the terminal, such as a chip or a chip system; or the communication apparatus can be the RAN node in the fourth aspect, or a device included in the RAN node, such as a chip or a chip system.

[0056] In a twelfth aspect, a communication apparatus is provided, which can be the first communication apparatus, or a module or unit (for example, a chip, or a chip system, or a circuit) corresponding to the first communication apparatus in executing the method / operation / step / action described in the first aspect, or a module or unit capable of being used with the first communication apparatus.

[0057] Alternatively, the communication apparatus can be the second communication apparatus, or a module or unit (for example, a chip, or a chip system, or a circuit) corresponding to the second communication apparatus in executing the method / operation / step / action described in the second aspect, or a module or unit capable of being used with the second communication apparatus.

[0058] Alternatively, the communication apparatus can be a RAN node, or a module or unit (e.g., a chip, or a chip system, or a circuit) in the RAN node that performs the method / operation / step / action described in the fourth aspect, or a module or unit that can be used in the RAN node.

[0059] Alternatively, the communication apparatus can be a terminal, or a module or unit (e.g., a chip, or a chip system, or a circuit) in the terminal that performs the method / operation / step / action described in the third aspect or the fifth aspect, or a module or unit that can be used in the terminal.

[0060] It can be understood that, when the communication apparatus provided in any one of the seventh aspect to the twelfth aspect is a chip, the sending action / function of the communication apparatus can be understood as outputting information, and the receiving action / function of the communication apparatus can be understood as inputting information.

[0061] The thirteenth aspect provides a computer readable storage medium, which stores a computer program or instructions, when the computer program or instructions are executed on a communication apparatus, the communication apparatus can perform the method described in any one of the aspects and any possible design thereof.

[0062] The fourteenth aspect provides a computer program product containing instructions, when the computer program product is executed on a communication apparatus, the communication apparatus can perform the method described in any one of the aspects and any possible design thereof.

[0063] The fifteenth aspect provides a communication system, which includes a first communication apparatus and a second communication apparatus. Further, the communication system can also include a terminal. The first communication apparatus can be used to implement the method described in the first aspect and any possible design thereof, the second communication apparatus can be used to implement the method described in the second aspect and any possible design thereof, and the terminal can be used to implement the method described in the third aspect and any possible design thereof.

[0064] The technical effects brought by any one of the seventh aspect to the fifteenth aspect can be referred to the technical effects brought by different design manners of the first aspect to the sixth aspect, which will not be described herein. BRIEF DESCRIPTION OF DRAWINGS

[0065] FIG. 1 is a schematic diagram of a sensing scenario provided in the present application;

[0066] FIG. 2 is a schematic diagram of another sensing scenario provided in the present application;

[0067] FIG. 3 is a schematic diagram of a structure of a communication system provided in the present application;

[0068] FIG. 4 is a structural schematic diagram of an O-RAN system provided by the present application;

[0069] FIG. 5 is a flow schematic diagram of a communication method provided by the present application;

[0070] FIG. 6 is a phase comparison diagram of sequence m in two sequence sets generated based on different training conditions provided by the present application;

[0071] FIG. 7 is a phase comparison diagram of sequence m in two sequence sets generated based on the same training condition provided by the present application;

[0072] FIG. 8 is a flow schematic diagram of another communication method provided by the present application;

[0073] FIG. 9 is a schematic diagram of a correlation result of a sequence provided by the present application;

[0074] FIGS. 10-12 are structural schematic diagrams of communication apparatuses provided by the present application. DETAILED DESCRIPTION

[0075] In the description of the present application, unless otherwise specified, “ / ” represents that the objects before and after the “ / ” are in an “or” relationship, for example, A / B can represent A or B; “and / or” in the present application is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural.

[0076] In the description of the present application, unless otherwise specified, “multiple” means two or more than two. “At least one of the following” or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent: a, b, c, a and b, a and c, b and c, a and b and c, where a, b, and c can be single or multiple.

[0077] In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same functions and effects are distinguished by using “first”, “second”, etc. The skilled in the art can understand that “first”, “second”, etc. do not limit the quantity and execution order, and “first”, “second”, etc. also do not necessarily mean different.

[0078] In the embodiments of the present application, the word "exemplary" or "for example" is used to mean "an example of" or "an example, only. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the exemplary or for example embodiments are presented so as to enable a clear and concise disclosure of the present application. Exemplary or for example language is used merely to facilitate reading and understanding of the present application.

[0079] It can be understood that, the "embodiments" mentioned in the specification throughout mean that the specific features, structures or characteristics related to the embodiments are included in at least one embodiment of the present application. Therefore, the various embodiments throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It can be understood that, in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0080] It can be understood that, in the present application, "when" and "if" refer to the corresponding processing under certain objective conditions, not the time limit, and do not require judgment action when implementing, nor mean that there are other limitations.

[0081] It can be understood that, in some scenarios, some optional features in the embodiments of the present application can be implemented independently without relying on other features, such as the scheme currently based on, to solve the corresponding technical problems and achieve the corresponding effects. In some scenarios, it can also be combined with other features according to demand. Correspondingly, the device given in the embodiments of the present application can also realize these features or functions, which will not be described here.

[0082] In the present application, except for special description, the same or similar parts of each embodiment can be mutually referred. In various embodiments of the present application, if there is no special description and no logical conflict, the terms and / or descriptions of different embodiments are consistent and can be mutually referred. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship. The following embodiments of the present application do not constitute a limitation on the protection scope of the present application. Before introducing the embodiments, some terms related to the present application are introduced.

[0083] In order to facilitate the understanding of the technical scheme of the embodiments of the present application, first, the brief introduction of the related technology of the present application is as follows.

[0084] 1. Wireless sensing, sensing signal:

[0085] The technical principle of wireless sensing is different from that of wireless communication. For example, in wireless communication, the sending end modulates information on a radio wave and sends it to the receiving end, and the receiving end demodulates the signal carried on the radio wave to obtain the information. In the sensing scenario, the sending device radiates electromagnetic waves to the surrounding environment to send sensing signals, and the receiving device receives the sensing signals reflected by the surrounding environment and compares them with the transmitted sensing signals to sense the relevant information of the surrounding environment, such as whether there is a target to be detected in the environment, the number of targets, the positions of the targets, and the like. For example, the reflected sensing signal can also be referred to as a return signal, or the return signal of the sensing signal, which can be replaced with each other without limitation.

[0086] The sensing signal can be understood as a signal for sensing (or detecting) a sensed target. The sensed target can also be understood as a target object, such as a scatterer or a reflector. The sensing signal can be a probe signal, a linear frequency modulation signal, a radar signal, a radar sensing signal, a radar probe signal, an environmental sensing signal, a pulse signal, a signal in a wireless communication system, and the like. The sensing signal can be a reference signal, for example, the initial amplitude and phase information of which can be pre-configured to the receiving end by a configuration sequence or the like. The sensing signal can also be a data signal, and the receiving end can calculate the initial amplitude and phase of each data signal by data checking or other known modulation methods. The sensing signal can also have other names, which are not limited in the present application.

[0087] Generally, sensing is divided into single-station sensing and double-station sensing in terms of mode. In the single-station sensing mode, the sending end and the receiving end of the sensing signal are the same device. In terms of sensing process, the station not only sends the sensing signal, but also receives the reflection signal of the sensing signal on the target surface, so the single-station sensing mode can also be referred to as self-transmission and self-reception mode.

[0088] In the double-station sensing mode, the sending end and the receiving end of the sensing signal are two different devices. In terms of sensing process, station A sends the sensing signal, and station B receives the reflection signal of the sensing signal on the target surface, so the double-station sensing mode can also be referred to as self-transmission and other-reception or A-transmission and B-reception mode.

[0089] 2. Communication and sensing integration

[0090] In the evolution process of the fifth generation (5G) wireless communication technology to 5G-Advanced (5G-A) and future communication technology, the communication and sensing integration technology is considered as one of the key technologies that can expand the business capabilities of mobile communication networks. The core idea of this technology is to add sensing capabilities to the mobile communication network to build the ability to detect, image, and identify targets, so that the two capabilities of communication and sensing are integrated in one network, achieving harmonious coexistence and even mutual benefit.

[0091] Exemplarily, in the communication-sensing integration technology, from the perspective of sensing mode, six sensing scenarios shown in FIG. 1 can be included. Among them, sensing scenario (1) and sensing scenario (4) are single-station sensing modes, sensing scenario (1) is self-transmission and self-reception by the base station, and sensing scenario (4) is self-transmission and self-reception by the terminal. Sensing scenarios (2), (3), (5), and (6) are double-station sensing modes, sensing scenario (2) is transmission by base station A and reception by base station B, sensing scenario (3) is transmission by the base station and reception by the terminal, sensing scenario (5) is transmission by the terminal and reception by the base station, and sensing scenario (6) is transmission by terminal A and reception by terminal B. Among them, sensing scenarios (3)-(6) can also be referred to as UE-assisted sensing scenarios.

[0092] In addition, in the communication-sensing integration system, the base station has the ability to communicate with the terminal and has sensing capability. Exemplarily, as shown in (a) of FIG. 2, the base station can communicate with the terminal and can perform sensing in the self-transmission and self-reception mode, such as transmitting a sensing signal and receiving a return signal; or as shown in (b) of FIG. 2, base station A can communicate with the terminal and can perform sensing in the self-transmission and other-reception mode, such as transmitting a sensing signal and receiving a return signal by base station B.

[0093] Currently, in the scenarios of communication, sensing, or positioning, the reference signal is usually generated based on a sequence with good correlation characteristics. The correlation characteristics of the sequence can include the autocorrelation performance of the sequence and the cross-correlation performance between the sequences.

[0094] Exemplarily, in the communication scenario, the reference signal can include but is not limited to: a demodulation reference signal (DMRS), a sounding reference signal (SRS), a preamble carried in a physical random access channel (PRACH), etc. In the sensing scenario, the reference signal can be a sensing signal. In the positioning scenario, the reference signal includes but is not limited to: a positioning reference signal (PRS) or a sidelink positioning reference signal (SL-PRS).

[0095] The autocorrelation performance of the sequence can reflect the multi-path or multi-target resolution capability, and the cross-correlation performance between the sequences can reflect the interference suppression capability or interference degree between multiple devices.

[0096] Exemplarily, a sequence includes L elements, which is represented in the time domain as [x0, x1, x2, …, xL-1]. The autocorrelation function of the sequence is represented as: Rxx(k) = E[x(k) * x(k + L)], where E[.] represents the expectation operation, and * represents the complex conjugate operation. L-1For example, the (periodic) autocorrelation function of the sequence x can be represented as:

[0097] where τ = 0, 1, …, L-1, τ represents time domain offset, or called cyclic shift. * represents conjugate operation, and % represents modulo operation. l The elements in the sequence are denoted as x0, x1, x2, …, xL-1. According to the above formula, each correlation value R[τ] in the autocorrelation function of the sequence is obtained by correlating the sequence with the sequence cyclically shifted by τ.

[0098] For example, a sequence is represented in time domain as x = [x0, x1, x2, …, xL-1], and another sequence is represented in time domain as y = [y0, y1, y2, …, yL-1]. L-1 L-1 For example, a sequence is represented in time domain as x = [x0, x1, x2, …, xL-1], and another sequence is represented in time domain as y = [y0, y1, y2, …, yL-1]. L-1 For example, a sequence is represented in time domain as x = [x0, x1, x2, …, xL-1], and another sequence is represented in time domain as y = [y0, y1, y2, …, yL-1].

[0099] The meanings of the parameters can be referred to the descriptions of the corresponding parameters in the autocorrelation function above, and will not be repeated here.

[0100] Generally, the autocorrelation performance of the sequence can be understood as: when τ≠0, the amplitude of R[τ] is as low as possible compared to R[0] (the autocorrelation function value at τ=0). The cross-correlation performance between the sequences can be understood as: for τ = 0, 1, …, L-1, the amplitudes of R x,y [τ] are as low as possible.

[0101] In particular, in a mobile scenario, such as the sending end or the receiving end is in a mobile state, or the target to be perceived is in a mobile state, there is a Doppler frequency offset between the reference signal received by the receiving end and the reference signal sent by the sending end. The Doppler frequency offset will cause the received reference signal to be phase-rotated, that is, it will multiply the original sequence sent by the phase rotation caused by the Doppler frequency. In this scenario, the autocorrelation or cross-correlation performance of the sequence usually needs to be considered. The ambiguity is an extended performance index on the correlation performance index. The autocorrelation or cross-correlation of the sequence is obtained by correlating a sequence with another sequence cyclically shifted; the autocorrelation or cross-correlation of the sequence is further considered by multiplying a sequence with the phase rotation caused by the Doppler and correlating with the cyclic shift of another sequence. Therefore, the autocorrelation or cross-correlation of the Doppler for 0 can be understood as the autocorrelation / cross-correlation.

[0102] For example, the autocorrelation function of the sequence can be represented as:

[0103] The cross-correlation function between the sequences can be represented as:

[0104] wherein u represents the Doppler frequency, and T represents the transmission duration of the sequence.

[0105] It can be understood that the autocorrelation function can be understood as the autocorrelation function when the Doppler frequency is 0, and the cross-correlation function can be understood as the cross-correlation function when the Doppler frequency is 0. The autocorrelation function and the cross-correlation function are uniformly taken as examples for description in the following embodiments of the present application.

[0106] As a possible implementation, the sequence adopted by the reference signal is obtained according to an analytical construction manner, for example, the sequence can be a traditional Zadoff-Chu sequence (referred to as ZC sequence), a sequence obtained after extension of the ZC sequence, a Gold sequence, an M sequence, etc. For example, the DMRS, the PRS or the SL-PRS is generated based on the Gold sequence, and the SRS or the preamble in the PRACH is generated based on the ZC sequence.

[0107] For example, the expression of the ZC sequence can be represented as:

[0108] wherein l = 0, 1, …, L zc -1, L zc represents the length of the ZC sequence, and q represents the root of the ZC sequence, different roots correspond to different sequences. Further, the existing protocol TR 38.211 groups the ZC sequence, q can be determined according to the group index μ and the index v in the group, for example, L zc is usually a prime number, if a sequence of a non-prime length is to be generated, the ZC sequence can be extended, for example, if a sequence of M zc length is to be generated, it can be generated according to the following manner: u,v [n] = x q [n mod L zc ]

[0109] wherein mod represents the modulo operation, n = 0, 1, …, N zc -1, L zc is the largest prime number less than N zc .

[0110] For example, the sequence obtained after extension of the ZC sequence can be a sequence obtained after cyclic extension, zero padding extension, or repetition of the last few elements of the ZC sequence. In addition, the ZC sequence can be extended in other ways to obtain the extended sequence, and the extension manner is not limited in the present application.

[0111] Exemplarily, both Gold sequences and M sequences are pseudo-random sequences. The M sequence is also referred to as a maximum length linear shift register sequence. The Gold sequence is generated by combining two M sequences with equal order. For example, a 31-order Gold sequence defined in the existing protocol TR 38.211 can be expressed as: c[n] = (x1[n+N c ]+x2[n+N c ])mod2

[0112] wherein the first M sequence x1(n+31) = (x1[n+3]+x1[n])mod2 is initialized as x1[1] = 1 and x1[n] = 0 (n = 1, 2, …, 30). The second M sequence x2(n+31) = (x2[n+3]+x2[n+2]+x2[n+1]+x2[n])mod2 can be initialized as , and the specific value can be configured when used. N c is a constant. Exemplarily, N c = 1600.

[0113] However, the sequence obtained by the above-mentioned analytical construction method has poor ambiguity performance, and the auto-ambiguity peak sidelobe level (APSL) and the cross-ambiguity peak sidelobe level (CPSL) are high, especially in the case of high Doppler frequency offset.

[0114] As another possible implementation, the sequence can be obtained based on an optimization method, such as an artificial intelligence (AI) optimization method, to generate a sequence set with good auto-ambiguity performance and cross-ambiguity performance. For example, the model parameters / training variables can be defined as the sequence elements of the sequence set, the loss function / objective function is defined as the minimum APSL of each sequence and the CPSL between any two sequences, and the values of the sequence elements are updated until the loss value tends to be stable or lower than a certain threshold.

[0115] Exemplarily, in the embodiments of the present application, the sequence obtained based on the optimization method can also be understood as a sequence obtained based on computer search, a sequence obtained based on an AI method, or referred to as an optimization sequence, a computer search sequence, an AI sequence, etc., and the name thereof is not limited in the present application.

[0116] However, the sequence obtained based on the AI optimization method cannot be expressed by an analytical expression like the ZC sequence and the Gold sequence. If the protocol directly defines these sequences, a large amount of content is required. In particular, a sequence with a longer length includes a larger number of elements, and multiple solutions with similar performance can easily occur during optimization. For example, there can be multiple completely different sequences, but they have similar ambiguity performance. In this case, it is difficult to determine which of the multiple solutions with similar performance should be written into the protocol.

[0117] However, if each device optimizes to obtain a sequence set, it is difficult to ensure that the sequence sets obtained by different devices have good mutual ambiguity performance. For example, if base station 1 and base station 2 obtain sequence set 1 and sequence set 2, respectively, by using their respective optimization methods, the sequences in sequence set 1 and the sequences in sequence set 2 can have high CPSL, thereby causing serious interference between the terminals served by the two base stations.

[0118] Therefore, the base station can obtain the training conditions used by other base stations to generate a sequence set, such as an initial sequence and a loss function, and train the sequence set based on the training conditions. Since a base station can obtain the training conditions used by other base stations and determine a sequence set based on the training conditions, the same or similar training conditions can be used by multiple base stations. Since the same or similar training conditions can generate similar or identical sequence sets, and different sequences in the same sequence set have low mutual ambiguity sidelobes, different sequences in the similar or identical sequence sets also have low mutual ambiguity sidelobes, thereby reducing the interference between the terminals served by two base stations. The specific implementation of the method will be described in detail in the subsequent embodiments, and will not be described here.

[0119] The technical solutions of the embodiments of the present application can be applied to various communication systems, which can be a third generation partnership project (3GPP) communication system, for example, a long term evolution (LTE) system, a fourth generation (4th generation, 4G) system, a new radio (NR) system, a fifth generation (5th generation, 5G) system, a system in which LTE and 5G are hybrid networked, a perception system, a communication and perception integrated system, a non-terrestrial network (NTN), a device-to-device (D2D) communication system, a vehicle to everything (V2X) communication system, a machine-type communication (MTC) system, an internet of things (IoT) system, or other future communication systems. The communication system can also be a non-3GPP communication system, which is not limited.

[0120] It should be noted that the above-mentioned communication system to which the present application is applicable is only an example, and the communication system to which the present application is applicable is not limited thereto. The communication system provided by the present application does not cause any limitation to the solutions of the present application. Here, it is uniformly stated that the following will not be described in detail.

[0121] FIG. 3 shows a possible, non-limiting system diagram. As shown in FIG. 3, the communication system 30 includes a radio access network (RAN) 300. Optionally, it can also include a core network (CN) 400 and / or the Internet (not shown in FIG. 3). The RAN 300 includes at least one RAN node (such as 310a and 310b in FIG. 3, collectively referred to as 310) and at least one terminal (such as 320a-320j in FIG. 3, collectively referred to as 320). The core network 400 includes at least one core network device.

[0122] Optionally, other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in FIG. 3), etc., can also be included in the RAN 300. The terminal 320 is connected to the RAN node 310 in a wireless manner (such as the two communicate through an air interface). The RAN node 310 is connected to the core network 400 in a wireless or wired manner. The core network device in the core network 400 and the RAN node 310 in the RAN 300 can be different physical devices respectively, or can be the same physical device integrated with the logical functions of the core network and the logical functions of the wireless access network.

[0123] In a possible implementation, the RAN 300 can be a 3GPP related cellular system, e.g., a 4G, 5G mobile communication system, an NTN system (e.g., an NTN supporting a transparent mode and / or a regenerative mode, or an NTN supporting an earth fixed cell and / or an earth moving cell), or a future mobile communication system. The RAN 300 can also be an open RAN (O-RAN or ORAN), a cloud radio access network (CRAN), or a wireless fidelity (WiFi) system. The RAN 300 can also be a communication system integrating two or more of the above systems.

[0124] In some scenarios, the roles of the RAN nodes 310 and the terminals 320 are relative, e.g., the network element 320i in FIG. 3 can be a helicopter or a drone, which can be configured to move a base station, and for a terminal 320j accessing to the RAN 300 through the network element 320i, the network element 320i is a base station; but for the base station 310a, the network element 320i is a terminal. The RAN nodes 310 and the terminals 320 are sometimes collectively referred to as communication apparatuses, e.g., the network elements 310a and 310b in FIG. 3 can be understood as communication apparatuses with base station functions, and the network elements 320a-320j can be understood as communication apparatuses with terminal functions.

[0125] In a possible implementation, the RAN nodes 310 are network side devices with wireless transceiving functions. The RAN nodes are sometimes also referred to as RAN entities or access nodes, etc., and form part of the communication system to help terminals to realize wireless access. The RAN nodes 310 in the communication system 30 can be nodes of the same type or nodes of different types.

[0126] As a possible implementation, the RAN nodes 310 can be access network devices, e.g., base stations, evolved NodeBs (eNodeBs), access points (APs), transmission reception points (TRPs), next generation NodeBs (gNBs) in a 5G mobile communication system, base stations of a subsequent evolution of 3GPP, base stations in future mobile communication systems, access nodes in a WiFi system, wireless relay nodes, wireless backhaul nodes, etc.

[0127] For example, a RAN node can be a macro base station (e.g., 310a in FIG. 3), a micro node or an indoor node (e.g., 310b in FIG. 3), a relay node or a donor node, or a radio controller in a CRAN scenario. Optionally, a RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, a RAN node in V2X technology can be a road side unit (RSU).

[0128] As another possible implementation, a terminal device is assisted by multiple RAN nodes to implement wireless access, and different RAN nodes respectively implement part of the functions of an access network device. For example, a RAN node can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), a radio unit (RU), or a sensing unit (SU), etc.

[0129] For example, a CU and a DU can be separately arranged, or can also be included in the same network element, for example, included in a baseband unit (BBU). An RU can be included in a radio frequency device or a radio frequency unit, for example, included in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0130] Optionally, a CU can be connected with a core network and one or more DUs. The CU can have part of the functions of the core network. In addition, an access network device can include one or more CUs, one or more DUs, and one or more RUs.

[0131] For example, a CU can be used to implement layer 2 (L2) and layer 3 (L3) functions, and further, the CU can also have part of the functions of the core network. A DU can be used to implement layer 1 (L1) and part of L2 functions, and an RU can be used to implement L1 computing and radio frequency (RF) digital part functions.

[0132] As shown in FIG. 4, the CU can be connected with the core network and one or more DUs. There is a backhaul interface between the CU and the core network, which is used to carry the traffic between the CU and the core network. There is a midhaul interface between the CU and the DU, which is used to carry the traffic between the CU and the DU. The DU can be connected with one or more RUs. There is a fronthaul interface between the DU and the RU, which is used to carry the traffic between the DU and the RU.

[0133] In hardware, the CU and the DU can include a chassis platform, a mainboard, peripheral devices and cooling devices. The mainboard contains a processing unit, a memory, an internal input / output (I / O) interface and an external connection port. Its hardware accelerator design has an interface, and the hardware functional components include storage of software, hardware and system debugging interface, and a single board management controller.

[0134] The DU is usually implemented using a multi-core processor and one or more hardware accelerators. Part of the DU protocol stack can be implemented in software running on the multi-core processor, and the computation-intensive L1 and L2 functions can be offloaded to the hardware accelerator based on field programmable gate array (FPGA) / graphic processing unit (GPU); or all L1 functions are offloaded to the hardware accelerator based on FPGA / GPU, while other protocol stack contents are implemented in software running on the processor; or all the protocol stack is implemented in software running on the processor. The hardware accelerator supports interconnection with an x86 or non-x86 processor, and similarly, the accelerator has a multi-channel peripheral component interconnect express (PCIe) interface pointing to a central processing unit (CPU) and is externally connected through a GbE connection.

[0135] The RU can include an O-RAN processing unit (OPU), a digital processing unit (DPU) and an RF processing unit.

[0136] The OPU is used to receive enhanced common public radio interface (eCPRI) frames from the O-RAN fronthaul and perform the fronthaul interface, the lowest layer L1 (encoding, scrambling, modulation, layer mapping, precoding), synchronization, beamforming, and resource unit mapping. The OPU can be implemented as a CPU, FPGA, or a specific application-specific integrated circuit (ASIC).

[0137] The DPU is used to perform synchronization, uplink (UL) digital down converters (DDC), downlink (DL) digital up conversion (DUC), channel failure ratio (CFR), digital pre-distortion (DPD) processing, etc., to improve power amplifier efficiency by reducing the peak to average power ratio (PAPR) / adjacent channel leakage ratio (ACLR) of the RF front end. The DPU can be implemented as an FPGA or an ASIC.

[0138] The RF processing unit includes a transceiver module, up / down converters, power amplifiers (PAs), low noise amplifiers (LNAs), Tx / Rx filters. The conversion between the analog and digital domains (e.g., digital to analog converter (DAC), analog to digital converter (ADC), RF sampling, frequency conversion) can be performed within the transceiver module. Note that the physical and logical partitioning within the RF processing unit does not require specific boundaries.

[0139] In different systems, the CU (or CU-CP and CU-UP), DU or RU can also have different names, but those skilled in the art can understand their meanings. For example, in the ORAN system, the CU can also be referred to as the O-RAN central unit (O-CU), the DU can also be referred to as the O-RAN distributed unit (O-DU), the CU-CP can also be referred to as the O-RAN central unit control plane (O-CU-CP), the CU-UP can also be referred to as the O-RAN central unit user plane (O-CU-UP), and the RU can also be referred to as the O-RAN radio unit (O-RU).

[0140] As yet another possible implementation, the RAN node can also be a non-real time RAN intelligent controller (Non-RT RIC or NRT RIC) and / or a near-real time RAN intelligent controller (Near-RT RIC or nRT RIC).

[0141] Among them, the Non-RT RIC is used to implement the non-real-time intelligent management of the RAN, can implement AI / machine learning (ML) including model training and model updating, and guide the application programs / functions in the Near-RT RIC based on a policy. The Near-RT RIC is used to implement the near-real-time intelligent management of the RAN, and realizes the near-real-time control and optimization of the modules and resources of the O-RAN through data collection and related operations on the E2 interface. The E2 interface can be understood as an open interface between two nodes (or endpoints).

[0142] All or part of the functions of the RAN node in the present application can also be implemented by software functions running on hardware, or by virtualized functions instantiated on a platform such as a cloud platform, or by software modules, hardware modules, or a combination of software modules and hardware modules. The RAN node in the present application can also be a logical node, a logical module or software that can implement all or part of the functions of the access network device, or a device with part of the functions of the access network device, such as a chip system, which can be installed in the access network device.

[0143] In a possible implementation, the core network device can refer to a device in the core network 400 that provides service support for the terminal. In the embodiments of the present application, the core network device in the core network 400 includes a sensing function (SF) network element, which is mainly responsible for sensing services and is used to implement sensing functions. The sensing functions include, for example, sensing control functions and / or sensing calculation functions. Further, the SF network element can also support sensing charging functions when the terminal and / or the RAN node perform sensing.

[0144] In a possible scenario, the functions of the SF network element can be implemented by a network data analysis function (NWDAF) network element, or the SF network element and the NWDAF network element can be combined. Alternatively, the SF network element can be deployed in combination with the core network, or can be deployed separately.

[0145] Optionally, in addition to the SF network element, the core network device in the core network 400 can also include at least one of the following: an access and mobility management function (AMF) network element, a session management function (SMF) network element, a user plane function (UPF) network element, a policy control function (PCF) network element, a unified data management (UDM) network element, an application function (AF) network element, a network exposure function (NEF) network element, a location management function (LMF) network element, and the like. Of course, the core network 400 can also include other core network devices, which are not limited.

[0146] In a possible implementation, the terminal 320 is a user-side device with wireless transceiving function, which can be a fixed device, a mobile device, a handheld device (for example, a mobile phone), a wearable device, a vehicle-mounted device, or a wireless device (for example, a communication module, a modem, or a chip system, etc.) built in the above devices. The terminal is used to connect people, objects, machines, etc., and can be widely used in various scenarios, such as cellular communication, D2D communication, V2X communication, MTC communication, IoT, virtual reality (VR), augmented reality (AR), industrial control, self driving, remote medical, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, unmanned aerial vehicle, robot, etc. For example, the terminal can be a handheld terminal in cellular communication, a communication device in D2D, an Internet of Things device in MTC, a camera in smart transportation and smart city, or a communication device on an unmanned aerial vehicle, etc. Alternatively, the terminal can be a mobile phone, a tablet computer, a computer with wireless transceiving function, a wearable device, a vehicle, an unmanned aerial vehicle, a helicopter, an airplane, a ship, a robot, a mechanical arm, a smart home device, etc. The embodiments of the present application do not limit the device form of the terminal. The terminal can also be referred to as a UE, a user terminal, a user device, a user unit, a user station, a terminal, an access terminal, an access station, a UE station, a remote station, a mobile device, or a wireless communication device, etc.

[0147] It should be noted that the system described in the embodiments of the present application is used to more clearly illustrate the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0148] The communication method provided by the embodiments of the present application will be described below by taking the interaction between the RAN node, the terminal, and the core network device in the communication system shown in FIG. 3 as an example. It should be noted that the names of messages, the names of parameters, or the names of information between the RAN node and the terminal in the embodiments described below are only examples, and other names can also be used in other embodiments. The method provided by the present application does not specifically limit this.

[0149] It can be understood that, in the embodiments of the present application, the RAN node or the terminal or the core network device can perform part or all of the steps in the embodiments of the present application, and these steps or operations are only examples, and the embodiments of the present application can also perform other operations or variations of various operations. In addition, various steps can be performed in different orders according to the embodiments of the present application, and it is possible that not all operations in the embodiments of the present application are performed.

[0150] It can be understood that, in the embodiments of the present application, the RAN node, the terminal, or the core network device is taken as an example for illustrating the execution subject of the interaction, but the present application does not limit the execution subject of the interaction. For example, the method performed by the RAN node in the present application can also be performed by a module (such as a chip, a chip system, or a processor) applied to the RAN node, and can also be implemented by a logical node, a logical module, or software capable of realizing all or part of the functions of the RAN node; the method performed by the terminal in the present application can also be performed by a module (such as a chip, a chip system, or a processor) applied to the terminal, and can also be implemented by a logical node, a logical module, or software capable of realizing all or part of the functions of the terminal; the method performed by the core network device in the present application can also be performed by a module (such as a chip, a chip system, or a processor) applied to the core network device, and can also be implemented by a logical node, a logical module, or software capable of realizing all or part of the functions of the core network.

[0151] Before the method flow provided by the present application is described, the training condition in the method will be described first.

[0152] In a possible implementation, the training condition in the embodiments of the present application can be understood as a training condition used when the sequence set is obtained in an optimization manner, such as a factor affecting the ambiguity performance of the sequence. In addition, the training condition can also be referred to as an optimization condition, and the name of the training condition is not limited in the present application.

[0153] As a possible implementation, obtaining the sequence set in an optimization manner can be understood as generating the sequence set by solving an optimization problem. The optimization problem can be related to at least one of the following: APSL, CPSL, auto-ambiguity integrated sidelobe level (AISL), cross-ambiguity integrated sidelobe level (CISL), or peak to average power ratio (PAPR).

[0154] For example, APSL represents the maximum value in the maximum auto-ambiguity sidelobe of all sequences in the sequence set. Assuming that X = {x 0 ,x1 , …, x M-1} represents a sequence set with capacity M (i.e. there are M sequences in the sequence set), where a sequence is denoted as , i.e. the sequence length is L or the sequence contains L elements in total, the APSL can be represented as:

[0155] where, represents the maximum auto-ambiguity sidelobe of a sequence x m . u represents the Doppler frequency and τ represents the cyclic shift.

[0156] Exemplarily, the CPSL represents the maximum value in the maximum cross-ambiguity sidelobes of any two sequences in the sequence set. Based on the sequence set X with the above assumptions, the CPSL can be represented as:

[0157] where, represents the maximum cross-ambiguity sidelobe between the sequence and the sequence . u represents the Doppler frequency and τ represents the cyclic shift.

[0158] Exemplarily, the AISL represents the sum or average of the auto-ambiguity sidelobes of all sequences in the sequence set. The CISL represents the sum or average of the cross-ambiguity sidelobes between any two sequences in the sequence set.

[0159] Exemplarily, the PAPR represents the ratio of the maximum value of the time-domain signal envelope power (P peak ) to the average power (P avg ) in decibel (dB), i.e.:

[0160] The PAPR can be understood as a value that measures the degree of fluctuation of the envelope of a signal, the greater the PAPR, the greater the degree of fluctuation of the envelope. A higher PAPR will reduce the performance of the communication system. The signal of a wireless communication system usually needs to be power amplified before being transmitted. Due to the limitations of technology and equipment cost, the power amplifier is usually linearly amplified within a range, and if it exceeds this range, the signal will be distorted. Signal distortion can cause the receiving end of the received signal to be unable to correctly parse the signal. In order to ensure that the peak value of the signal is still within the linear range of the power amplifier that can normally amplify power, it is necessary to reduce the average power of the transmitted signal. This way will cause the power amplifier to be low in efficiency, or equivalent to a smaller coverage range. Therefore, when designing a sequence, the elements of the sequence can be optimized so that the PAPR of the transmitted signal carrying the sequence is maintained at a relatively low level, for example, lower than 3 dB.

[0161] As a possible implementation, considering that the optimization problem is relatively complex, it can be solved by means of AI and the like, for example, taking the optimization problem as a loss function of a neural network to generate a sequence set.

[0162] Exemplarily, each element in the sequence set can be taken as a model parameter of a neural network / output of the neural network, and the optimization problem can be taken as a loss function, for example, the loss function is related to at least one index in the APSL, the CPSL, the AISL, the CISL or the PAPR. The neural network can be a single-layer 1 x ML fully connected neural network, M is the number of sequences included in the sequence set, L is the length of the sequence, and M and L are positive integers. Since the sequence is generated based on the AI mode, no training data is required, and therefore the input of the neural network can be set to 1 by default, and the model parameter of the neural network is the output of the neural network.

[0163] Exemplarily, it is assumed that the sequence set needs to be generated by solving the following optimization problem: min X wAPSL+(1-w)CPSL, then min X wAPSL+(1-w)CPSL can be taken as a loss function, and the sequence set is generated based on the AI mode. Wherein, w can be understood as a weight.

[0164] In addition, when the sequence is generated by means of optimization, the elements in the sequence can be constrained, for example, each element is constant modulus, and then the phase of the sequence element can be optimized, for example, the phase of the sequence element is taken as a model parameter. Wherein, the sequence element is constant modulus, which can be understood as that the sequence element is constant modulus in the time domain, or the sequence element is constant modulus in the frequency domain. The above can be understood as a time domain sequence element, and the DFT of the time domain sequence can be performed to obtain the corresponding frequency domain sequence. Or, the above can be understood as a frequency domain sequence element, and the IDFT of the frequency domain sequence can be performed to obtain the corresponding time domain sequence.

[0165] In a possible implementation, the training conditions in the embodiments of the present application, that is, the factors affecting the sequence ambiguity performance, can include an initialization sequence and a loss function. Optionally, the training conditions can also include an optimizer.

[0166] As a possible implementation, the initialization sequence can be a non-random, reproducible sequence, for example, the initialization sequence can be a ZC sequence, a sequence obtained by extending a ZC sequence, a Gold sequence, an M sequence, etc. The implementation of each sequence can refer to the related description above, which will not be repeated here. In addition, the initialization sequence can also be referred to as an initialization value, and of course can also have other names, which are not limited in the present application.

[0167] As a possible implementation, the loss function is related to at least one of the following indicators: APSL, CPSL, AISL, CISL, or PAPR. The implementation of each indicator can refer to the related description above, which will not be repeated here. For example, the loss function is related to at least one of APSL, CPSL, AISL, CISL, or PAPR, which can be understood as: the form of the loss function is related to at least one of APSL, CPSL, AISL, CISL, or PAPR.

[0168] Further, there are parameters of the loss function. The parameters of the loss function include at least one of the weight corresponding to the above-mentioned at least one indicator, the constraint value, the Doppler frequency offset range (also referred to as the Doppler frequency range), the time delay range, or the sequence transmission duration.

[0169] For example, the weight corresponding to a certain indicator can represent the influence degree of the indicator on the loss function; the constraint value corresponding to a certain indicator can be understood as the maximum value allowed after optimization of the indicator, or the expected value after optimization of the indicator; the Doppler frequency offset range, the time delay range, or the sequence transmission duration corresponding to a certain indicator can be understood as the Doppler frequency offset range, the time delay range, or the sequence transmission duration used when calculating the indicator. The Doppler frequency offset range can be understood as the value range of u in the expression of the above-mentioned APSL / CPSL / AISL / CISL, the time delay range can be understood as the value range of τ in the expression of APSL / CPSL / AISL / CISL, and the sequence transmission duration can be understood as T in the fuzzy function.

[0170] For example, APSL / CPSL / AISL / CISL can correspond to at least one of the weight, the constraint value, the Doppler frequency offset range (also referred to as the Doppler frequency range), the time delay range, or the sequence transmission duration, and PAPR can correspond to the weight and / or the constraint value.

[0171] For example, the parameters of the loss function can remain unchanged in the model optimization process (i.e., in the process of generating the sequence set), which do not need to be optimized. The parameters of the loss function can also be referred to as the hyperparameters of the loss function, which can be replaced with each other.

[0172] Exemplarily, the loss function can include but is not limited to: wAPSL+(1-w)CPSL, w1APSL+w2CPSL+w3PAPR, wAPSL+(1-w)CPSL+max(PAPR,p). Wherein, w, w1 can be understood as the weight corresponding to the APSL, (1-w), w2 can be understood as the weight of the CPSL, w3 can be understood as the weight of the PAPR, and p can be understood as the constraint value of the PAPR. w, w1, w2, w3 are all greater than or equal to 0 and less than or equal to 1, and p is a positive number.

[0173] As a possible implementation, the optimizer can include but is not limited to: stochastic gradient descent (SGD), adaptive moment estimation (Adam), root mean square propagation (RMSProp). Further, the parameters of the optimizer can include but are not limited to: learning rate, learning decay value, momentum parameter, etc.

[0174] Exemplarily, the parameters of the optimizer can remain unchanged during the model optimization process (i.e., during the generation of the sequence set), and do not need to be optimized. The parameters of the optimizer can also be referred to as the hyperparameters of the optimizer, and can be mutually replaced.

[0175] As a possible implementation, one possible use of the training condition is: the initialization sequence can be used as the initial model parameter of the neural network / the initial output of the neural network, and the model parameter of the neural network is continuously updated based on the loss function or based on the loss function and the optimizer, so that the value of the loss function generally presents a downward trend, and finally the sequence set is obtained.

[0176] In a possible implementation, the protocol can predefine at least one training condition, and each training condition can have a corresponding index or identifier. Exemplarily, the at least one training condition can include part or all of the training conditions shown in Table 1.

[0177] Table 1

[0178] Exemplarily, the ZC sequence and the Gold sequence in Table 1 can be understood as specific sequences, rather than sequence types. For example, the protocol can define which ZC sequence is included in the training condition, such as defining the parameters used to determine the ZC sequence, such as the value of the root of the ZC sequence. Similarly, the protocol can define which Gold sequence / M sequence is included in the training condition, such as defining the parameters used to determine the Gold sequence / M sequence, such as the initialization parameters of the Gold sequence / M sequence.

[0179] For example, the parameters of the loss function, such as the weights of various indicators, constraint values, Doppler ranges, time delay ranges, sequence transmission durations, and the like considered when calculating the APSL / CPSL / AISL / CISL, can be default values predefined by the protocol, or can be configurable and not limited.

[0180] For example, the parameters of the optimizer, such as the learning rate, learning decay value, momentum parameter, and the like, can be default values predefined by the protocol, or can be configurable and not limited.

[0181] It should be noted that Table 1 is only an example of a training condition, and the initialization sequence, loss function, or optimizer shown in Table 1 can be arbitrarily combined to form a new training condition, or the initialization sequence, loss function, or optimizer can have other values or combinations, and Table 1 does not limit the scheme of the present application in any way, and the present application does not limit the specific form of the training condition.

[0182] Table 1 uses multiple initialization sequences and multiple loss functions predefined by the protocol as an example for illustration. In addition, the protocol can also use the following three implementation methods:

[0183] As a first possible implementation, the protocol can define one initialization sequence and multiple loss functions. Each loss function and the initialization sequence can be a set of training conditions. For example, the training conditions defined by the protocol can be as shown in Table 2. Referring to Table 2, the protocol defines one initialization sequence as a ZC sequence, and further defines three loss functions.

[0184] Table 2

[0185] Optionally, since the three loss functions correspond to the same initialization sequence, the index in Table 2 can also be understood as the index of the loss function. In addition, Table 2 can not include the initialization sequence, but define the initialization sequence separately.

[0186] As a second possible implementation, the protocol can define one initialization sequence and one loss function, and the initialization sequence and the loss function are a set of training conditions. For example, the protocol defines the initialization sequence as a ZC sequence, and the loss function as wAPSL+(1-w)CPSL, or defines the initialization sequence as a Gold sequence, and the loss function as w1APSL+w2CPSL+w3PAPR, which is not limited.

[0187] As a third possible implementation, the protocol can define multiple initialization sequences and one loss function. Each initialization sequence and the loss function can be as a set of training conditions. For example, the protocol defines the training conditions as shown in Table 3. Referring to Table 3, the protocol defines one loss function as wAPSL+(1-w)CPSL, and in addition, defines three loss functions.

[0188] Table 3

[0189] Optionally, since the loss functions corresponding to the three initialization sequences are the same, the index in Table 3 can also be understood as the index of the initialization sequence. In addition, Table 3 can also not include the loss function, but define the loss function separately.

[0190] Optionally, the protocol can also predefine the expected value of each loss function, such as the expected value after optimization, for example, define the expected value of wAPSL+(1-w)CPSL. Alternatively, the protocol can predefine the expected value of at least one index of the loss function, for example, define the expected value of APSL as -13.5 dB, the expected value of CPSL as -20 dB, or the expected value of PAPR as 3 dB, etc.

[0191] For example, if the device cannot achieve the expected value no matter how it optimizes, it means that the device does not have the ability to generate an AI sequence; if the device can achieve the expected value after optimization, it means that the device has the ability to generate an AI sequence.

[0192] After a certain device determines whether it has the ability to generate an AI sequence, it can report the ability to the RAN or core network. The RAN or core network can determine whether to configure the training condition to the device based on the ability. For example, if the device does not have the ability to generate an AI sequence, it does not configure the training condition to the device, because even if the training condition is configured, the device cannot generate an AI sequence; if the device has the ability to generate an AI sequence, it can configure the training condition to the device. Optionally, if a certain device does not have the ability to generate an AI sequence, it can not be allowed to access the network.

[0193] In one possible implementation, the sequence set obtained by the above-mentioned optimization-based method can be a single-channel sequence set, that is, the sequence set includes M sequences, and each sequence has a length of L, for example, each sequence can be represented as [x0, x1, x2, …, xL-1]. L-1 ]。

[0194] Alternatively, the sequence set obtained by the above-mentioned optimization-based manner can also be a multi-channel sequence set, which includes at least one multi-channel sequence. The multi-channel sequence can be understood as a sequence group including a plurality of channel sequences, the plurality of channel sequences have the same length, and the total length of the multi-channel sequence is the sum of the lengths of the plurality of channel sequences. The number of the plurality of sequences included in the multi-channel sequence can be referred to as the number of channels of the multi-channel sequence. For example, a K-channel sequence with a length of L includes L×K elements, which can be represented as: [[x 0,0 ,x 1,0 ,x 2,0 ,…,x L-1,0 ],……,[x 0,k ,x 1,k ,x 2,k ,…,x L-1,k ],……,[x 0,K-1 ,x 1,K-1 ,x 2,K-1 ,…,x L-1,K-1 ]

[0195] The correlation / fuzziness result of the multi-channel sequence can be understood as the superposition of the independent correlation of each channel sequence in the multi-channel sequence, and the fuzzy sidelobes of the channel sequences can be offset to achieve better fuzziness characteristics than the single-channel sequence. The multi-channel sequence can also be understood as a sequence that needs to be segmented and independently correlated, and the number of channels can also be understood as the number of sequence segments when the sequence is correlated.

[0196] When optimizing the multi-channel sequence, the indicators APSL / CPSL / AISL / CISL of the above-mentioned loss function are obtained according to the fuzziness function of the multi-channel sequence, and the PAPR is obtained by considering the transmission of all channel sequences. The parameters of the above-mentioned loss function can also include the number of channels of the multi-channel sequence and the transmission interval between adjacent two channel sequences. It can be understood that the transmission duration of the multi-channel sequence is equal to the number of channels of the multi-channel sequence multiplied by the transmission interval between adjacent two channel sequences.

[0197] The communication method provided by the embodiments of the present application will be described below based on the above description. As shown in FIG. 5, the communication method can include the following steps:

[0198] S501, the second communication device determines the first training condition.

[0199] As a possible implementation, the second communication device can be a core network device. The core network device can be a network element that has been deployed in the current core network, such as an AMF network element, that is, the function implemented by the second communication device in the present application is added on the basis of the existing core network network element; or the core network device can be a new, independently deployed core network network element, for example, an SF network element, which is not specifically limited in the present application.

[0200] As another possible implementation, the second communication device can be a RAN node. At this time, the first training condition can be a training condition used when the RAN node generates the sequence set.

[0201] The first training condition includes a first initialization sequence and a first loss function. Optionally, the first training condition further includes a first optimizer.

[0202] As a possible implementation, the first initialization sequence can be one of a ZC sequence, a sequence obtained by extending a ZC sequence, a Gold sequence, or an M sequence. For details, refer to the above description of the initialization sequence, which will not be repeated here.

[0203] As a possible implementation, the first loss function is associated with at least one of the APSL, CPSL, AISL, CISL, or PAPR indicators. The parameters of the first loss function include at least one of the weights corresponding to each indicator, the constraint value, the Doppler frequency offset range, the time delay range, or the sequence transmission duration. For details, refer to the above description of the loss function, which will not be repeated here.

[0204] As a possible implementation, the first optimizer can be one of SGD, Adam, and RMSProp. The parameters of the first loss function include at least one of the learning rate, the learning rate decay value, or the momentum parameter. For details, refer to the above description of the optimizer, which will not be repeated here.

[0205] As a possible implementation, the first training condition is one of at least one predefined training condition. For example, the protocol can define at least one training condition, which includes the first training condition. For details of how the protocol defines the training condition, refer to the above description, which will not be repeated here.

[0206] As a possible implementation, the second communication device determines the first training condition, which can include the second communication device determining the first initialization sequence, the form of the first loss function, and the parameters of the first loss function. For example, the first initialization sequence is determined to be a ZC sequence, the form of the first loss function is wAPSL+(1-w)CPSL, and the parameter w of the first loss function is 0.4.

[0207] S502, the second communication device sends the first information to the first communication device. Correspondingly, the first communication device receives the first information from the second communication device.

[0208] As a possible implementation, the first communication device is a RAN node. In the case that the second communication device is also a RAN node, the first communication device and the second communication device are different RAN nodes.

[0209] The first information indicates the first training condition. The first training condition includes the first initialization sequence and the first loss function. Optionally, the first training condition further includes the first optimizer. The first training condition can refer to the related description in the above step S501, which will not be repeated here.

[0210] As a first possible implementation, in the case that the protocol defines multiple initialization sequences and multiple loss functions, the first information can include an index of the first training condition in the at least one training condition. At this time, it can be considered that the first information indicates the form of the first loss function. For example, taking the predefined at least one training condition as shown in Table 1 as an example, assuming that the first training condition is the training condition corresponding to index 0, the first information can include index 0, the form of the first loss function is wAPSL+(1-w)CPSL, and the parameter w of the first loss function can be predefined by the protocol or can be indicated by the second communication device.

[0211] Alternatively, the training condition can be associated with a service or a service scenario, such as that training condition 1 is associated with service scenario 1 and training condition 2 is associated with service scenario 2, that is, under service scenario 1, training condition 1 is used, and under service scenario 2, training condition 2 is used. At this time, the first information can include an identifier or an index of the service scenario associated with the first training condition.

[0212] As a second possible implementation, in the case that the protocol defines one initialization sequence and multiple loss functions, the first information can include an index of the first loss function in the multiple loss functions. Since the protocol only defines one initialization sequence, after determining the first loss function, the communication device in the network can determine that the first training condition includes the first loss function and the one initialization sequence predefined by the protocol, so that in this implementation, the first information can also be considered to indicate the first training condition. In addition, it can also be considered that the first information indicates the form of the first loss function, and the parameters of the first loss function can be predefined by the protocol or can be indicated by the second communication device.

[0213] As a third possible implementation, the protocol defines an initialization sequence as the first initialization sequence and a loss function as the first loss function, and in this case, the first information can include the parameters of the first loss function, or it can be considered that the first information indicates the parameters of the first loss function.

[0214] Alternatively, the protocol defines an initialization sequence and a loss function, and in the case where the parameters of the loss function are predefined, the steps S501 and S502 can not be performed. Based on the protocol definition, each communication device in the network generates a sequence set using the same fixed training condition.

[0215] As a fourth possible implementation, the protocol defines multiple initialization sequences and a loss function, and in this case, the first information can include the index of the first initialization sequence in the multiple initialization sequences. Since the protocol only defines one loss function, after determining the first initialization sequence, the communication device in the network can determine that the first training condition includes the first initialization sequence and the loss function predefined by the protocol, so in this implementation, the first information can also be considered to indicate the first training condition.

[0216] As a possible implementation, in the case where the first initialization sequence is a Gold sequence, the first information also indicates the initialization parameters of the Gold sequence. In the case where the first initialization sequence is an M sequence, the first information also indicates the initialization parameters of the M sequence.

[0217] As a possible implementation, the second communication device can send the first information to multiple first communication devices. For example, in the case where the second communication device is a core network device, the core network device can send the first information to multiple RAN nodes managed or served by it; in the case where the second communication device is a RAN node, the RAN node can send the first information to multiple RAN nodes adjacent to it.

[0218] As a possible implementation, in the above first, second or fourth possible implementation, the second communication device can also indicate the parameters of the first loss function to the first communication device. For example, the second communication device can indicate the parameters of the first loss function through the first information, that is, the first information also indicates the parameters of the first loss function; or the second communication device can send second information to the first communication device, the second information indicating the parameters of the first loss function, and correspondingly, the first communication device receives the second information from the second communication device to obtain the parameters of the first loss function.

[0219] For example, the second information and the first information can be carried in the same message, or they can be carried in different messages, which is not limited in the present application.

[0220] Exemplarily, in a case that the second communication device does not indicate the parameter of the first loss function to the first communication device, or indicates a partial parameter of the first loss function, the parameter of the first loss function which is not indicated can be predefined by a protocol.

[0221] As a possible implementation, the second communication device can further indicate an expected value of the first loss function to the first communication device, and / or, indicate an expected value of at least one index of the first loss function, the at least one index of the first loss function can include at least one of APSL, CPSL, AISL, CISL, or PAPR. For example, the second communication device can indicate the expected value by the first information; or the second communication device can send third information to the first communication device, indicate the expected value by the third information, and correspondingly, the first communication device can receive the third information from the second communication device to obtain the expected value.

[0222] Exemplarily, taking wAPSL+ (1-w) CPSL as an example of the first loss function, the second communication device can indicate an expected value of wAPSL+ (1-w) CPSL, and / or, can indicate an expected value of APSL and an expected value of CPSL.

[0223] Exemplarily, the third information and the first information can be carried in the same message, or can be carried in different messages, which is not limited in the present application.

[0224] As a possible implementation, the second communication device can further indicate a parameter of the first optimizer to the first communication device. For example, the second communication device can indicate the parameter of the first optimizer by the first information; or the second communication device can send fourth information to the first communication device, indicate the parameter of the first optimizer by the fourth information, and correspondingly, the first communication device can receive the fourth information from the second communication device to obtain the parameter of the first optimizer.

[0225] S503, the first communication device determines the first sequence set according to the first training condition.

[0226] As a possible implementation, the first communication device can take the first initialization sequence as an initial model parameter of the neural network / initial output of the neural network, and subsequently update the model parameter of the neural network based on the first loss function, or based on the first loss function and the first optimizer, so that the value of the first loss function generally presents a downward trend, or the value of the first loss function reaches an expected value, or at least one index of the first loss function reaches an expected value, and finally obtain the first sequence set. In this scenario, it can be considered that multiple communication device nodes generate sequence sets by using the same training condition.

[0227] Exemplarily, the parameters of the first loss function and / or the parameters of the first optimizer can be determined by an indication of the second communication device, such as determining the parameters of the first loss function and / or the parameters of the first optimizer by the first information, or determining the parameters of the first loss function by the second information and / or determining the parameters of the first optimizer by the fourth information. Alternatively, the parameters of the first loss function and / or the parameters of the first optimizer can be predefined by a protocol, which is not limited.

[0228] Exemplarily, the expected value of the first loss function and / or the expected value of at least one indicator of the first loss function can be indicated by the second communication device, such as being indicated by the second communication device through the first information or the third information, or the expected value can be predefined by a protocol, which is not limited.

[0229] Exemplarily, in the case that the first communication device cannot reach the expected value no matter how it optimizes, it means that the first communication device does not have the ability to generate the AI sequence and cannot generate the first sequence set. At this time, the first communication device can report to the second communication device that it does not have the ability to generate the AI sequence, and the second communication device can send the first sequence set to the first communication device based on the report of the first communication device.

[0230] It should be noted that the first communication device has the ability to generate the AI sequence and can generate the first sequence set in the embodiments of the present application.

[0231] As another possible implementation, the first communication device can determine another loss function (referred to as a third loss function) similar to the first loss function according to the first loss function in the first training condition, and then determine the first sequence set according to the first initialization sequence and the third loss function. In this scenario, it can be considered that multiple communication device nodes generate sequence sets using similar training conditions.

[0232] Exemplarily, the first communication device can take the first initialization sequence as the initial model parameter of the neural network / the initial output of the neural network, and subsequently update the model parameter of the neural network based on the third loss function or based on the third loss function and the first optimizer, so that the value of the third loss function generally presents a downward trend, or the value of the third loss function reaches an expected value, or at least one indicator of the third loss function reaches an expected value, and finally the first sequence set is obtained.

[0233] Wherein, the same training condition can refer to the same initialization sequence and loss function, and the similar training condition can refer to the same initialization sequence and similar loss function. Different devices can generate the same or similar sequence sets based on the same training condition, and can generate similar sequence sets based on the similar training condition.

[0234] For example, the loss functions can be understood as similar if the indexes for determining the loss functions are the same, but the forms and / or parameters of the loss functions are different. For example, the loss functions corresponding to index 1 and index 2 in Table 1 can be understood as similar loss functions.

[0235] As a possible implementation, in a case where the second communication device is a core network device, the core network device sends the first information to a plurality of first communication devices, and the plurality of first communication devices determine the sequence set according to the first training condition. The sequence sets determined by the plurality of first communication devices according to the first training condition are similar or identical. For example, the core network device indicates that the first initialization sequence in the first training condition is the same, and the first loss function is the same or similar.

[0236] For example, in a case where the core network device indicates the same first loss function to the plurality of first communication devices, the plurality of first communication devices can generate the sequence set by using the same training condition. In a case where the core network device indicates the similar first loss function to the plurality of first communication devices, the plurality of first communication devices generate the sequence set by using the similar training condition.

[0237] As another possible implementation, in a case where the second communication device is a RAN node, the second communication device also determines the sequence set according to the first training condition. The sequence set determined by the second communication device and the sequence set determined by the second communication device according to the first training condition are similar or identical.

[0238] For example, the second communication device and the first communication device can generate the sequence set by using the same training condition, such as the first training condition. Alternatively, the second communication device and the first communication device can generate the sequence set by using the similar training condition, such as the second communication device using the first training condition and the first communication device using the first initialization sequence and the third loss function, where the third loss function is similar to the first loss function.

[0239] S504, the first communication device sends and / or receives the reference signal according to the first sequence in the first sequence set. For example, the first sequence can be any sequence in the first sequence set.

[0240] As a possible implementation, in a communication or positioning scenario, the first communication device can send or receive the reference signal according to the first sequence in the first sequence set, for example, sending the DMRS or PRS to the terminal according to the first sequence, or receiving the SRS or the preamble in the PRACH from the terminal according to the first sequence.

[0241] As another possible implementation, in the sensing scenario, the first communication device can transmit and / or receive the reference signal according to the first sequence in the first sequence set. In the sensing scenario, the reference signal can also be referred to as a sensing signal. For example, in the self-transmitting and self-receiving mode, the first communication device transmits and receives the reference signal according to the first sequence; in the self-transmitting and other-receiving mode, the first communication device transmits the reference signal according to the first sequence, and the third communication device receives the reference signal according to the first sequence, or the third communication device transmits the reference signal according to the first sequence, and the first communication device receives the reference signal according to the first sequence. Exemplarily, the third communication device can be a terminal.

[0242] Exemplarily, the first communication device transmitting the reference signal according to the first sequence can be understood as that the first communication device generates the reference signal according to the first sequence, and transmits the reference signal. The first communication device receiving the reference signal according to the first sequence can be understood as that the first communication device performs correlation operation on the received reference signal according to the first sequence.

[0243] As a possible implementation, the first communication device transmitting and / or receiving the reference signal according to the first sequence can be understood as that the first communication device transmits or receives the reference signal according to the first sequence in the process of communicating with a certain terminal, or transmits and / or receives the reference signal according to the first sequence in one sensing process. In addition, the first communication device can communicate with different terminals according to different sequences in the first sequence set, or transmit and / or receive the reference signal according to different sequences in the first sequence set in different sensing processes.

[0244] In a possible implementation, in the communication or positioning scenario, or in the self-transmitting and other-receiving mode, the third communication device also needs to obtain a sequence similar or identical to the first sequence to transmit or receive the reference signal. Exemplarily, the third communication device can be a terminal. The first communication device can indicate the sequence similar or identical to the first sequence to the third communication device through the following three possible implementations:

[0245] As the first possible implementation, the first communication device transmits seventh information to the third communication device, where the seventh information indicates the first training condition. Optionally, the seventh information further indicates the training parameter associated with the first training condition. The training parameter includes the parameter of the loss function and / or the parameter of the optimizer, i.e., the training parameter associated with the first training condition includes the parameter of the first loss function and / or the parameter of the first optimizer.

[0246] In the possible implementation, after receiving the seventh information, the third communication apparatus can determine the second sequence set according to the first training condition, and transmit or receive the reference signal according to the sequence (for example, the sequence with the same index, hereinafter referred to as the second sequence) corresponding to the first sequence in the second sequence set. For example, the second sequence set is similar to or the same as the first sequence set, and the second sequence is similar to or the same as the first sequence.

[0247] As a second possible implementation, the first communication apparatus transmits the first sequence set and the index of the first sequence to the third communication apparatus. The third communication apparatus can determine the first sequence from the first sequence set according to the index of the first sequence, and transmit or receive the reference signal according to the first sequence.

[0248] As a third possible implementation, the first communication apparatus transmits the first sequence to the third communication apparatus. After receiving the first sequence, the third communication apparatus transmits or receives the reference signal according to the first sequence.

[0249] Based on the above scheme, the second communication apparatus can indicate a certain training condition to the first communication apparatus, so that the first communication apparatus can determine a sequence set according to the training condition, and transmit and / or receive the reference signal according to the sequence in the sequence set. The second communication apparatus can be a core network device or a RAN node, and the first communication apparatus can be a RAN node. In the case that the second communication apparatus is a core network device, it is considered that the core network device configures the training condition to the RAN node, and therefore the same or similar training condition can be configured to the adjacent RAN nodes. In the case that the second communication apparatus is a RAN node, it is considered that the training condition is exchanged between multiple RAN nodes, and therefore the same or similar training condition can be used by the multiple RAN nodes. Since the same or similar training condition can generate similar or the same sequence set, and the different sequences in the same sequence set have low mutual ambiguity sidelobes, the different sequences in the similar or the same sequence set also have low mutual ambiguity sidelobes, thereby reducing the interference between the terminals served by the multiple RAN nodes.

[0250] For example, the sequence set 1 and the sequence set 2 each include 30 sequences, each sequence has a length of 102, and the sequences are frequency domain constant modulus sequences. If the sequence set 1 and the sequence set 2 are generated based on the optimization mode but use different initialization sequences, as shown in (a) of FIG. 6, it is a phase comparison of the sequence m in the sequence set 1 and the sequence m in the sequence set 2, where the phase value of each sequence element ranges from 0 to 2π. As shown in (b) of FIG. 6, it is the phase difference of each sequence element of the sequence m in the sequence set 1 and the sequence m in the sequence set 2. As shown in FIG. 6, the phase of the sequence elements of the sequence m in the sequence set 1 and the sequence m in the sequence set 2 is quite different, the phase difference is relatively random, and the similarity of the two sequences is poor.

[0251] If sequence set 1 and sequence set 2 are generated based on optimization with the same initialization sequence, the phase of sequence m in sequence set 1 and the phase of sequence m in sequence set 2 are compared as shown in (a) of FIG. 7, where the phase of each sequence element ranges from 0 to 2π. The phase difference of each sequence element of sequence m in sequence set 1 and sequence m in sequence set 2 is shown in (b) of FIG. 7. As shown in FIG. 7, the phases of sequence elements of sequence m in sequence set 1 and sequence m in sequence set 2 are approximately equal, and the phase difference is concentrated around 0 or 2π, i.e., the similarity of the two sequences is good.

[0252] That is, the two sequence sets obtained based on the same initialization sequence are similar, and therefore the cross-correlation performance between the sequences corresponding to different sequence identifiers in the two sequence sets is good. For example, the cross-correlation performance between sequence 1 in sequence set 1 and sequence 2 in sequence set 2 is good. This is because the sequences in sequence set 1 have good cross-correlation performance, such as the cross-correlation performance between sequence 1 in sequence set 1 and sequence 2 in sequence set 1, and sequence 2 in sequence set 2 is similar or identical to sequence 2 in sequence set 1, so the cross-correlation performance between sequence 1 in sequence set 1 and sequence 2 in sequence set 2 is good.

[0253] For example, for the two sequence sets corresponding to FIG. 6, the cross-correlation peak sidelobe level between sequence 1 in sequence set 1 and sequence 2 in sequence set 2 is about -14.8 dB. For the two sequence sets corresponding to FIG. 7, the cross-correlation peak sidelobe level between sequence 1 in sequence set 1 and sequence 2 in sequence set 2 is about -17.5 dB, because for the two sequences corresponding to FIG. 7, sequence 1 and sequence 2 in sequence set 1 have a lower cross-correlation peak sidelobe, and sequence 2 in sequence set 2 is similar or identical to sequence 2 in sequence set 1.

[0254] In a possible implementation, the second communication device described above can be a core network device. For example, in step S501, the core network device can determine the first training condition in the following three ways.

[0255] In the first way, the core network device determines the first training condition according to the training conditions recommended by each RAN node.

[0256] In a possible implementation, in this way, the communication method provided by the application can further include the following steps: the first communication device sends the fifth information to the second communication device, and correspondingly, the second communication device receives the fifth information from the first communication device.

[0257] The fifth information indicates a second training condition. The second training condition is a training condition recommended by the first communication device. For example, the second training condition includes a second initialization sequence and / or a second loss function. Further, the second training condition also includes a second optimizer.

[0258] Optionally, the fifth information further indicates a training parameter associated with the second training condition. The training parameter associated with the second training condition includes a parameter of the second loss function and / or a parameter of the second optimizer. The parameter of the second loss function can refer to the description of the parameter of the loss function, and the parameter of the second optimizer can refer to the description of the parameter of the optimizer, which will not be repeated here.

[0259] For example, there can be multiple first communication devices sending the fifth information to the second communication device. The fifth information sent by a certain first communication device indicates the training condition and / or the training parameter recommended by the first communication device. The second communication device can determine the first training condition in combination with the training condition recommended by each first communication device. Further, the training parameter associated with the first training condition can also be determined in combination with the training parameter recommended by each first communication device.

[0260] That is, the second communication device can determine the first training condition according to the fifth information. Further, the training parameter associated with the first training condition can also be determined according to the training parameter associated with the second training condition indicated by the fifth information.

[0261] For example, the second communication device can determine the training condition recommended most frequently as the first training condition. For example, the first communication device #1, the first communication device #2 and the first communication device #3 recommend the second training condition #1, the second training condition #2 and the second training condition #1 to the second communication device respectively. Then, the second communication device determines the second training condition #1 as the first training condition. Further, the training parameter associated with the second training condition #1 can also be determined as the training parameter associated with the first training condition.

[0262] Optionally, in the case that there are multiple first communication devices sending the fifth information to the second communication device, the second communication device can send the first information to each of the multiple first communication devices to indicate the first training condition in the step S502. In addition, the second communication device can also send the eighth information to the first communication device. The eighth information can indicate the training parameter associated with the first training condition.

[0263] In a possible implementation, the second training condition indicated by the fifth information sent by the first communication device can be determined by the first communication device itself. Alternatively, the second training condition can be determined by the first communication device according to the recommendation of the terminal. For example, the at least one terminal can respectively send sixth information to the first communication device, and the sixth information sent by a certain terminal indicates the training condition and / or the training parameter recommended by the terminal. Correspondingly, the second communication device receives the sixth information from the at least one terminal, and determines the second training condition and / or the training parameter associated with the second training condition according to the sixth information from the at least one terminal. That is, the first communication device determines the second training condition and / or the training parameter associated with the second training condition according to the training condition and / or the training parameter reported by each terminal.

[0264] As a possible implementation, the training condition recommended by the terminal to the first communication device can include a loss function. The training parameter recommended by the terminal can include parameters of the loss function, such as a Doppler frequency offset range, a time delay range, and the like. For example, the parameters of the loss function can be determined according to at least one of the speed of the terminal, the speed of the target to be perceived, or the distance of the target to be perceived.

[0265] As a possible implementation, the first communication device can determine the training condition recommended most frequently as the second training condition, which can refer to the above description of the second communication device determining the first training condition, and details are not repeated here. Alternatively, the first communication device can determine the training condition recommended by the terminal with the largest moving speed as the second training condition, or determine the training parameter recommended by the terminal with the largest moving speed as the training parameter associated with the second training condition. The present application does not make specific limitations on the implementation of the first communication device determining the second training condition and / or the training parameter associated with the second training condition according to the sixth information.

[0266] Option 2: The core network device determines the training condition reported by the fourth communication device as the first training condition.

[0267] For example, the fourth communication device can be a RAN node. The fourth communication device and the first communication device can be different RAN nodes.

[0268] For example, the fourth communication device can send ninth information to the second communication device, where the ninth information indicates the first training condition, and further, the ninth information also indicates the training parameter associated with the first training condition. Correspondingly, the second communication device receives the ninth information from the fourth communication device, obtains the first training condition, and then sends the first information to the at least one first communication device to indicate the first training condition. Further, the second communication device also indicates the training parameter associated with the first training condition to the at least one first communication device.

[0269] That is, after receiving the training condition reported by a certain RAN node, the core network device can indicate the training condition to other RAN nodes, so that the other RAN nodes can generate a sequence set using the same training condition as the RAN node. Further, when the RAN node also reports the training parameter, the core network device can also indicate the training parameter to the other RAN nodes.

[0270] In a third mode, the core network device determines the first training condition by itself.

[0271] For example, in the third mode, the RAN node can not recommend the training condition to the core network device, or the core network device can not refer to any training condition recommended by the RAN node, but directly determine the first training condition according to the actual situation or the service scenario or the preset rule, and indicate the first training condition to the plurality of first communication devices.

[0272] In a possible implementation, in the embodiments of the present application, the information for indicating the training condition transmitted by each device can include an index of the training condition in at least one predefined training condition, that is, the training condition is indicated by the index of the training condition.

[0273] In the above method, the training conditions can be directly interacted between different RAN nodes or through the core network device, so as to ensure that different RAN nodes can generate a sequence set using the same training condition. In addition, the present application also provides a communication method, in which the terminal can recommend the training condition and / or the training parameter to the RAN node. As shown in FIG. 8, the communication method includes the following steps:

[0274] S801, the terminal sends first information to the RAN node. Correspondingly, the RAN node receives the first information from the terminal.

[0275] The first information indicates the training condition and / or the training parameter recommended / expected by the terminal. The training condition recommended by the terminal includes an initialization sequence and / or a loss function. For example, the terminal can recommend the initialization sequence, or can recommend the loss function, or can recommend the initialization sequence and the loss function. The training parameter recommended by the terminal can include parameters of the loss function, such as a Doppler frequency offset range, a time delay range, etc. For example, the parameters of the loss function can be determined according to at least one of the speed of the terminal, the speed of the target to be perceived, or the distance of the target to be perceived. The initialization sequence, the loss function, and the parameters of the loss function can refer to the related description above, and will not be described here.

[0276] Optionally, the training condition recommended by the terminal can also include an optimizer, that is, the terminal can also recommend the optimizer. Correspondingly, the training parameter recommended by the terminal can also include parameters of the optimizer. The optimizer and the parameters of the optimizer can refer to the related description above, and will not be described here.

[0277] S802, the RAN node determines, according to the first information, the first training condition and / or the training parameter associated with the first training condition.

[0278] The first training condition comprises a first initialization sequence and a first loss function. Optionally, the first training condition can further comprise a first optimizer.

[0279] The training parameter associated with the first training condition comprises a parameter of the first loss function. Optionally, the training parameter associated with the first training condition can further comprise a parameter of the first optimizer.

[0280] As a possible implementation, in the case that the terminal recommends the training condition, the RAN node determines the first training condition according to the first information; in the case that the terminal recommends the training parameter, the RAN node determines the training parameter associated with the first training condition according to the first information.

[0281] Illustratively, the first training condition determined by the RAN node and the training condition recommended by the terminal can be the same or different. Similarly, the training parameter associated with the first training condition determined by the RAN node and the training parameter recommended by the terminal can be the same or different.

[0282] As a possible implementation, the at least one terminal can respectively send the first information to the first communication device, and the first information sent by a certain terminal indicates the training condition and / or the training parameter recommended by the terminal. Correspondingly, the RAN node receives the first information from the at least one terminal, and determines the first training condition and / or the training parameter associated with the first training condition according to the first information from the at least one terminal. That is, the RAN node determines the first training condition and / or the training parameter associated with the first training condition by comprehensively determining the training condition and / or the training parameter reported by each terminal.

[0283] As a possible implementation, the RAN node can determine the training condition recommended most frequently as the first training condition, which can refer to the related description of the determination of the first training condition by the second communication device in the aforementioned manner one, and will not be described here. Alternatively, the RAN node can determine the training condition recommended by the terminal with the largest moving speed as the first training condition, or determine the training parameter recommended by the terminal with the largest moving speed as the training parameter associated with the first training condition.

[0284] As a possible implementation, the RAN node can further generate a sequence set according to the first training condition. For the convenience of description, the sequence set generated by the RAN node according to the first training condition will be referred to as the first sequence set in the following embodiments of the present application.

[0285] S803, the RAN node sends the second information to the terminal. Correspondingly, the terminal receives the second information from the RAN node.

[0286] As a first possible implementation, the second information indicates the first training condition and / or the training parameter associated with the first training condition. For example, the second information indicates the first training condition, and further indicates the training parameter associated with the first training condition. The implementation of the second information indicating the first training condition can refer to the related description of the first information indicating the first training condition in step S502, which will not be repeated here. Correspondingly, after receiving the second information, the terminal can determine the sequence set according to the second information, such as generating the sequence set (hereinafter referred to as the second sequence set) according to the first training condition. For details, please refer to the related description in step S503, which will not be repeated here.

[0287] As a second possible implementation, the second information can indicate the sequence set obtained by the RAN node according to the first training condition, i.e., the first sequence set. For example, the second information can include the sequence elements of each sequence in the first sequence set; or the second information can include the compressed first sequence set, and correspondingly, after receiving the second information, the terminal can use the corresponding decompression algorithm to decompress, so as to obtain the first sequence set.

[0288] As a third possible implementation, the second information can indicate the first sequence, which is one sequence in the sequence set obtained by the RAN node according to the first training condition, i.e., the first sequence is one sequence in the first sequence set.

[0289] In a possible implementation, after step S803, the RAN node and the terminal can send and / or receive the reference signal based on the sequence in the sequence set.

[0290] As a first possible implementation, in the case that the second information indicates the first training condition and / or the training parameter associated with the first training condition, the RAN node sends or receives the reference signal according to the first sequence, and correspondingly, the terminal receives or sends the reference signal according to the second sequence. The second sequence can be a sequence in the second sequence set that has the same index as the first sequence, for example, the first sequence is sequence 1 in the first sequence set, and the second sequence is sequence 2 in the second sequence set.

[0291] As a second possible implementation, in the case that the second information indicates the first sequence set, the RAN node sends or receives the reference signal according to the first sequence, and correspondingly, the terminal receives or sends the reference signal according to the second sequence. The second sequence can be a sequence in the second sequence set that has the same index as the first sequence.

[0292] Optionally, in the above two possible implementations, the RAN node also needs to indicate the index of the first sequence (or the second sequence) to the terminal, so that the terminal can determine the second sequence.

[0293] As a third possible implementation, in the case where the second information indicates the first sequence, the RAN node transmits or receives the reference signal according to the first sequence, and correspondingly, the terminal receives or transmits the reference signal according to the first sequence.

[0294] Wherein, the implementation of the RAN node, the terminal transmitting or receiving the reference signal can refer to the related description of the first communication device transmitting and / or receiving the reference signal according to the first sequence in the above step S504, which will not be repeated here.

[0295] Based on the above scheme, the terminal can report its recommended or expected training condition to the RAN node, so that the RAN node can reasonably select the training condition to generate the sequence set, thereby improving the adaptability of the sequence set, and further improving the performance in subsequent communication or sensing based on the sequence in the sequence set.

[0296] In addition, in the case where the RAN node indicates the training condition to the terminal, the terminal can generate the sequence set based on the training condition, so that the RAN node and the terminal can generate similar or identical sequence sets (such as the first sequence set and the second sequence set being identical or similar) based on the same or similar training condition, and further can perform subsequent sensing or communication based on the sequence set. On the one hand, since the first sequence set generated by the RAN node does not need to be transmitted to the terminal, the signaling overhead can be reduced. On the other hand, since the RAN node and the terminal can generate similar or identical sequence sets based on the same or similar training condition, they can use similar or identical sequences for sensing or communication, thereby reducing the signaling overhead while having little impact on the sensing performance or communication performance.

[0297] In a possible implementation, in the above embodiments of the present application, two sequence sets being similar can be understood as: the sequence with the same index or identifier in the two sequence sets is similar, and the correlation results of the two have obvious main lobes.

[0298] For example, based on the same training condition to generate the sequence set 1 and the sequence set 2, (a) of FIG. 9 is the autocorrelation result of the sequence 1 in the sequence set 1, as shown in (a) of FIG. 9, there is an obvious main lobe at the position with a time delay offset of 0, and the amplitudes of the remaining side lobes are all lower than that of the main lobe. (b) of FIG. 9 is the correlation result of the sequence 1 in the sequence set 2 and the sequence 1 in the sequence set 1, as shown in (b) of FIG. 9, the correlation results of the two also have an obvious main lobe at the position with a time delay offset of 0, that is, the sequence 1 in the sequence set 2 and the sequence 1 in the sequence set 1 are similar.

[0299] In a possible implementation, for the above method embodiments, in a CU-DU architecture or an ORAN system, the functions of the RAN node interacting with the terminal can be implemented by the DU or O-DU. The information sent by the RAN node to the terminal can be generated by the DU or O-DU, or can be generated by the CU or O-CU and sent to the DU or O-DU. The functions of the RAN node interacting with the core network can be implemented by the CU or O-CU. The processing functions of the RAN node can be implemented by the CU or O-CU, or can be implemented by the DU or O-DU, or can be jointly implemented by the CU and the DU (or the O-CU and the O-DU), without limitation.

[0300] For example, taking the method shown in FIG. 5 as an example, in the case of the second communication device being a core network device, the CU can receive the first information from the core network device, and then send the first information to the DU. The DU determines the first sequence set according to the first training condition indicated by the first information. Subsequently, the DU can generate a reference signal according to the first sequence in the first sequence set and send the reference signal to the RU in the form of a radio wave, or the RU can receive the reference signal in the form of a radio wave and then send the baseband reference signal to the DU, and the DU processes the reference signal according to the first sequence. The specific implementation of each step can be referred to the above related description, which will not be repeated here.

[0301] The above describes the method provided by the present application, and in addition, the present application also provides a communication device for implementing the functions described in the above method embodiments.

[0302] It can be understood that, in order to implement the above functions, the communication device comprises a hardware structure and / or a software module for executing each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the examples described in the embodiments disclosed herein, the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is driven by hardware or computer software to drive hardware depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0303] The embodiments of the present application can divide the functions of the communication device according to the above method embodiments, for example, each function module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be realized in the form of hardware or software function module. It should be noted that the division of modules in the embodiments of the present application is illustrative, and is only a logical function division. There can be another division way in actual implementation.

[0304] FIG. 10 shows a structural diagram of a communication apparatus 100. The communication apparatus 100 includes a processing module 1001 and a transceiver module 1002. The communication apparatus 100 can be used to implement the functions of the RAN node or the terminal or the core network device described above.

[0305] In some embodiments, the communication apparatus 100 can further include a storage module (not shown in FIG. 10) for storing program instructions and data.

[0306] In some embodiments, the transceiver module 1002, which can also be referred to as a transceiver unit, is configured to implement the transmitting and / or receiving functions. The transceiver module 1002 can be constituted by a transceiver circuit, a transceiver, a transceiver, or a communication interface.

[0307] In some embodiments, the transceiver module 1002 can include a receiving module and a transmitting module, which are respectively configured to perform the receiving and transmitting steps of the RAN node or the terminal or the core network device in the method embodiments described above, and / or other processes for supporting the technologies described herein; and the processing module 1001 can be configured to perform the processing steps of the RAN node or the terminal or the core network device in the method embodiments described above, and / or other processes for supporting the technologies described herein.

[0308] When the communication apparatus 100 is used to implement the functions of the first communication apparatus:

[0309] The transceiver module 1002 is configured to receive first information, the first information indicating a first training condition, the first training condition including a first initialization sequence and a first loss function; and the processing module 1001 is configured to determine a first sequence set according to the first training condition. The transceiver module 1002 is further configured to transmit and / or receive a reference signal according to a first sequence in the first sequence set.

[0310] Optionally, the transceiver module 1002 is further configured to receive second information, the second information indicating a parameter of the first loss function.

[0311] Optionally, the transceiver module 1002 is further configured to receive third information, the third information indicating an expected value of the first loss function, and / or indicating an expected value of at least one index of the first loss function.

[0312] Optionally, the first training condition further includes a first optimizer. The transceiver module 1002 is further configured to receive fourth information, the fourth information indicating a parameter of the first optimizer.

[0313] Optionally, the transceiver module 1002 is further configured to transmit fifth information, the fifth information indicating a second training condition, the second training condition being a recommended training condition for the first communication apparatus.

[0314] Optionally, the transceiver 1002 is further configured to receive sixth information, the sixth information indicating the training condition recommended by the terminal and / or the training parameter associated with the training condition; and the processing module 1001 is further configured to determine the second training condition and / or the training parameter associated with the second training condition according to the sixth information.

[0315] Optionally, the transceiver 1002 is further configured to send seventh information, the seventh information indicating the first training condition.

[0316] When the communication apparatus 100 is configured to implement the function of the second communication apparatus:

[0317] The processing module 1001 is configured to determine the first training condition, the first training condition including a first initialization sequence and a first loss function; and the transceiver 1002 is configured to send first information, the first information indicating the first training condition.

[0318] Optionally, the transceiver 1002 is further configured to send second information, the second information indicating a parameter of the first loss function.

[0319] Optionally, the transceiver 1002 is further configured to send third information, the third information indicating an expected value of the first loss function, and / or indicating an expected value of at least one index of the first loss function.

[0320] Optionally, the first training condition further includes a first optimizer; and the transceiver 1002 is further configured to send fourth information, the fourth information indicating a parameter of the first optimizer.

[0321] Optionally, the processing module 1001 is configured to determine the first training condition by: receiving, by the transceiver 1002, fifth information, the fifth information indicating a second training condition, the second training condition being a training condition recommended by the first communication apparatus; and determining, by the processing module 1001, the first training condition according to the fifth information.

[0322] Optionally, the fifth information further indicates a training parameter associated with the second training condition; the processing module 1001 is further configured to determine a training parameter associated with the first training condition according to the training parameter associated with the second training condition; and the transceiver 1002 is further configured to send eighth information, the eighth information indicating the training parameter associated with the first training condition.

[0323] Optionally, the processing module 1001 is configured to determine the first training condition by: receiving, by the transceiver 1002, ninth information, the ninth information indicating the first training condition.

[0324] When the communication apparatus is configured to implement the function of the RAN node:

[0325] The transceiver module 1002 is configured to receive first information, the first information indicating a terminal-recommended training condition and / or a terminal-recommended training parameter, the terminal-recommended training condition including an initialization sequence and / or a loss function, and the terminal-recommended training parameter including a parameter of the loss function; the processing module 1001 is configured to determine, according to the first information, a first training condition and / or a training parameter associated with the first training condition, the first training condition including a first initialization sequence and a first loss function; and the transceiver module 1002 is further configured to send second information. The second information indicates the first training condition and / or the training parameter associated with the first training condition, or the second information indicates a sequence set obtained according to the first training condition, or the second information indicates a first sequence, the first sequence being one sequence in the sequence set obtained according to the first training condition.

[0326] When the communication apparatus is used to implement the function of the terminal, the communication apparatus includes:

[0327] The transceiver module 1002 is configured to send first information, the first information indicating a terminal-recommended training condition and / or a terminal-recommended training parameter, the terminal-recommended training condition including an initialization sequence and / or a loss function, and the terminal-recommended training parameter including a parameter of the loss function; and the transceiver module 1002 is further configured to receive second information. The second information indicates the first training condition and / or the training parameter associated with the first training condition, or the second information indicates a sequence set obtained according to the first training condition, or the second information indicates a first sequence, the first sequence being one sequence in the sequence set obtained according to the first training condition; and the first training condition includes a first initialization sequence and a first loss function.

[0328] All related contents of each step involved in the method embodiments described above can be referred to the function description of the corresponding functional module, and will not be repeated here.

[0329] In the present application, the communication apparatus 100 can be in the form of an integrated manner to present various functional modules. The "module" here can refer to a specific application-specific integrated circuit (ASIC), a circuit, a processor and a memory executing one or more software or firmware programs, an integrated logic circuit, and / or other devices that can provide the above functions.

[0330] In some embodiments, when the communication apparatus 100 in FIG. 10 is a chip or a chip system, the function / implementation process of the transceiver module 1002 can be implemented through the input / output interface (or communication interface) of the chip or chip system, and the function / implementation process of the processing module 1001 can be implemented through the processor (or processing circuit) of the chip or chip system.

[0331] Since the communication apparatus 100 provided by the embodiment can execute the method, the technical effects obtained by the communication apparatus 100 can refer to the method, which will not be repeated here.

[0332] As a possible product form, the RAN node or the terminal or the core network device described in the embodiments of the present application can be implemented using one or more field programmable gate arrays (FPGA), programmable logic devices (PLD), controllers, state machines, gate logic, discrete hardware components, any other suitable circuitry, or any combination thereof capable of performing the various functions described throughout the present application.

[0333] As another possible product form, the RAN node or the terminal or the core network device described in the embodiments of the present application can be implemented by a general bus architecture. For ease of illustration, refer to FIG. 11, which is a structural schematic diagram of a communication apparatus 1100 provided by an embodiment of the present application, the communication apparatus 1100 including a processor 1101 and a transceiver 1102. The communication apparatus 1100 can be a RAN node, or a chip or chip system therein; or the communication apparatus 1100 can be a terminal, or a chip or module therein; or the communication apparatus 1100 can be a core network device, or a chip or chip module therein. FIG. 11 only shows the main components of the communication apparatus 1100. In addition to the processor 1101 and the transceiver 1102, the communication apparatus can further include a memory 1103 and an input output device (not shown in FIG. 11).

[0334] Optionally, the processor 1101 is mainly used for processing communication protocols and communication data, and controlling the whole communication apparatus, executing software programs, processing data of the software programs, so as to implement the method provided in the method embodiments. The memory 1103 is mainly used for storing software programs and data. The transceiver 1102 can include radio frequency circuitry and an antenna, the radio frequency circuitry is mainly used for conversion between baseband signals and radio frequency signals and processing of the radio frequency signals. The antenna is mainly used for transceiving radio frequency signals in the form of electromagnetic waves. The input output device, such as touch screen, display screen, keyboard, etc., is mainly used for receiving user input data and outputting data to the user.

[0335] Optionally, the processor 1101, the transceiver 1102, and the memory 1103 can be connected through a communication bus.

[0336] When the communication apparatus is powered on, the processor 1101 can read the software program in the memory 1103, execute the instructions of the software program, and process the data of the software program. When data needs to be transmitted wirelessly, the processor 1101 performs baseband processing on the data to be transmitted, and outputs the baseband signal to the radio frequency circuit. The radio frequency circuit performs radio frequency processing on the baseband signal, and transmits the radio frequency signal in the form of electromagnetic wave through the antenna. When data is transmitted to the communication apparatus, the radio frequency circuit receives the radio frequency signal through the antenna, converts the radio frequency signal into a baseband signal, and outputs the baseband signal to the processor 1101. The processor 1101 converts the baseband signal into data and processes the data.

[0337] In another implementation, the radio frequency circuit and the antenna can be arranged independently of the processor performing the baseband processing, for example, in a distributed scenario, the radio frequency circuit and the antenna can be arranged remotely from the communication apparatus.

[0338] In some embodiments, in the hardware implementation, those skilled in the art can conceive that the above-mentioned communication apparatus 100 can adopt the form of the communication apparatus 1100 shown in FIG. 11.

[0339] As an example, the functions / implementation processes of the processing module 1001 in FIG. 10 can be realized by the processor 1101 in the communication apparatus 1100 in FIG. 11 invoking the computer execution instructions stored in the memory 1103. The functions / implementation processes of the transceiver module 1002 in FIG. 10 can be realized by the transceiver 1102 in the communication apparatus 1100 in FIG. 11.

[0340] As another possible product form, the RAN node or the terminal or the core network device in the present application can adopt the constituent structure shown in FIG. 12, or include the components shown in FIG. 12. FIG. 12 is a constituent diagram of a communication apparatus 1200 provided in the present application. The communication apparatus 1200 can be a RAN node or a chip or a system on chip in the RAN node; or can be a terminal or a chip or a system on chip in the terminal; or can be a core network device or a chip or a system on chip in the core network device.

[0341] As shown in FIG. 12, the communication apparatus 1200 includes at least one processor 1201, and at least one communication interface (only one communication interface 1204 is shown in FIG. 12 as an example, and the processor 1201 is taken as an example for description). Optionally, the communication apparatus 1200 can further include a communication bus 1202 and a memory 1203.

[0342] The processor 1201 can be a general-purpose central processing unit (CPU), a general-purpose processor, a network processing unit (NP), a digital signal processing (DSP), a microprocessor (such as X86, ARM), a microcontroller, an FPGA, a PLD, a state machine, a gate logic, a discrete hardware circuit, other suitable hardware configured to perform various functions, or any combination thereof. The processor 1201 can also be other apparatuses with processing capabilities, such as a circuit, a device, or a software module, without limitation.

[0343] The communication bus 1202 is used to connect different components in the communication apparatus 1200, so that different components can communicate. The communication bus 1202 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is shown in FIG. 12, but it does not mean that there is only one bus or one type of bus. For example, the communication bus 1202 can include any number of interconnected buses and bridges, depending on the specific application of the communication apparatus and the overall design constraints. In addition, the communication bus 1202 can also link various other circuits, such as timing sources, peripherals, voltage regulators, and power management circuits, etc.

[0344] The communication interface 1204 is used to communicate with other devices or communication networks. For example, the communication interface 1204 can be a module, a circuit, or any device capable of realizing communication.

[0345] As a possible implementation, the communication interface 1204 can also be an input / output interface located in the processor 1201, used to realize the signal input and signal output of the processor.

[0346] As another possible implementation, the communication interface 1204 can also be understood as a bus interface. It is used to provide an interface between the communication bus and the transceiver. The transceiver can provide an interface or device for communicating with various other devices through wireless / wired transmission media. The transceiver can be coupled to an antenna array, and the transceiver and the antenna array can be used together to communicate with a network of a corresponding type.

[0347] The memory 1203 can be a device having a storage function, configured to store instructions and / or data. The instructions can be a computer program. For example, the memory 1203 can be a read-only memory (ROM) or another type of static storage device that can store static information and / or instructions, or a random access memory (RAM) or another type of dynamic storage device that can store information and / or instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or another optical disk storage, an optical disk storage (including a compact disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or another magnetic storage device, etc., without limitation.

[0348] It should be noted that the memory 1203 can be independent of the processor 1201, or can be integrated with the processor 1201. The memory 1203 can be located in the communication device 1200, or can be located outside the communication device 1200, without limitation.

[0349] The processor 1201 can be configured to execute instructions stored in the memory 1203, or to execute a computer program or instructions stored in a computer-readable storage medium, to implement the method provided in the above embodiments of the present application.

[0350] Optionally, the processor 1201 and / or the memory 1203 can include an artificial intelligence (AI) module, which is configured to implement AI-related functions. The AI module can be implemented in a software, hardware, or software-hardware combined manner. For example, the AI module can include a radio network intelligent controller (RIC) module. For example, the AI module can be a near-real-time RIC or a non-real-time RIC.

[0351] As an optional implementation, the communication apparatus 1200 can further include an output device 1205 and an input device 1206. The output device 1205 communicates with the processor 1201 and can display information in various manners. For example, the output device 1205 can be a liquid crystal display (LCD), a light emitting diode (LED) display device, a cathode ray tube (CRT) display device, a projector, or the like. The input device 1206 communicates with the processor 1201 and can receive input of a user in various manners. For example, the input device 1206 can be a mouse, a keyboard, a touch screen device, a sensing device, or the like.

[0352] In some embodiments, on the hardware implementation, those skilled in the art can conceive that the communication apparatus 100 shown in FIG. 10 can take the form of the communication apparatus 1200 shown in FIG. 12.

[0353] As an example, the functions / implementation processes of the processing module 1001 in FIG. 10 can be implemented by the processor 1201 in the communication apparatus 1200 shown in FIG. 12 invoking computer-executable instructions stored in the memory 1203. The functions / implementation processes of the transceiver module 1002 in FIG. 10 can be implemented by the communication interface 1204 in the communication apparatus 1200 shown in FIG. 12.

[0354] It should be noted that the structure shown in FIG. 12 does not constitute a specific limitation on the RAN node or the terminal or the core network device. For example, in some other embodiments of the present application, the RAN node or the terminal or the core network device can include more or fewer components than those shown, or combine certain components, or split certain components, or different arrangement of components. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0355] In some embodiments, the embodiments of the present application also provide a communication apparatus including a processor for implementing the method in any of the above method embodiments.

[0356] As a possible implementation, the communication apparatus further includes a memory. The memory is used to save necessary computer programs and data. The computer programs can include instructions, and the processor can invoke the instructions in the computer programs stored in the memory to instruct the communication apparatus to perform the method in any of the above method embodiments. Of course, the memory can also not be in the communication apparatus.

[0357] As another possible implementation, the communication apparatus further includes an interface circuit, which is a code / data read-write interface circuit, configured to receive computer execution instructions (the computer execution instructions are stored in the memory, and can be read directly from the memory or can pass through other devices) and transmit to the processor.

[0358] As still another possible implementation, the communication apparatus further includes a communication interface, configured to communicate with a module outside the communication apparatus.

[0359] It can be understood that the communication apparatus can be a chip or a chip system, when the communication apparatus is a chip system, the communication apparatus can be composed of a chip, or can include a chip and other discrete devices, and embodiments of the present application do not make specific limitations.

[0360] The present application also provides a computer readable storage medium, which stores a computer program or instructions, and the computer program or instructions realize the functions of any of the above method embodiments when executed by a computer.

[0361] The present application also provides a computer program product, which realizes the functions of any of the above method embodiments when executed by a computer.

[0362] Those skilled in the art can understand that, for the convenience and brevity of description, the specific working processes of the above-described system, apparatus and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0363] It can be understood that the system, apparatus and method described in the present application can also be implemented in other ways. For example, the apparatus embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0364] The units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on a plurality of network units. The components shown as units can or can not be physical units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0365] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0366] In the embodiments described above, all or some of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or some of the embodiments can be implemented in the form of a computer program product storing computer program instructions. The computer program instructions are executed in a computer to implement the procedures or functions described in the embodiments of the present application. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer program instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (for example, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or data storage device including one or more servers, data centers, etc. integrated with the medium. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, solid state disk (SSD)), etc. In the embodiments of the present application, the computer can include the device described above.

[0367] Although the present application is described herein in conjunction with various embodiments, other variations of the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from the appended claims, the disclosure and the accompanying drawings. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality. A single processor or other unit can fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0368] Although the present application has been described in connection with the preferred embodiments thereof with reference to the specific content thereof, it will be apparent to those skilled in the art that various modifications and changes can be made thereto without departing from the scope of the present application. Accordingly, the description and drawings are to be regarded as illustrative in nature and are not to be taken as limiting the scope of the present application as defined by the appended claims. Obviously many modifications and changes can be made in the application without departing from the scope thereof. It is understood that the application is not to be limited to the specific examples set forth as examples, but that these examples are intended to cover all modifications and variations of this application.

Claims

1. A communication method characterized by comprising: The method comprises: receiving first information, the first information indicating a first training condition, the first training condition comprising a first initialization sequence and a first loss function; determining a first sequence set according to the first training condition; sending and / or receiving a reference signal according to a first sequence in the first sequence set.

2. The method of claim 1, wherein, The first training condition is one of at least one predefined training condition.

3. The method according to claim 1 or 2, characterized in that, The first initialization sequence is one of a ZC sequence, a sequence obtained by extending a ZC sequence, a Gold sequence, or an M sequence.

4. The method of claim 3, wherein, The first initialization sequence is a Gold sequence, and the first information further indicates an initialization parameter of the Gold sequence; or The first initialization sequence is an M sequence, and the first information further indicates an initialization parameter of the M sequence.

5. The method according to any one of claims 1 to 4, characterized in that, The first loss function is related to at least one of the following indicators: a self-fuzzing peak side lobe level APSL, a cross-fuzzing peak side lobe level CPSL, a self-fuzzing integrated side lobe level AISL, a cross-fuzzing integrated side lobe level CISL, or a peak-to-average power ratio PAPR.

6. The method of claim 5, wherein, Parameters of the first loss function include at least one of the following corresponding to the at least one indicator: a weight, a constraint value, a Doppler frequency offset range, a time delay range, or a sequence sending duration.

7. The method of claim 6, wherein, The first information further indicates the parameters of the first loss function; or The method further comprises: receiving second information, the second information indicating the parameters of the first loss function.

8. The method according to any one of claims 1 to 7, characterized in that, The method further comprises: receiving third information, the third information indicating an expected value of the first loss function, and / or indicating an expected value of at least one indicator of the first loss function.

9. The method according to any one of claims 1 to 8, characterized in that, The first training condition further comprises a first optimizer.

10. The method of claim 9, wherein, Parameters of the first optimizer include at least one of the following: a learning rate, a learning rate decay value, or a momentum parameter.

11. The method according to claim 9 or 10, characterized in that, The first information further indicates the parameters of the first optimizer; or The method further comprises: receiving fourth information, the fourth information indicating the parameters of the first optimizer.

12. The method according to any one of claims 1 to 11, characterized in that, The method further comprises: sending fifth information, the fifth information indicating a second training condition, the second training condition being a training condition recommended by a first communication device.

13. The method of claim 12, wherein, The fifth information further indicates training parameters associated with the second training condition; the training parameters associated with the second training condition include parameters of a second loss function and / or parameters of a second optimizer, the second loss function being a loss function included in the second training condition, and the second optimizer being an optimizer included in the second training condition.

14. The method according to claim 12 or 13, characterized in that, The method further comprises: receiving sixth information, the sixth information indicating a training condition and / or training parameters recommended by a terminal; determining the second training condition and / or the training parameters associated with the second training condition according to the sixth information.

15. The method according to any one of claims 1 to 14, characterized in that, The method further comprises: sending seventh information, the seventh information indicating the first training condition.

16. The method of claim 15, wherein, The seventh information further indicates training parameters associated with the first training condition.

17. A method of communication, comprising: The method comprises: determining a first training condition, the first training condition comprising a first initialization sequence and a first loss function; sending first information, the first information indicating the first training condition.

18. The method of claim 17, wherein, The first information further indicates a parameter of the first loss function; or The method further includes: sending second information, the second information indicating a parameter of the first loss function.

19. The method of claim 17 or 18, wherein, The method further includes: sending third information, the third information indicating an expected value of the first loss function, and / or indicating an expected value of at least one index of the first loss function.

20. The method according to any one of claims 17-19, characterized by, The first training condition further includes a first optimizer; The first information further indicates a parameter of the first optimizer; or The method further includes: sending fourth information, the fourth information indicating a parameter of the first optimizer.

21. The method according to any one of claims 17-20, characterized by, The determination of the first training condition includes: receiving fifth information, the fifth information indicating a second training condition, the second training condition being a training condition recommended by a first communication device; determining the first training condition according to the fifth information.

22. The method of claim 21, wherein, The fifth information further indicates a training parameter associated with the second training condition; the method further includes: determining a training parameter associated with the first training condition according to the training parameter associated with the second training condition; sending eighth information, the eighth information indicating the training parameter associated with the first training condition.

23. The method according to any one of claims 17-20, characterized by, The determination of the first training condition includes: receiving ninth information, the ninth information indicating the first training condition.

24. A method of communication, comprising: The method includes: receiving first information, the first information indicating a training condition and / or a training parameter recommended by a terminal, the training condition recommended by the terminal including an initialization sequence and / or a loss function, the training parameter recommended by the terminal including a parameter of the loss function; determining a first training condition and / or a training parameter associated with the first training condition according to the first information, the first training condition including a first initialization sequence and a first loss function; sending second information; wherein the second information indicates the first training condition and / or the training parameter associated with the first training condition, or the second information indicates a sequence set obtained according to the first training condition, or the second information indicates a first sequence, the first sequence being one sequence in the sequence set obtained according to the first training condition.

25. A method of communication, comprising: The method includes: sending first information, the first information indicating a training condition and / or a training parameter recommended by a terminal, the training condition recommended by the terminal including an initialization sequence and / or a loss function, the training parameter recommended by the terminal including a parameter of the loss function; receiving second information; wherein the second information indicates a first training condition and / or a training parameter associated with the first training condition, or the second information indicates a sequence set obtained according to the first training condition, or the second information indicates a first sequence, the first sequence being one sequence in the sequence set obtained according to the first training condition; the first training condition including a first initialization sequence and a first loss function.

26. The method of claim 24 or 25, wherein, The training condition recommended by the terminal and the first training condition are the same or different; or The training parameter recommended by the terminal and the training parameter associated with the first training condition are the same or different.

27. A communications device, characterized by The communication device comprises a processor; the processor is configured to run computer programs or instructions, so that the communication device performs the method according to any one of claims 1-16, or so that the communication device performs the method according to any one of claims 17-23, or so that the communication device performs the method according to any one of claims 24-26.

28. A computer-readable storage medium, characterized in that, A computer readable storage medium stores computer instructions or programs, when the computer instructions or programs are run on a computer, so that the method according to any one of claims 1-16 is performed, or so that the method according to any one of claims 17-23 is performed, or so that the method according to any one of claims 24-26 is performed.

29. A computer program product, characterised in that, The computer program product comprises computer instructions; when part or all of the computer instructions are run on a computer, so that the method according to any one of claims 1-16 is performed, or so that the method according to any one of claims 17-23 is performed, or so that the method according to any one of claims 24-26 is performed.

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