Communication method and related apparatus

Abstract

Provided in the present application are a communication method and a related apparatus. The method comprises: a terminal device receiving information of a function, wherein the function is used for fitting power distribution coefficients corresponding to a plurality of resource elements (REs), the function is a polynomial function, and the information of the function may comprise a coefficient corresponding to the function; and on the basis of a sub-carrier index and the function, determining a power distribution coefficient corresponding to each RE. By means of the technical solution provided in the present application, the overheads of air interface transmission can be reduced.

Description

Communication method and related apparatus

[0001] This application claims priority to the Chinese patent application No. 202410825946.5, filed on June 21, 2024, with the State Intellectual Property Office of China, and entitled "Communication method and related apparatus", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of wireless communication, in particular to a communication method and related apparatus. BACKGROUND

[0003] In a wireless communication system, a reference signal can be superimposed on data for transmission, for example, to implement that each resource element (RE) has both a reference signal and data, so as to save resources and support more flow data transmission. However, the superposition of the two needs to be based on a certain power allocation coefficient. Since the reference signal and the data are transmitted on the same time-frequency resource, the receiving end (counterpart) needs to know the power allocation coefficient when performing channel estimation, so the transmitting end needs to transmit the power allocation coefficient to the counterpart.

[0004] In a possible implementation, all RE corresponding power allocation coefficients are informed to the counterpart through signaling such as downlink control information (DCI), but this will result in a large air interface transmission overhead. Therefore, how to reduce the air interface transmission overhead is a problem to be solved. SUMMARY

[0005] Embodiments of the present application provide a communication method and related apparatus, which can reduce the air interface transmission overhead.

[0006] In a first aspect, the present application provides a communication method, which can be applied to a terminal device, can be applied to an apparatus (for example, a chip, or a chip system, or a circuit) in the terminal device, or is an apparatus that can be matched with the terminal device. Hereinafter, the application in the terminal device is taken as an example for description. The method can include: receiving, by the terminal device, information of a function, the function being used to fit power allocation coefficients corresponding to a plurality of REs, the function being a polynomial function, and the information of the function including coefficients corresponding to the function; and determining, by the terminal device, the power allocation coefficient corresponding to each RE according to a subcarrier index and the function.

[0007] In the scheme provided by the present application, the power allocation coefficients corresponding to a plurality of REs can be fitted by a function, the information of the fitted function is only transmitted on the air interface, and then the terminal device can determine the power allocation coefficient corresponding to each RE according to the function, without directly transmitting a plurality of power allocation coefficients on the air interface, so as to reduce the air interface transmission overhead.

[0008] In a possible implementation, the information of the function further includes a type of the function; and the information of the function further includes a segmented interval if the type of the function is a segmented function. By this embodiment, fitting the power distribution coefficients corresponding to each RE based on a segmented manner can improve the fitting effect of the function of the fitted power distribution coefficients, so that the fitted power distribution coefficients are more accurate.

[0009] In a possible implementation, the information of the function further includes an order corresponding to the function.

[0010] In a possible implementation, the method further includes: determining the order corresponding to the function according to the number of the coefficients corresponding to the function.

[0011] In a possible implementation, the information of the function includes information of the function corresponding to non-0 elements, and the information of the function further includes a subcarrier index corresponding to a 0 element and / or an approximate 0 element. By this embodiment, the 0 element and / or the approximate 0 element in the power distribution coefficients corresponding to multiple REs can be separated out based on separately notifying the subcarrier index corresponding to the 0 element and / or the approximate 0 element to the opposite end, and the power distribution coefficients of the remaining non-0 elements are fitted, so that the difficulty of fitting can be reduced and the accuracy of fitting can be improved.

[0012] In a possible implementation, the function includes a first function and a second function, the first function is used for fitting the power distribution coefficients corresponding to multiple REs corresponding to column index X, and the second function is used for fitting a difference value between column index X-Y and column index X and a difference value between column index X and column index X+Z, Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14. By this embodiment, a notification manner that different power distribution coefficients exist on different OFDM symbols can be implemented, and function fitting and function transmission of the power distribution coefficients existing in two dimensions (subcarriers and OFDM symbols) can be implemented based on multiple functions.

[0013] In a second aspect, the present application provides a communication method, which can be applied to a network device, can be applied to a device (for example, a chip, or a chip system, or a circuit) in the network device, or is a device capable of matching the network device, and is described below by taking the application to the network device as an example. The method can include: a network device sends information of a function, the function is used for fitting power distribution coefficients corresponding to multiple REs, the function is a polynomial function, and the information of the function includes coefficients corresponding to the function.

[0014] In the scheme provided in the present application, the network device can fit the power allocation coefficients corresponding to the plurality of REs by the function, implement transmission of information of the fitting function only in the air interface, and then the terminal device can determine the power allocation coefficients corresponding to each RE according to the function, without directly transmitting the plurality of power allocation coefficients in the air interface, so as to reduce the air interface transmission overhead.

[0015] It should be understood that the execution subject of the second aspect can be the network device, the specific content of the second aspect corresponds to the content of the first aspect, and the corresponding features and beneficial effects of the second aspect can refer to the description of the first aspect. To avoid repetition, the detailed description is appropriately omitted here.

[0016] In a possible implementation, the information of the function further includes a type of the function; and if the type of the function is a segmented function, the information of the function further includes a segmented interval.

[0017] In a possible implementation, the information of the function further includes an order corresponding to the function.

[0018] In a possible implementation, the information of the function includes information of the function corresponding to a non-0 element, and the information of the function further includes a subcarrier index corresponding to a 0 element and / or an approximately 0 element.

[0019] In a possible implementation, the function includes a first function and a second function, the first function is used for fitting the power allocation coefficients corresponding to the plurality of REs corresponding to the column index X, and the second function is used for fitting a difference value between the column index X-Y and the column index X and between the column index X and the column index X+Z, Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14.

[0020] In the third aspect, the present application provides a communication method, which can be applied to a terminal device, can be applied to an apparatus (for example, a chip, or a chip system, or a circuit) in the terminal device, or is an apparatus capable of matching use with the terminal device. Hereinafter, the method is described by taking the application to the terminal device as an example. The method can include: receiving, by the terminal device, a first vector including a pattern of compressed power allocation coefficients corresponding to one or more REs; and decompressing the first vector to obtain the power allocation coefficients corresponding to the one or more REs.

[0021] In the scheme provided in the present application, different from directly transmitting the power allocation coefficients, a pattern of compressed power allocation coefficients transmitted by the opposite terminal can be received, and the pattern of compressed power allocation coefficients is decompressed to obtain the power allocation coefficients corresponding to the one or more REs, so as to reduce the air interface transmission overhead.

[0022] In a possible implementation, the pattern of the power allocation coefficients corresponding to the one or more REs is compressed by an artificial intelligence (AI) encoder, and decompressing the first vector to obtain the power allocation coefficients corresponding to the one or more REs includes: decompressing the first vector by an AI decoder to obtain the power allocation coefficients corresponding to the one or more REs.

[0023] In a possible implementation, the AI encoder and the AI decoder are trained in pairs, and a loss function used in the training is one or more of a function related to a reconstruction loss, the function related to the reconstruction loss including a normalized mean squared error (NMSE), a mean squared error (MSE), a cosine similarity (CS), a general cosine similarity (GCS), or a squared general cosine similarity (SGCS).

[0024] In a fourth aspect, a communication method is provided. The method can be applied to a network device, and can also be applied to an apparatus (for example, a chip or a chip system or a circuit) in the network device, or an apparatus that can be used in combination with the network device. The method is described below by taking application to the network device as an example. The method can include: compressing, by the network device, a pattern of power allocation coefficients corresponding to one or more REs; and transmitting a first vector, the first vector including the compressed pattern of the power allocation coefficients corresponding to the one or more REs.

[0025] In the scheme provided in this application, unlike directly transmitting the power allocation coefficients, the pattern of the power allocation coefficients corresponding to the one or more REs can be compressed, and the compressed pattern is transmitted to a peer, so that air interface transmission overhead can be reduced.

[0026] It should be understood that the execution subject of the fourth aspect can be the network device, and the specific content of the fourth aspect corresponds to the content of the third aspect. The corresponding features of the fourth aspect and the beneficial effects achieved can refer to the description of the third aspect. To avoid repetition, the detailed description is appropriately omitted here.

[0027] In a possible implementation, the first vector is decompressed by the AI decoder, and the pattern of the power allocation coefficients corresponding to the one or more REs is compressed by an AI encoder.

[0028] In a possible implementation, the AI encoder and the AI decoder are trained in pairs, and a loss function used in the training is a function related to a reconstruction loss, and the related function includes one or more of NMSE, MSE, CS, GCS, or SGCS.

[0029] In a fifth aspect, an embodiment of the present application provides a communication apparatus, which can be a terminal device, a device (for example, a chip, or a chip system, or a circuit) in the terminal device, and can also be applied to a logic module or software capable of realizing all or part of the terminal device function. The communication apparatus includes a module / unit for executing the method in any of the first aspect, the third aspect, and possible implementation manners thereof. The functions can be realized by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions. For details, refer to the description of the first aspect and the third aspect, which will not be repeated here.

[0030] In a sixth aspect, an embodiment of the present application provides a communication apparatus, which can be a network device, a device (for example, a chip, or a chip system, or a circuit) in the network device, and can also be applied to a logic module or software capable of realizing all or part of the network device function. The communication apparatus includes a module, unit, or means for executing the method in any of the second aspect, the fourth aspect, and possible implementation manners thereof. The functions can be realized by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions. For details, refer to the description of the second aspect and the fourth aspect, which will not be repeated here.

[0031] In a seventh aspect, a communication apparatus is provided, which can be a terminal device, or a device (for example, a chip, or a chip system, or a circuit) in the terminal device. The apparatus can include a processor, and can also include a memory, an input interface, and an output interface, the input interface being configured to receive information from other communication apparatuses outside the communication apparatus, the output interface being configured to output information to other communication apparatuses outside the communication apparatus, and the processor being configured to invoke a computer program stored in the memory to execute the communication method provided in the first aspect or any of the implementation manners of the first aspect, the third aspect, or any of the implementation manners of the third aspect.

[0032] In an eighth aspect, a communication apparatus is provided, which can be a network device or a device (e.g., a chip or a chip system or a circuit) in a network device. The apparatus can include a processor, a memory, an input interface and an output interface, the input interface being configured to receive information from other communication apparatuses outside the apparatus, the output interface being configured to output information to other communication apparatuses outside the apparatus, and the processor being configured to invoke the computer program stored in the memory to execute the communication method provided in the second aspect or any of the implementation forms of the second aspect, or the fourth aspect or any of the implementation forms of the fourth aspect.

[0033] In a ninth aspect, a computer readable storage medium is provided, which stores computer instructions, and when the computer program or the computer instructions are executed, the method provided in the first aspect or any of the possible implementation forms of the first aspect, the second aspect or any of the possible implementation forms of the second aspect, the third aspect or any of the possible implementation forms of the third aspect, or the fourth aspect or any of the possible implementation forms of the fourth aspect is executed.

[0034] In a tenth aspect, a computer program product is provided, which includes executable instructions, and when the computer program product is executed on a communication device, the method provided in the first aspect or any of the possible implementation forms of the first aspect, the second aspect or any of the possible implementation forms of the second aspect, the third aspect or any of the possible implementation forms of the third aspect, or the fourth aspect or any of the possible implementation forms of the fourth aspect is executed.

[0035] In an eleventh aspect, a communication apparatus is provided, which includes a processor and can further include a memory, and is configured to implement the method provided in the first aspect or any of the possible implementation forms of the first aspect, the second aspect or any of the possible implementation forms of the second aspect, the third aspect or any of the possible implementation forms of the third aspect, or the fourth aspect or any of the possible implementation forms of the fourth aspect. The communication apparatus can be a chip system, which can be composed of a chip or can include a chip and other discrete devices.

[0036] In a twelfth aspect, a communication system is provided, which includes at least one terminal device and at least one network device, and when the at least one terminal device and the at least one network device are executed in the communication system, the communication method provided in the first aspect to the fourth aspect is executed. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows.

[0038] FIG. 1 is a schematic diagram of a communication system suitable for the communication method provided in the embodiments of the present application;

[0039] FIG. 2 is a schematic diagram of another communication system applicable to the communication method according to the embodiments of the present application;

[0040] FIG. 3 is a schematic diagram of an application framework of a communication system according to the embodiments of the present application;

[0041] FIG. 4 is a schematic diagram of an application framework of another communication system according to the embodiments of the present application;

[0042] FIG. 5 is a schematic diagram of a DMRS time-frequency resource pattern according to the embodiments of the present application;

[0043] FIG. 6 is a schematic diagram of a single RB pattern of a possible pilot superimposed data transmission according to the embodiments of the present application;

[0044] FIG. 7 is a schematic diagram of a possible power allocation factor on a single RB according to the embodiments of the present application;

[0045] FIG. 8 is a schematic diagram of a neuron structure according to the embodiments of the present application;

[0046] FIG. 9 is a schematic diagram of a neural network structure according to the embodiments of the present application;

[0047] FIG. 10 is a schematic diagram of an interaction of a communication method according to the embodiments of the present application;

[0048] FIGS. 11-13 are schematic diagrams of a pattern of a power allocation factor optimized by an AI model according to the embodiments of the present application;

[0049] FIG. 14 is a schematic diagram of a function fitting according to the embodiments of the present application;

[0050] FIG. 15 is a schematic diagram of another function fitting according to the embodiments of the present application;

[0051] FIG. 16 is a schematic diagram of yet another function fitting according to the embodiments of the present application;

[0052] FIG. 17 is a schematic diagram of an interaction of another communication method according to the embodiments of the present application;

[0053] FIG. 18 is a schematic diagram of a pattern compression and decompression according to the embodiments of the present application;

[0054] FIGS. 19 and 20 are schematic diagrams of a structure of a communication apparatus according to the embodiments of the present application. DETAILED DESCRIPTION

[0055] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0056] The terms "first" and "second" and the like in the description, claims and drawings of the application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprises", "comprising", "includes", "including" and the like are to be construed open-ended, allowing for instances where there are equivalents to processes, methods, systems, products or devices that do not literally include the recited steps or elements, but are otherwise equivalent in function, result, or operation. Additionally, the terms "a" and "an" are defined as one or more unless explicitly stated otherwise.

[0057] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that any of the embodiments described herein can be incorporated in to other embodiments.

[0058] In the present application, "at least one" means one or more, "multiple" means two or more, "at least two" means two or three or more, and "and / or" is used to describe the relationship between associated objects, indicating that there can be three relationships, for example, "A and / or B" can mean: only A, only B, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one" or the like means any combination of these items, including single or multiple combinations. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0059] In the present application, "sending information" can be understood as a device sending information to another device, or also can be understood as a logical module in a device sending information to another logical module. For example, "the access network device sending information" can be understood as the access network device sending information to another device (such as a terminal), or can be understood as a logical module 1 in the access network device sending information to a logical module 2 in the access network device.

[0060] In the present application, "receiving information" can be understood as a device receiving information from another device, or can also be understood as a logical module in a device receiving information from another logical module. For example, "the access network device receiving information" can be understood as the access network device receiving information from another device (such as a terminal), or can be understood as a logical module 1 in the access network device receiving information from a logical module 2 in the access network device.

[0061] In the present application, "sending information to (for example, a terminal)" can be understood as that the destination of the information is the terminal. It can include directly or indirectly sending information to the terminal. "Receiving information from (for example, a terminal)" or "receiving information from (for example, a terminal)" can be understood as that the source of the information is the terminal, and it can include directly or indirectly receiving information from the terminal. The information can be processed as necessary between the source and the destination of the information, such as format change, etc., but the destination can understand the valid information from the source. Similar expressions in the present application can be understood similarly, and will not be repeated here.

[0062] The network architecture to which the embodiments of the present application are applicable will be described first.

[0063] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as a 5th generation (5G) or new radio (NR) system, a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a wireless local area network (WLAN) system, a satellite communication system, a future communication system, or a fusion system of multiple systems, etc. The technical solutions provided in the present application can also be applied to device to device (D2D) communication, vehicle-to-everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and internet of things (IoT) communication system or other communication systems, and the embodiments of the present application do not limit this.

[0064] A network element in a communication system can send or receive a signal to or from another network element. The signal can include information, signaling, data, etc. The network element can also be replaced by an entity, a network entity, a device, a communication device, a communication module, a node, a communication node, etc. In the embodiments of the present application, the network element is taken as an example for description. For example, the communication system can include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. It can be understood that the terminal device in the embodiments of the present application can be replaced by a first network element, and the network device can be replaced by a second network element, both of which perform the corresponding communication method in the embodiments of the present application.

[0065] In a wireless communication network, for example, in a mobile communication network, the services supported by the network are increasingly diverse, and therefore the needs to be met are increasingly diverse. For example, the network needs to be able to support high rates, ultra-low latency, and / or ultra-large connections. This feature makes network planning, network configuration, and / or resource scheduling increasingly complex. In addition, as the functions of the network become increasingly powerful, for example, supporting increasingly high frequency spectrums, supporting high-order multiple input multiple output (MIMO) technology, supporting beamforming, and / or supporting new technologies such as beam management, network energy saving has become a hot research topic. These new needs, new scenarios, and new features have brought unprecedented challenges to network planning, operation and maintenance, and efficient operation. In order to meet this challenge, artificial intelligence technology can be introduced into wireless communication, thereby realizing network intelligence. In order to support artificial intelligence (AI) technology in the wireless network, an AI node can also be introduced into the network.

[0066] Please refer to FIG. 1, which is a schematic diagram of a communication system suitable for the communication method of the embodiments of the present application. As shown in FIG. 1, the communication system 100 can include at least one network device, for example, the network device 110 shown in FIG. 1; the communication system 100 can also include at least one terminal device, for example, the terminal device 120 and the terminal device 130 shown in FIG. 1. The network device 110 and the terminal device (such as the terminal device 120 and the terminal device 130) can communicate through a wireless link. The communication devices in the communication system, for example, the network device 110 and the terminal device 120, can communicate through multi-antenna technology.

[0067] Referring to FIG. 2, FIG. 2 is a schematic diagram of another communication system applicable to the communication method provided in the embodiments of the present application. As shown in FIG. 2, compared with the communication system 100 shown in FIG. 1, the communication system 200 shown in FIG. 2 further includes an AI network element 140. The AI network element 140 is configured to perform AI-related operations, for example, constructing a training data set or training an AI model.

[0068] In a possible implementation, the network device 110 can send data related to training of the AI model to the AI network element 140, and the AI network element 140 can construct a training data set and train the AI model. For example, the data related to training of the AI model can include data reported by the terminal device. The AI network element 140 can send a result of an AI model-related operation to the network device 110, and the network device 110 can forward the result to the terminal device. For example, the result of the AI model-related operation can include at least one of the following: a trained AI model, an evaluation result or a test result of the model, and the like. For example, part of the trained AI model can be deployed on the network device 110, and the other part can be deployed on the terminal device. Alternatively, the trained AI model can be deployed on the network device 110, or the trained AI model can be deployed on the terminal device.

[0069] It should be understood that FIG. 2 is only used as an example to illustrate that the AI network element 140 is directly connected to the network device 110, and in other scenarios, the AI network element 140 can also be connected to the terminal device. Alternatively, the AI network element 140 can be connected to both the network device 110 and the terminal device. Alternatively, the AI network element 140 can also be connected to the network device 110 through a third-party network element. The embodiments of the present application do not limit the connection relationship between the AI network element and other network elements.

[0070] The AI network element 140 can also be arranged in the network device and / or the terminal device as a module, for example, arranged in the network device 110 or the terminal device shown in FIG. 1.

[0071] It should be noted that FIGS. 1 and 2 are only simplified schematic diagrams for understanding by way of example, for example, the communication system can further include other devices, for example, can further include a wireless relay device and / or a wireless backhaul device, which are not shown in FIGS. 1 and 2. In actual applications, the communication system can include multiple network devices, and can also include multiple terminal devices. The embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0072] In the embodiments of the present application, the terminal device can also be referred to as a user equipment (UE), an access terminal, a user unit, a user station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent or a user apparatus.

[0073] The terminal device can be a device providing voice / data, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. At present, some examples of the terminal are: a mobile phone, a pad, a notebook computer, a palm computer, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a wearable device, a terminal device in a 5G network, or a terminal device in a future evolved public land mobile network (PLMN), etc. The embodiments of the present application are not limited thereto.

[0074] By way of example and not limitation, in embodiments of the present application, the terminal device can also be a wearable device. The wearable device can also be referred to as a wearable smart device, which is a general term for devices that are designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, clothing, and shoes. The wearable device is a portable device that is directly worn on the body or integrated into the clothes or accessories of the user. The wearable device is not only a hardware device, but also has strong functions through software support and data interaction and cloud interaction. The general wearable smart device includes a full function, a large size, and can realize complete or partial functions without relying on a smart phone, such as a smart watch or smart glasses, and focuses on a certain application function and needs to cooperate with other devices such as a smart phone, such as various smart wristbands and smart jewelry for monitoring vital signs.

[0075] In embodiments of the present application, the device for implementing the function of the terminal device can be a terminal device, or a device capable of supporting the terminal device to implement the function, such as a chip system, which can be installed in the terminal device or used in matching with the terminal device. In embodiments of the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices. In embodiments of the present application, only the device for implementing the function of the terminal device is taken as an example for description, and the present application is not limited to the scheme.

[0076] The network device in the embodiments of the present application can be a device for communicating with a terminal device, and the network device can also be referred to as an access network device or a radio access network device, for example, the network device can be a base station. The network device in the embodiments of the present application can refer to a radio access network (RAN) node (or device) for accessing a terminal device to a wireless network. The base station can broadly cover various names in the following or be replaced by the following names, such as: Node B (NodeB), evolved Node B (eNB), 5G base station (gNodeB, gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), primary station, secondary station, multi-mode wireless (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station can also refer to a communication module, modem or chip used in the aforementioned device or apparatus. The base station can also be a mobile switching center and a device assuming a base station function in D2D, V2X, M2M communication, a network side device in a future communication system, a device assuming a base station function in a future communication system, etc. The base station can support networks of the same or different access technologies. Optionally, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU). The embodiments of the present application do not limit the specific technology and specific device form of the network device.

[0077] The base station can be fixed or mobile. For example, a helicopter or a drone can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station. In other examples, the helicopter or the drone can be configured to serve as a device communicating with another base station.

[0078] In some deployments, the network device mentioned by embodiments of the present application can be a device including a CU, or a DU, or a device including a CU and a DU, or a control plane CU node (central unit-control plane (CU-CP)) and a user plane CU node (central unit-user plane (CU-UP)), and a DU node. For example, the network device can include a gNB-CU-CP, a gNB-CU-UP, and a gNB-DU.

[0079] In some deployments, wireless access is assisted by multiple RAN nodes cooperating to assist a terminal, and different RAN nodes respectively implement part of the functions of a base station. For example, the RAN node can be a CU, a DU, a CU-CP, a CU-UP, or an RU, etc. The CU and the DU can be separately arranged, or can also be included in the same network element, such as a BBU. The RU can be included in a radio frequency device or a radio frequency unit, such as an RRU, an AAU, or an RRH.

[0080] The RAN node can support one or more types of fronthaul interfaces, different fronthaul interfaces respectively corresponding to DUs and RUs having different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more of baseband functions, and the RU is configured to implement one or more of radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, which, relative to the CPRI, moves one or more of partial baseband functions of the downlink and / or uplink, such as, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / add cyclic prefix (CP), from the DU to the RU for implementation, and for the uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / remove cyclic prefix (CP), from the DU to the RU for implementation. In a possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the splitting manner between the DU and the RU is different, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0081] Taking eCPRI Cat A as an example, for downlink transmission, with layer mapping as the cut, the DU is configured to implement layer mapping and one or more functions (i.e., one or more of encoding, rate matching, scrambling, modulation, layer mapping) before layer mapping, while other functions (e.g., one or more of resource element (RE) mapping, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) after layer mapping are implemented in the RU. For uplink transmission, with de-RE mapping as the cut, the DU is configured to implement de-mapping and one or more functions (i.e., one or more of decoding, de-rate matching, de-scrambling, de-modulation, inverse discrete Fourier transform (IDFT), channel equalization, de-RE mapping) before de-mapping, while other functions (e.g., one or more of digital BF or fast Fourier transform (FFT) / CP removal) after de-mapping are implemented in the RU. It can be understood that the function description of the DU and the RU corresponding to various types of eCPRI can refer to the eCPRI protocol, which is not described here.

[0082] In a possible design, the processing unit in the BBU for implementing baseband functions is referred to as a base band high (BBH) unit, and the processing unit in the RRU / AAU / RRH for implementing baseband functions is referred to as a base band low (BBL) unit.

[0083] 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 an O-CU (open CU), the DU can also be referred to as an O-DU, the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. Any of the CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0084] In the embodiments of the present application, the apparatus for implementing the function of the network device can be a network device, or an apparatus capable of supporting the network device to implement the function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. The apparatus can be installed in the network device or used in combination with the network device. In the embodiments of the present application, only the apparatus for implementing the function of the network device is taken as an example for illustration, and the scheme of the embodiments of the present application is not limited in this way.

[0085] The network device and / or the terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on water surface; and can also be deployed on airplanes, balloons and satellites in the air. The scenarios where the network device and the terminal device are located are not limited in the embodiments of the present application. In addition, the terminal device and the network device can be hardware devices, or software functions running on special hardware, software functions running on general hardware, such as virtualized functions instantiated on a platform (for example, a cloud platform), or entities including special or general hardware devices and software functions. The specific forms of the terminal device and the network device are not limited in the present application.

[0086] The communication system to which the technical scheme of the embodiments of the present application is applicable can also include other network elements or entities. As an example, the communication system to which the technical scheme of the embodiments of the present application is applicable can also include a core network, which can include one or more of the following entities: an access and mobility management function (AMF) entity, a session management function (SMF) entity, a unified data management (UDM) network element, or a user plane function (UPF) entity, etc.

[0087] The AMF entity can also be referred to as an access and mobility management function, an access and mobility management device, an access and mobility management network element, an access management device or a mobility management device, and is mainly used for mobility management and access management, etc.

[0088] Optionally, the AMF entity can also be used to implement other functions in the mobility management entity (MME) function except for session management. For example, the AMF entity can be used for access authorization (or authentication), registration of a user equipment, mobility management, tracking area update process, reachability detection, selection of a session management network element, management of mobile state conversion, etc.

[0089] The SMF entity is mainly used for management of session-related services, such as establishment of a session.

[0090] The UDM network element is mainly used for processing terminal device identification, access authentication, registration, and mobility management, etc.

[0091] The UPF entity is located between the user access layer and the control layer, and mainly provides user plane board services.

[0092] The network elements or entities in the core network described above can have other names, which are not limited in the present application.

[0093] Optionally, the AI node can be deployed in one or more of the following positions in the communication system: an access network device, a terminal device, or a core network device, etc., or the AI node can also be deployed separately, for example, deployed in a position other than any of the above devices, such as a host or a cloud server of an over the top (OTT) system. The AI node can communicate with other devices in the communication system, which can be one or more of the following: a network device, a terminal device, or a network element of a core network, etc.

[0094] It can be understood that the present application does not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes being responsible for different functions.

[0095] It can also be understood that the AI node can be a separate device, or can be integrated into the same device to implement different functions, or can be a network element in a hardware device, or can be a software function running on a dedicated hardware, or a virtualized function instantiated on a platform (such as a cloud platform), and the present application does not limit the specific form of the AI node.

[0096] The AI node can be an AI network element or an AI module.

[0097] Referring to FIG. 3, FIG. 3 is a schematic diagram of an application framework of a communication system according to an embodiment of the present application. As shown in FIG. 3, the network elements in the communication system are connected through interfaces (such as NG, Xn), or air interfaces. One or more AI modules (only one is shown in FIG. 3 for clarity) are arranged in one or more of the following devices: a core network device, an access network node (RAN node), a terminal, or an operation administration and maintenance (OAM) device. The access network node can be a separate RAN node, or can include multiple RAN nodes, for example, including a CU and a DU. The CU and / or DU can also be provided with one or more AI modules. Optionally, the CU can also be split into a CU-CP and a CU-UP. One or more AI models are arranged in the CU-CP and / or CU-UP.

[0098] The AI module is used to implement a corresponding AI function. The AI modules deployed in different network elements can be the same or different. The AI module can implement different functions according to different parameter configurations. The model of the AI module can be configured based on one or more of the following parameters: structural parameters (such as at least one of the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of neurons, the activation function of neurons, or the bias in the activation function), input parameters (such as the type of input parameters and / or the dimension of input parameters), or output parameters (such as the type of output parameters and / or the dimension of output parameters). The bias in the activation function can also be referred to as the bias of the neural network.

[0099] One AI module can have one or more models. One model can infer an output, which includes one parameter or multiple parameters. The learning process, training process, or inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0100] The network device can be a network device provided with one or more AI modules. The network device can be one or more of the core network devices, access network nodes (RAN nodes), or OAM shown in FIG. 3.

[0101] Referring to FIG. 4, FIG. 4 is a schematic diagram of an application framework of another communication system provided by an embodiment of the present application. As shown in FIG. 4, the AI module can be a RIC, such as a near-real-time RIC or a non-real-time RIC. For example, the near-real-time RIC is arranged in the RAN node (for example, in the CU, DU), and the non-real-time RIC is arranged in the OAM, the cloud server, the core network device, or other network devices. The RIC can obtain a subset of multiple terminal devices from the RAN node (for example, CU, CU-CP, CU-UP, DU, and / or RU), reorganize it into a training data set #2, and train based on the training data set #2. Exemplarily, the near-real-time RIC and the non-real-time RIC can also be arranged separately as a network element, and the network device can be the near-real-time RIC or the non-real-time RIC.

[0102] As shown in FIG. 4, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI module shown in FIG. 3, which is used to implement AI-related functions. The RIC includes a near-real time RIC (near-RT RIC) and a non-real time RIC (Non-RT RIC). The non-real time RIC mainly processes non-real time information, such as data that is not sensitive to latency, which can be on the order of seconds. The real-time RIC mainly processes near-real time information, such as data that is relatively sensitive to latency, which is on the order of tens of milliseconds.

[0103] The near-real time RIC is used for model training and inference. For example, it is used to train an AI model and perform inference using the AI model. The near-real time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data. Optionally, the near-real time RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the near-real time RIC delivers inference results to a DU, which then delivers them to an RU.

[0104] The non-real time RIC is also used for model training and inference. For example, it is used to train an AI model and perform inference using the AI model. The non-real time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data, and inference results can be delivered to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the non-real time RIC delivers inference results to a DU, which then delivers them to an RU.

[0105] The near-real time RIC and the non-real time RIC can also be separately configured as a network element. Alternatively, the near-real time RIC and the non-real time RIC can also be part of other devices. For example, the near-real time RIC can be configured in a RAN node (such as a CU or a DU), while the non-real time RIC can be configured in an OAM, a cloud server, a core network device, or another network device.

[0106] The following definitions of technical terms that can appear in the embodiments of the present application are provided. The terms used in the embodiments section of the present application are only used to explain the specific embodiments of the present application and are not intended to limit the present application.

[0107] (1) Artificial intelligence: Let the machine have human intelligence, apply computer software and hardware to simulate some intelligent behavior of human beings, including machine learning and many other methods.

[0108] (2) Machine learning: Learn models or rules from raw data, there are many different machine learning methods, such as neural network (NN), decision tree, support vector machine, etc.

[0109] (3) AI model: A function model that maps a certain dimension of input to a certain dimension of output, and the model parameters are obtained by machine learning training. For example, f(x) = ax 2 +b is a quadratic function model, which can be regarded as an AI model, a and b correspond to the parameters of the model, which can be obtained by machine learning training.

[0110] (4) Loss function: used to measure the difference between the predicted value of the model and the true value.

[0111] (5) Reference signal:

[0112] As an important part of system design, the reference signal is mainly responsible for channel state measurement, data demodulation, beam training (beam training) and time-frequency parameter control (tracking) and the like. The design of the reference signal mainly includes the design of the random sequence generation and the design of the time-frequency resource mapping, and the transmission of the corresponding sequence power, combined with the above three points to form a complete reference signal pattern design.

[0113] It should be noted that the reference signal pattern design / configuration mentioned in the embodiments of the present application can at least include the configuration of the position of the reference signal, the configuration of the power of the reference signal, and the configuration of the sequence of the reference signal.

[0114] The reference signal can refer to different signals in uplink and downlink transmission, for example:

[0115] Table 1 Specific signals referred to by reference signals in uplink and downlink

[0116] One of the roles of the reference signal is to enable channel estimation, which is needed for coherent monitoring and decoding of the data at the receiver. Since the reference signal carries little or no useful information, the overhead of the reference signal needs to be considered to balance the channel estimation performance and the useful time-frequency resources for data transmission. The reference signal is usually sparse in time, frequency and space domains, so after the channel estimation algorithm is used to estimate the channel at the time-frequency resource units where the reference signal is transmitted, the channel at the time-frequency-space resource units where no reference signal is transmitted also needs to be estimated. Common channel estimation algorithms include least square (LS), linear minimum mean square error (LMMESE) and compressed sensing (CS) algorithms.

[0117] It should be noted that the reference signal can also be referred to as a pilot or pilot signal, and the reference signal, pilot and pilot signal appearing in this paper can be replaced equivalently.

[0118] (6) Frame structure with data and pilot time-frequency orthogonal

[0119] As described above for the reference signal, there are many reference signals in the 5G system with different roles, among which the DMRS is used for data demodulation, i.e. the DMRS can be used to estimate the channel response of the time-frequency resource occupied by the data. There is a certain trade-off between the accuracy of channel estimation and the density / overhead of DMRS. If the channel exhibits severe frequency selectivity (i.e. the channel varies greatly in the frequency domain), the density of DMRS in the frequency domain should be increased. Similarly, if the channel varies quickly in the time domain, more resources need to be occupied in the time domain to deploy the reference signal. After determining the DMRS density in the time-frequency domain, the position of the DMRS in the time-frequency resource block needs to be further considered. For example, under the condition of channel stability, in order to reduce the interpolation error and reduce the implementation complexity, the DMRS can be uniformly distributed in the frequency and time domain. Since the DMRS itself does not transmit any data signal useful to the user, the DMRS needs to be distributed at an appropriate density to maximize the throughput.

[0120] The DMRS random sequence generation method depends on the specific waveform used, and currently 5G supports two waveforms: cyclic prefix-orthogonal frenquency division multiplexing (CP-OFDM) and discrete fourier transform-orthogonal frenquency division multiplexing (DFT-s-OFDM). The way DMRS sequences are mapped to physical time-frequency resource units is as follows. Specifically, the position of DMRS in a single time-frequency resource block can be determined by the following parameters:

[0121] Mapping type: type A and type B, the two mapping types differ in the starting symbol position of the physical downlink shared channel (PDSCH) and the limit of the number of PDSCH symbols.

[0122] DMRS configuration type: determines the frequency domain resource position of DMRS.

[0123] DMRS additional position: determines whether there is an additional DMRS in the time domain.

[0124] Max length: determines whether it is a single-symbol DMRS or a double-symbol DMRS.

[0125] A possible DMRS pattern is shown in FIG. 5, where the black time-domain resource block represents the position carrying the DMRS signal, and the white time-domain resource block represents the position carrying the data. This pattern corresponds to DMRS mapping type A, DMRS configuration type 1, max length = 1, DMRS additional position = 0, and DMRS type A position = 2. The protocol generates a limited number of DMRS patterns by giving the acceptable values (range) of each parameter.

[0126] (7) Frame structure for data and pilot superposition transmission:

[0127] The pilot is superimposed on the data for transmission, which realizes that each RE has both pilot and data. The superposition of the two is based on a certain power coefficient. For example, in a certain RE, the two are superimposed with a power coefficient of 0.5, and the final symbol formed is:

[0128] Wherein, Pilot represents pilot symbol on certain RE, data symbol represents data symbol on the same RE, both of which can be generated based on certain constellation mapping mode, Pilot can be quadrature phase shift keying (QPSK), data symbol can be QPSK, 16 / 64 / 256 / 1024 QAM, it is to be noted that the above mapping is not limited, as long as the constellation mapping meeting the requirements (energy normalization, etc.) can realize the superposition transmission of both.

[0129] Please refer to FIG. 6, which is a schematic diagram of a possible pilot superposition data sending single RB pattern provided by the embodiment of the present application. As shown in FIG. 6, each RE can realize the superposition of pilot and data with different power coefficients, each RE can realize the superposition of pilot and data with different power coefficients, the black part represents the proportion of pilot in the RE, and the white part represents the proportion of data in the RE. Wherein, each RE can correspond to a power allocation coefficient, for example, a RE has a black part, the corresponding power allocation coefficient is a non-0 value, for example, a RE has no black part, the corresponding power allocation coefficient can be 0 or approximately 0. A possible power allocation coefficient of each RE on a single RB can be shown in FIG. 7. For the power allocation coefficient corresponding to the RE, the power allocation coefficient can correspond to the power allocation coefficient of the pilot or data in the RE. It can be understood that if the power allocation coefficient corresponds to the power allocation coefficient of the pilot in the RE, the power allocation coefficient of the data in the RE can be 1-the power allocation coefficient of the pilot in the RE; if the power allocation coefficient corresponds to the power allocation coefficient of the data in the RE, the power allocation coefficient of the pilot in the RE can be 1-the power allocation coefficient of the data in the RE.

[0130] For example, assuming the power allocation coefficient is A and corresponds to the power of the pilot, the power allocated to the pilot P is A, and the power allocated to the data S is 1-A, then the pilot mapped to a RE can be represented as The data mapped to the RE can be represented as Thus, the superposition signal can be

[0131] This way can have the following two advantages:

[0132] 1) Resource saving: releasing data resources (can occupy the original pilot position), realizing throughput gain.

[0133] 2) Support more flow: give pilot greater freedom (length, placement position / way), different flows can be directly distinguished by orthogonal sequences.

[0134] (8) Artificial intelligence and machine learning:

[0135] Machine learning is an important technical approach to achieve artificial intelligence. Machine learning can be divided into supervised learning, unsupervised learning and reinforcement learning. Supervised learning is based on the collected sample values and sample labels, using machine learning algorithms to learn the mapping relationship from sample values to sample labels, and using machine learning models to express the learned mapping relationship. The process of training machine learning model is the process of learning this mapping relationship. For example, in signal detection, the noisy received signal is the sample, and the true constellation point corresponding to the signal is the label. Machine learning expects to learn the mapping relationship between sample and label through training, i.e. to learn a signal detector. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the true label. Once the mapping relationship is learned, the learned mapping can be used to predict the label of each new sample. The learned mapping relationship of supervised learning can include linear mapping and nonlinear mapping. According to the type of label, the learning task can be divided into classification task and regression task.

[0136] Unsupervised learning only relies on the collected sample values to discover the internal pattern of the sample by using algorithms. In unsupervised learning, a class of algorithms uses the sample itself as a supervision signal, i.e. the model learns the mapping relationship from sample to sample, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the sample itself. Self-supervised learning can be used for signal compression and decompression recovery applications. Common algorithms include autoencoder and generative adversarial network.

[0137] Reinforcement learning is different from supervised learning, which is a class of algorithms that learn strategies to solve problems by interacting with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have clear "correct" action label data. The algorithm needs to interact with the environment to obtain the reward signal feedback from the environment, and then adjust the decision action to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user according to the system total throughput feedback from the wireless network, and then expects to obtain a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal decision action. However, because the "correct action" label cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action". Reinforcement learning training is achieved through iterative interaction with the environment.

[0138] Deep neural network (DNN) is a specific implementation form of machine learning. According to the universal approximation theorem, neural network can theoretically approximate any continuous function, thus making neural network have the ability to learn any mapping. Traditional communication system needs to design communication module with the help of rich expert knowledge, while deep learning communication system based on DNN can automatically discover the implicit pattern structure from a large amount of data set, establish the mapping relationship between data, and obtain better performance than traditional modeling method.

[0139] Please refer to FIG. 8, which is a schematic diagram of a neuron structure provided by an embodiment of the present application. The idea of DNN comes from the neuron structure of brain organization. Each neuron performs weighted summation operation on its input value, and the weighted summation result generates output through a nonlinear function. As shown in FIG. 8, specifically, assuming that the input of a neuron is x = [x0, …, xN-1], the weight corresponding to the input is d = [d0, …, dN-1], the bias of weighted summation is b, and the form of nonlinear function can be diversified, one example is the max{0, x} maximum function. Then the effect of execution of a neuron can be n . n Please refer to FIG. 9, which is a schematic diagram of a neural network structure provided by an embodiment of the present application. As shown in FIG. 9, DNN generally has a multi-layer structure, each layer of DNN can contain multiple neurons, and the received values are transmitted to the intermediate hidden layer after being processed by the neurons in the input layer. Similarly, the hidden layer transmits the calculation result to the last output layer to generate the final output of DNN. DNN generally has more than one hidden layer, and the hidden layer often directly affects the ability to extract information and fit functions. Increasing the number of hidden layers of DNN or expanding the width of each layer can improve the function fitting ability of DNN. The weight value in each neuron is the parameter of the DNN network model. The model parameters are optimized through the training process, so that the DNN network has the ability to extract data features and express mapping relationships. DNN generally uses supervised learning or unsupervised learning strategy to optimize model parameters.

[0140] According to the construction mode of the network, DNN can be divided into feedforward neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN). The FNN network shown in FIG. 2 is characterized by complete connection between two adjacent neurons in each layer, which makes FNN usually need a large amount of storage space and leads to high computational complexity.

[0141] ​CNN is a kind of neural network specially used to deal with data with similar grid structure. For example, time series data (time axis discrete sampling) and image data (two-dimensional discrete sampling) can be considered as similar grid structure data. CNN does not use all input information for operation at one time, but uses a fixed size window to intercept part of the information for convolution operation, which greatly reduces the calculation of model parameters. In addition, according to the different types of window intercepted information (such as people and objects in the same picture are different types of information), each window can use different convolution kernel operation, which makes CNN better extract the features of input data.

[0142] RNN is a kind of DNN network using feedback time series information. Its input includes the new input value at the current time and the output value of itself at the previous time. RNN is suitable for obtaining sequence characteristics with time correlation, and is particularly suitable for speech recognition, channel coding and decoding and other applications.

[0143] The above FNN, CNN and RNN are common neural network structures, which are constructed based on neurons. As mentioned above, each neuron performs weighted summation operation on its input value, and the weighted summation result is output through a nonlinear function. Therefore, the weights of the weighted summation operation of the neurons in the neural network and the nonlinear function are called the parameters of the neural network. Taking the neuron with max{0, x} as the nonlinear function as an example, the operation of the neuron is as follows: The parameters of the neuron performing the operation are the weights d = [d0, …, d n ], the bias of the weighted summation b, and the nonlinear function max{0, x}. The parameters of all neurons of a neural network constitute the parameters of the neural network.

[0144] It should be understood that the definitions of the above technical terms are only examples. For example, as technology continues to develop, the scope of the above definitions can also change, and the embodiments of the present application are not limited.

[0145] First, in order to facilitate the understanding of the embodiments of the present application, the technical problems to be solved by the present application are further analyzed and proposed.

[0146] For the scheme that there is both reference signal and data on each RE, the superposition of reference signal and data needs to be based on a certain power allocation coefficient. Because the reference signal and the data are transmitted on the same time-frequency resource, the power allocation coefficient needs to be known at the end of channel estimation, that is, the sending end needs to transmit the power allocation coefficient to the opposite end through the air interface.

[0147] At present, there are various technical schemes for power allocation coefficient optimization and power allocation coefficient transmission, the following two kinds are exemplarily listed as follows:

[0148] Scheme one: find the most power allocation coefficient through non-AI scheme optimization, considering the complexity, basically through identification (ID) search, that is, from 0 to 1 according to a certain granularity such as 0.01 / 0.05 / … / 0.1 to find the optimal power allocation coefficient, and send the found power allocation coefficient to the opposite end.

[0149] The disadvantages of this scheme one: this optimization method can only be based on resource block (RB), resource block group (RBG), sub-band, and full-band sharing the same coefficient to complete, the granularity is large, and the optimal power allocation coefficient that is most suitable for the channel cannot be obtained. In other words, the air interface needs to transmit the power superposition coefficient shared by RB, RBG, sub-band, and full-band, the power coefficient optimization space is limited, and the performance is restricted.

[0150] Scheme two: find the power allocation coefficient through AI scheme optimization, and directly notify all the found power allocation coefficients to the opposite end through DCI.

[0151] The disadvantages of this scheme two: although the AI scheme can optimize the power allocation coefficient to the RE level, transmitting all the RE corresponding power allocation coefficients to the opposite end through DCI leads to a large air interface transmission overhead.

[0152] Therefore, the technical problems to be solved by the present application can include the following:

[0153] 1. How to improve the optimization performance of the power allocation coefficient;

[0154] 2. How to reduce the air interface transmission overhead of the transmission power allocation coefficient.

[0155] The present application proposes a communication method, which can realize the transmission of only function / segment function information or AI compressed vector by reconstructing the power allocation coefficient into a fitting function / segment function or through AI compression, so that the opposite end restores the power allocation coefficient according to the function information / segment function or AI compressed vector, thereby reducing the air interface transmission overhead.

[0156] The following will be described through the following embodiments. It should be understood that these communication methods can be used in combination with each other.

[0157] It should be understood that the AI evolution may change with the evolution of the technical scheme, and the technical scheme provided by the present application is not limited to the processes described below. Further, the description of the scene in the present application embodiment is only for example, and the scheme of the present application embodiment is not limited to only being used in the described scene. It is also applicable to scenes with similar problems.

[0158] The terminal device in the embodiments of the present application (corresponding to the embodiments described below with reference to FIG. 10-FIG. 11) can be the terminal device in the network architecture shown in FIG. 1, and the functions performed by the terminal device in the embodiments can also be performed by the device (for example, a chip, or a chip system, or a circuit) in the terminal device. The network device in the embodiments can be the access network device in the network architecture shown in FIG. 1, and the functions performed by the network device in the embodiments can also be performed by the device (for example, a chip, or a chip system, or a circuit) in the network device. The embodiments of the present application are uniformly described here, and will not be described again in the following.

[0159] In combination with the network architecture described above, a communication method provided by the embodiments of the present application is described below. Please refer to FIG. 10, which is an interaction schematic diagram of a communication method provided by the embodiments of the present application. As shown in FIG. 10, the communication method can include S1001-S1002.

[0160] S1001, the network device sends information of a function to the terminal device, the function being used to fit power allocation coefficients corresponding to a plurality of REs. Correspondingly, the terminal device receives the information of the function from the network device.

[0161] The reference signal is sent on the data to achieve that there is both the reference signal and the data on each RE, and the superposition of the two is based on a certain power allocation coefficient, for example, on a certain RE, the two are superimposed with a power allocation coefficient of 0.5. In the embodiments of the present application, the power allocation coefficient can be understood as the power allocation coefficient corresponding to each RE, and is sent after being optimized by an AI model. The power allocation coefficient obtained by optimizing the AI model can achieve RE-level power optimization, and the obtained power allocation coefficient is more easily adapted to the channel, thereby the network performance can be improved, and the performance of the communication system is further improved.

[0162] Specifically, the AI model is deployed on the terminal device side, that is, the terminal device side is responsible for AI-based channel estimation, equalization and demodulation in the scenario of data pilot superposition on the network device side. The network device side configures a demodulation reference signal (DMRS) to the terminal device, and unlike the traditional implementation, the DMRS and the data symbol are transmitted on different time-frequency resources RE, and in this embodiment, the two are combined into a single symbol with a certain power allocation coefficient and superimposed and transmitted on a single RE. After receiving the superimposed signal transmitted through the channel, the terminal device simultaneously uses the superimposed power allocation coefficient transmitted by the network device to realize channel estimation, equalization and demodulation. Among them, one or more AI models can be used to complete channel estimation, equalization and demodulation respectively, or to complete the joint of at least two functions, for example, a single AI model completes the functions of channel estimation and equalization, or a single AI model completes the functions of channel estimation, equalization and demodulation. This embodiment can pass each element value on the pattern of the AI-optimized fine granularity (such as at least the DMRS sequence intensity) power allocation coefficient to the opposite end for channel estimation.

[0163] Please refer to FIGS. 11-13, which are schematic diagrams of a power allocation coefficient optimized by an AI model provided by an embodiment of the present application. As shown in pattern 1 of FIG. 11, each OFDM symbol can be multiplexed with the pattern, and then the AI model only needs to optimize the power allocation coefficient corresponding to each RE on a single OFDM symbol. As shown in pattern 2 of FIG. 12, each OFDM symbol is not multiplexed with the pattern, and then the AI model needs to optimize the power allocation coefficient corresponding to each RE on all OFDM symbols. As shown in pattern 3 of FIG. 13, there are multiple streams, and each OFDM symbol is not multiplexed with the pattern, and then the AI model needs to optimize the power allocation coefficient corresponding to each RE on all OFDM symbols of each stream. As can be seen from FIGS. 11-13, for pattern 1, some rows of elements are 0, for pattern 2, there are all-0 rows, and for a single non-0 element row, the element value decreases from the center to both sides, for pattern 3, the first stream has basically no 0 elements, but there is a phenomenon that the element value of each column decreases from the center to both sides, and the pattern of the second stream is similar to pattern 2.

[0164] Among them, the function can be used to fit the power allocation coefficients corresponding to multiple REs. Corresponding to patterns 1-3 shown in FIGS. 11-13, the multiple REs can be the REs on a single OFDM symbol, or can be the REs on each OFDM symbol in multiple OFDM symbols, or can be the REs on each OFDM symbol in multiple OFDM symbols in multiple streams, and the present application does not limit this.

[0165] The function can be a polynomial function, for example, the function can be ax+b, coeff[0]=a, coeff[1]=b, and for example, the function can be ax 2+ bx + c, coeff[0] = a, coeff[1] = b, coeff[2] = c, and the high order is the same, where x represents a subcarrier index.

[0166] The function information can include one or more of a coefficient corresponding to the function, a type of the function, or an order corresponding to the function. Specifically:

[0167] In a first possible implementation, the function information can include a coefficient corresponding to the function. For example, if the function is ax + b, the coefficients corresponding to the function are a and b. For another example, if the function is ax 2 + bx + c, the coefficients corresponding to the function are a, b, and c. Optionally, the function information can also include an order corresponding to the function. For example, the function can be a first-order function or a second-order function or another high-order function. Alternatively, the terminal device can determine the order corresponding to the function according to the number of coefficients corresponding to the function. For example, if the coefficients corresponding to the function are a and b, the number is 2, the terminal device can determine that the function is a first-order function. For another example, if the coefficients corresponding to the function are a, b, and c, the number is 3, the terminal device can determine that the function is a second-order function, and other high-order functions are similar and will not be enumerated. For the first possible implementation, unlike directly transmitting the power allocation coefficient of each RE over the air interface, the embodiments of the present application transmit the function information of fitting the power allocation coefficient of each RE over the air interface, such as the coefficient of the function and the order of the function (optionally), which greatly reduces the air interface transmission overhead.

[0168] In a second possible implementation, a plurality of REs can be segmented, and each segment can be fitted with a segment function to fit the power allocation coefficient of each RE in the segment. In combination with the first possible implementation described above, the function information can include not only the coefficient of the function but also the type of the function. If the type of the function is a segment function, the segment interval can also be included. For example, if the number of segments is 4, the indexes of the subcarriers corresponding to the plurality of REs can be divided into 4 segments. For another example, if the number of segments is 8, the indexes of the subcarriers corresponding to the plurality of REs can be divided into 8 segments. It should be noted that the number of subcarrier indexes in each segment can be the same or different, and the fitting method of the segment function in each segment can be different. There can be a trade-off relationship between the number of segments and the fitting length, which is not limited by the present application. Optionally, the function information can also include the order of the function.

[0169] That is, the function information can include a segment type, for example, a segmented function or a non-segmented function, and if the function type is a segmented function, the function information can further include a segment interval and coefficients of the segmented function corresponding to each segment interval. Optionally, the function corresponding order can also be included, or the terminal device can determine the function corresponding order according to the number of coefficients of the function. Compared with the first possible implementation manner, the second possible implementation manner can improve the fitting effect of the function of fitting the power allocation coefficient corresponding to each RE, so that the fitted power allocation coefficient is more accurate.

[0170] In the third possible implementation manner, due to the existence of 0 elements and approximate 0 elements, for example, in the pattern 1 shown in FIG. 11, the power allocation coefficients corresponding to the subcarriers 6 and 22 and 23 are approximate 0 and 0, or the difficulty of fitting is increased and the accuracy is reduced, therefore, the 0 elements and / or approximate 0 elements in the power allocation coefficients corresponding to multiple REs can be separated out, and the power allocation coefficients of the remaining non-0 elements are fitted, so that the difficulty of fitting can be reduced and the accuracy of fitting can be improved. In combination with the first possible implementation manner, the function information can include function information corresponding to non-0 elements and subcarrier indexes corresponding to 0 elements and / or approximate 0 elements. For the function information corresponding to non-0 elements, reference can be made to the first possible implementation manner and the second possible implementation manner, which will not be described herein again. Compared with the first possible implementation manner and the second possible implementation manner, based on separately notifying the subcarrier indexes corresponding to 0 elements and / or approximate 0 elements to the opposite end, the 0 elements and / or approximate 0 elements in the power allocation coefficients corresponding to multiple REs can be separated out, and the power allocation coefficients of the remaining non-0 elements are fitted, so that the difficulty of fitting can be reduced and the accuracy of fitting can be improved.

[0171] In the fourth possible implementation manner, as can be seen from the pattern 2 and the pattern 3 shown in FIG. 12 and FIG. 13, there is a phenomenon that the element values of each column decrease from the center to both sides in the patterns, for example, in the pattern 2, taking column index (OFDM symbol) 3 as the center, the element values of each column gradually decrease to the left or right, therefore, the power allocation coefficients of the column with column index 3 can be transmitted by using the first to third possible implementation manners, and the values decreasing to the left or right with column index 3 as the center can be fitted by using another function.

[0172] That is, the function can include a first function and a second function, the first function can be used to fit the power distribution coefficients corresponding to the plurality of REs corresponding to the column index X located at the center position, and the second function is used to fit the differential values of the power distribution coefficients from the column index to the two sides of the center position, that is, to fit the differential values of the column index X-Y to the column index X and the column index X to the column index X+Z, where Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14. For example, the first function (such as f(x)) fits the power distribution coefficients of the column index 3, and the second function (such as g(x)) fits the differential values of the column index 3 to the column index 2 and the column index 3 to the column index 4, and then the function fitting the power distribution coefficients of the column index 2 and the column index 4 can be f(x)-g(x), x∈[0,47], and by analogy, the function fitting the power distribution coefficients of the column index 1 and the column index 5 can be f(x)-A*g(x), and by analogy, the function fitting the power distribution coefficients of the column index 0 and the column index 6 can be f(x)-B*g(x), and A and B are set values. Compared with the first to third possible implementation manners, the notification mode of different power distribution coefficients on different OFDM symbols can be realized, and the function fitting and function transmission of the two-dimensional (subcarrier and OFDM symbol) power distribution coefficients are realized based on multiple functions.

[0173] S1002, the terminal device determines the power distribution coefficient corresponding to each RE according to the subcarrier index and the function.

[0174] After the terminal device receives the function information from the network device, the terminal device can determine the power distribution coefficient corresponding to each RE according to the subcarrier index and the function.

[0175] According to the first possible implementation manner of the function information in the above step S1001, the terminal device can determine the function according to the coefficients corresponding to the function, and optionally, the terminal device can further determine the order of the function according to the number of the coefficients corresponding to the function, so as to determine the function. Then, the terminal device determines the power distribution coefficient corresponding to each RE according to the subcarrier index and the function. Please refer to FIG. 14, which is a schematic diagram of function fitting provided by an embodiment of the present application. The terminal device determines the power distribution coefficient according to the subcarrier index calculation, and the trained power distribution coefficient is shown in FIG. 14.

[0176] In a second possible implementation of the information of the function in step S1001, the terminal device can determine each piece of the segmented function according to the segmented interval of the function and the coefficients of the segmented function. Optionally, the terminal device can further determine the order of each piece of the segmented function according to the number of the coefficients corresponding to each piece of the segmented function. Optionally, the terminal device can determine that the segmented function is transmitted this time according to the type of the function. Then, the terminal device can determine the power allocation coefficient corresponding to each RE according to the subcarrier index corresponding to each piece of the segmented interval and the segmented function. Please refer to FIG. 15, which is another diagram of function fitting provided by an embodiment of the present application. The terminal device determines the power allocation coefficient of the segmented number of 4 and the segmented number of 8 according to the subcarrier index corresponding to each piece of the segmented interval, and the power allocation coefficient is shown in (a) of FIG. 15 and (b) of FIG. 15 respectively.

[0177] In a third possible implementation of the information of the function in step S1001, the terminal device can determine that the power allocation coefficient corresponding to the subcarrier index of the 0 element and / or the approximate 0 element is 0 and / or approximate 0 according to the subcarrier index of the 0 element and / or the approximate 0 element, and determine the function according to the information of the function corresponding to the non-0 element, and determine the power allocation coefficient corresponding to each RE of the non-0 element or the non-approximate 0 element according to the function and the subcarrier index other than the subcarrier index of the 0 element and / or the approximate 0 element. The power allocation coefficient corresponding to each RE can be obtained by combining all the subcarrier indexes. Please refer to FIG. 16, which is another diagram of function fitting provided by an embodiment of the present application. The terminal device determines the power allocation coefficient according to the subcarrier index corresponding to the 0 element and / or the approximate 0 element, and the function of the subcarrier index corresponding to the non-0 element, and the power allocation coefficient is shown in FIG. 16.

[0178] For the fourth possible implementation of the information corresponding to the function in step S1001, the terminal device can determine the power allocation coefficient corresponding to each RE with column index X according to the first function, determine the differential values of column index X-Y to column index X and column index X to column index X+Z according to the second function, and then determine the power allocation coefficient corresponding to each RE with column index X-Y and column index X+Z according to the power allocation coefficient corresponding to each RE with column index X and the differential values. For example, the column index is 0-6, the power allocation coefficient corresponding to each RE with column index 3 is determined according to the first function f(x), the differential values of column index 3 to column index 2 and column index 3 to column index 4 are determined according to the second function g(x), then the power allocation coefficient corresponding to each RE with column index 2 and column index 4 can be determined according to f(x)-g(x), x∈[0,47], by analogy, the power allocation coefficient corresponding to each RE with column index 1 and column index 5 can be determined according to f(x)-A*g(x), by analogy, the power allocation coefficient corresponding to each RE with column index 0 and column index 6 can be determined according to f(x)-B*g(x), and A and B are set values.

[0179] In this embodiment, the power allocation coefficients corresponding to a plurality of REs can be fitted by a function, the information of the fitted function is transmitted only in the air interface, and then the terminal device can determine the power allocation coefficient corresponding to each RE according to the function, without directly transmitting a plurality of power allocation coefficients in the air interface, so as to reduce the air interface transmission overhead.

[0180] Another communication method provided by the embodiments of the present application will be described below. It should be understood that the explanations of the terms of different embodiments in the present application can be mutually referred to, and to avoid redundancy of description, different embodiments can not repeat the same terms. Please refer to FIG. 17, which is an interaction schematic diagram of another communication method provided by the embodiments of the present application. As shown in FIG. 17, the communication method can include S1701-S1703.

[0181] S1701: The network device compresses the pattern of the power allocation coefficients corresponding to one or more REs.

[0182] The network device can compress the pattern of the power allocation coefficients corresponding to one or more REs by using an AI encoder.

[0183] S1702: The network device sends a first vector to the terminal device, the first vector including the compressed pattern of the power allocation coefficients corresponding to one or more REs. Correspondingly, the terminal device receives the first vector from the network device.

[0184] S1703: The terminal device decompresses the first vector to obtain the power allocation coefficients corresponding to one or more REs.

[0185] After receiving the first vector from the network device, the terminal device decompresses the first vector to obtain the power allocation coefficients corresponding to the one or more REs. Specifically, the power allocation coefficients corresponding to the one or more REs can be obtained by decompressing the first vector through an AI decoder.

[0186] In a possible implementation, the AI encoder and the AI decoder are trained in pairs, and a loss function used in the training is a function related to a reconstruction loss, and the function related to the reconstruction loss includes one or more of NMSE, MSE, CS, GCS, or SGCS.

[0187] Referring to FIG. 18, FIG. 18 is a schematic diagram of pattern compression and decompression provided by an embodiment of the present application. As shown in FIG. 18, taking pattern 2 as an example for example description, the present embodiment can train the compression / restoration neural network of the pattern by using the method of the AI encoder / AI decoder, wherein the network device can compress the pattern (for example, pattern 2) of the power allocation coefficients corresponding to the one or more REs through an AI encoder, and send the pattern to a terminal device. The input of the AI encoder includes at least the pattern 2 itself, and a vector with a given number of bits (180 bits / 240 bits / 720 bits, etc.) can be output as the output of the AI encoder; the terminal device receives the compressed pattern 2, and can decompress the pattern 2 to obtain a restored pattern 2 through an AI decoder. The AI decoder can input the string of bits, and output the restored pattern 2 (including the power allocation coefficients of all REs). The AI encoder and the AI decoder can be trained in pairs, and a loss function is set as a function related to a reconstruction loss.

[0188] In the present embodiment, the pattern is transmitted over the air through the AI compression / restoration method. Specifically, the pattern can be compressed through an encoder, and the pattern is restored at the opposite end through an AI decoder. Unlike directly transmitting the power allocation coefficients themselves, transmitting the AI compressed pattern greatly reduces the air transmission overhead.

[0189] The method embodiments provided by the embodiments of the present application are described above, and the device embodiments related by the embodiments of the present application are described below. It can be understood that, in order to realize the functions in the above embodiments, the terminal device and the network device include corresponding hardware structures and / or software modules for executing various functions. Those skilled in the art should easily realize that, in combination with the units and method steps of the examples described in the embodiments disclosed in the present application, the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application scene and design constraints of the technical solution.

[0190] Please refer to FIG. 19 and FIG. 20, which are structural diagrams of a communication apparatus provided in the embodiments of the present application. The communication apparatus can be used to implement the functions of the terminal device or the network device in the method embodiments described above, and thus can also achieve the beneficial effects possessed by the method embodiments described above. The communication apparatus can be a terminal device or a network device. The communication apparatus includes modules or units corresponding to the methods / operations / steps / actions performed by the terminal device or the network device in the method embodiments described above. The units can be hardware circuits, software, or a combination of hardware circuits and software. In the embodiments of the present application, the communication apparatus can be the terminal device shown in FIG. 1, the network device shown in FIG. 1, or a module (such as a chip) applied to the terminal device or the network device.

[0191] As shown in FIG. 19, the communication apparatus 1900 can include a processing unit 1901 and a transceiver unit 1902. The communication apparatus 1900 is used to implement the functions of the terminal device or the network device in the method embodiments shown in FIG. 10-FIG. 18.

[0192] When the communication apparatus 1900 is used to implement the functions of the terminal device in the method embodiment shown in FIG. 10, the processing unit 1901 is configured to:

[0193] The transceiver unit 1902 is configured to receive information of a function used to fit the power allocation coefficients corresponding to the plurality of REs, the function being a polynomial function, the information of the function including coefficients corresponding to the function; and determine the power allocation coefficient corresponding to each RE according to the subcarrier index and the function.

[0194] In a possible implementation, the information of the function can further include the type of the function; and if the type of the function is a segmented function, the information of the function can further include a segmented interval.

[0195] In a possible implementation, the information of the function can further include the order corresponding to the function.

[0196] In a possible implementation, the processing unit 1901 is configured to determine the order corresponding to the function according to the number of the coefficients corresponding to the function.

[0197] In a possible implementation, the information of the function includes information of the function corresponding to non-0 elements, and the information of the function can further include subcarrier indexes corresponding to 0 elements and / or near-0 elements.

[0198] In a possible implementation, the function includes a first function and a second function, the first function is used to fit the power allocation coefficients corresponding to the plurality of REs with the column index X, and the second function is used to fit the difference between the column index X-Y and the column index X and the difference between the column index X and the column index X+Z, Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14.

[0199] When the communication apparatus 1900 is configured to implement the functions of the network device in the method embodiment shown in FIG. 10, the following applies:

[0200] The transceiver unit 1902 is configured to send information of a function, the function being used to fit the power allocation coefficients corresponding to the plurality of REs, the function being a polynomial function, and the information of the function including coefficients corresponding to the function.

[0201] In a possible implementation, the information of the function further includes a type of the function, and if the type of the function is a segmented function, the information of the function can further include a segmented interval.

[0202] In a possible implementation, the information of the function can further include an order corresponding to the function.

[0203] In a possible implementation, the information of the function includes information of the function corresponding to non-0 elements, and the information of the function can further include subcarrier indexes corresponding to 0 elements and / or near-0 elements.

[0204] In a possible implementation, the function includes a first function and a second function, the first function being used to fit the power allocation coefficients corresponding to the plurality of REs with column index X, and the second function being used to fit the difference between column index X-Y and column index X and the difference between column index X and column index X+Z, Y being a positive integer less than or equal to X, Z being a positive integer greater than or equal to 1, and X+Z≤14.

[0205] For more detailed description of the processing unit 1901 and the transceiver unit 1902, reference can be made to the related description in the method embodiment shown in FIG. 10.

[0206] When the communication apparatus 1900 is configured to implement the functions of the terminal device in the method embodiment shown in FIG. 17, the following applies:

[0207] The transceiver unit 1902 is configured to receive a first vector, the first vector including a pattern of power allocation coefficients corresponding to one or more REs compressed;

[0208] The processing unit 1901 is configured to decompress the first vector to obtain the power allocation coefficients corresponding to the one or more REs.

[0209] In a possible implementation, the pattern of the power allocation coefficients corresponding to the one or more REs is compressed by an AI encoder, and the processing unit 1901 decompresses the first vector to obtain the power allocation coefficients corresponding to the one or more REs, specifically by decompressing the first vector by an AI decoder to obtain the power allocation coefficients corresponding to the one or more REs.

[0210] In a possible implementation, the AI encoder and the AI decoder are trained in pairs, and a loss function used in the training is a function related to a reconstruction loss, and the related function includes one or more of NMSE, MSE, CS, GCS, or SGCS.

[0211] When the communication apparatus 1900 is used to implement the functions of the network device in the method embodiment shown in FIG. 17:

[0212] The processing unit 1901 is configured to compress the pattern of the power allocation coefficients corresponding to the one or more REs.

[0213] The transceiver unit 1902 is configured to send the first vector, which includes the compressed pattern of the power allocation coefficients corresponding to the one or more REs.

[0214] In a possible implementation, the first vector is decompressed by the AI decoder, and the compressed pattern of the power allocation coefficients corresponding to the one or more REs includes a pattern of the power allocation coefficients corresponding to the one or more REs compressed by the AI encoder.

[0215] In a possible implementation, the AI encoder and the AI decoder are trained in pairs, and a loss function used in the training is a function related to a reconstruction loss, and the related function includes one or more of NMSE, MSE, CS, GCS, or SGCS.

[0216] For more detailed descriptions of the processing unit 1901 and the transceiver unit 1902, refer to the related descriptions in the method embodiment shown in FIG. 17.

[0217] A communication apparatus 2000 is provided as shown in FIG. 20, and is configured to implement the functions of the terminal or the access network device. The apparatus can be a communication device or an apparatus used in a communication device, and the communication device can be a terminal or an access network device. The apparatus used in the communication device can be a chip system or a chip in the communication device. The chip system can be composed of a chip, or can include a chip and other discrete devices.

[0218] The communication apparatus 2000 includes at least one processor 2010 configured to implement the processing functions of the device (for example, the access network device or the terminal) in the methods provided in the embodiments. The communication apparatus 2000 can also include a communication interface 2020 configured to implement the transceiving operations of the device (for example, the access network device or the terminal) in the methods provided in the embodiments. In the embodiments, the communication interface can be a transceiver, a circuit, a bus, a module, or other types of communication interfaces, and is configured to communicate with other devices through a transmission medium. For example, the communication interface 2020 in the communication apparatus 2000 is configured to communicate with other devices. The processor 2010 transceives data by using the communication interface 2020, and is configured to implement the methods described in the above method embodiments.

[0219] The communication device 2000 can further include at least one memory 2030 for storing program instructions and / or data. The memory 2030 is coupled to the processor 2010. The coupling between the apparatuses, units or modules in the embodiments of the present application is indirect coupling or communication connection between the apparatuses, units or modules, which can be electrical, mechanical or other forms, for information interaction between the apparatuses, units or modules. The processor 2010 can operate in cooperation with the memory 2030. The processor 2010 can execute the program instructions stored in the memory 2030. At least one of the at least one memory can be included in the processor.

[0220] The specific connection medium between the communication interface 2020, the processor 2010 and the memory 2030 in the embodiments of the present application is not limited. In FIG. 20, the memory 2030, the processor 2010 and the communication interface 2020 are connected by a bus, which is represented by a thick line in FIG. 20. The connection mode between other components is only illustrative and is not limited. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, only one thick line is used in FIG. 20, but it does not mean that there is only one bus or only one type of bus.

[0221] When the communication device 2000 is specifically a device (for example, an access network device or a terminal) such as a chip or a chip system, the communication interface 2020 can output or receive a baseband signal. When the communication device 2000 is specifically a device (for example, an access network device or a terminal), the communication interface 2020 can output or receive a radio frequency signal. In the embodiments of the present application, the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0222] It should be noted that the communication interface 2020 can be used to execute the functions of the transceiver unit 1902 described above, and the processor 2010 can be used to execute the functions of the processing unit 1901 described above, which will not be described herein.

[0223] When the above communication device is a chip applied to a terminal, the terminal chip implements the functions of the terminal in the above method embodiments, and the terminal chip receives information from other network elements; or the terminal chip sends information to other network elements.

[0224] When the communication device is a chip applied to an access network device, the access network device chip implements the functions of the access network device in the method embodiments. The access network device chip receives information from other network elements; or the access network device chip sends information to other network elements.

[0225] It can be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0226] The method steps in the embodiments of the present application can be realized in the form of hardware or by the processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory (RAM), a flash memory, a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically EPROM (EEPROM), a register, a hard disk, a mobile hard disk, a CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in an access network device or a terminal. Of course, the processor and the storage medium can also exist as discrete components in the terminal or the access network device.

[0227] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer programs or instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments are performed. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer programs or instructions can be stored in a computer readable storage medium or transmitted by the computer readable storage medium. The computer readable storage medium can be any available medium accessible by the computer or a data storage device such as a server integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0228] In various embodiments of the present application, the terms and / or descriptions of different embodiments are consistent and can be referred to each other if there is no special description and logical conflict. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0229] It can be understood that various numerical numbers involved in the embodiments of the present application are only used for differentiation for convenience of description, and are not used to limit the scope of the embodiments of the present application. The size of the serial number of the above processes does not mean the execution order, and the execution order of the processes should be determined according to its function and inherent logic.

[0230] The embodiments of the present application also provide a computer readable storage medium, which stores computer execution instructions, when the computer execution instructions are executed, the method executed by the terminal device or the network device in the above method embodiments is realized.

[0231] The embodiments of the present application also provide a computer program product, which includes a computer program, when the computer program is executed, the method executed by the terminal device or the network device in the above method embodiments is realized. If each component module of the above device is realized in the form of a software function unit and sold or used as an independent product, it can be stored in the computer readable storage medium.

[0232] The embodiments of the present application further provide a chip system, comprising at least one processor and a communication interface, the communication interface and the at least one processor are interconnected through a line, the at least one processor is used to run computer programs or instructions to execute part or all steps of any one of the method embodiments described in the corresponding method embodiments of FIG. 10-FIG. 18. The chip system can be composed of a chip, or can contain a chip and other discrete devices.

[0233] The embodiments of the present application further provide a communication system, which comprises a terminal device or a network device. The specific description can refer to the communication method shown in FIG. 10-FIG. 18.

[0234] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0235] The descriptions of the embodiments of the present application can be referred to each other, and the descriptions of the embodiments are each focused on. The parts not described in detail in a certain embodiment can be referred to the related description of other embodiments. For the convenience and brevity of description, for example, the functions of the devices and the steps executed by the devices provided in the embodiments of the present application can be referred to the related description of the method embodiments of the present application, and the method embodiments and the device embodiments can also be referred to, combined or cited each other.

[0236] It should be appreciated that the memory mentioned in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a hard disk drive (HDD), a solid-state drive (SSD), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, but not limitation, many forms of RAM can be used, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM). The memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory in the embodiments of the present application can also be a circuit or any other device capable of realizing a storage function, used for storing program instructions and / or data.

[0237] It should also be understood that the processor mentioned in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0238] It should be noted that when the processor is a general processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated in the processor.

[0239] It should be noted that the memory described herein is intended to include, but not limited to, these and any other suitable types of memory.

[0240] It should be understood that the size of the sequence number of the above-mentioned processes does not mean the order of execution in various embodiments of the present application, and the execution order of the processes 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.

[0241] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments provided herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those 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.

[0242] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0243] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments 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.

[0244] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0245] 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.

[0246] The functions, if realized in the form of software functional units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods according to the embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, and various other media that can store program codes.

[0247] The steps in the method embodiments of the present application can be adjusted in sequence, combined, and reduced according to actual needs.

[0248] The modules / units in the device embodiments of the present application can be combined, divided, and reduced according to actual needs.

[0249] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A communication method, characterized in that, The method includes: The information of the receiving function is used to fit the power allocation coefficients corresponding to multiple resource elements (REs). The function is a polynomial function, and the information of the function includes the coefficients corresponding to the function. The power allocation coefficient for each RE is determined based on the subcarrier index and the function.

2. The method according to claim 1, characterized in that, The information of the function also includes the type of the function; if the type of the function is a piecewise function, the information of the function also includes the segmented intervals.

3. The method according to claim 1 or 2, characterized in that, The information about the function also includes the order of the function.

4. The method according to claim 1 or 2, characterized in that, The method further includes: The order of the function is determined by the number of coefficients corresponding to the function.

5. The method according to any one of claims 1-4, characterized in that, The information of the function includes information about the function corresponding to the non-zero elements; the information of the function also includes the subcarrier index corresponding to the zero elements and / or near-zero elements.

6. The method according to any one of claims 1-5, characterized in that, The function includes a first function and a second function. The first function is used to fit the power allocation coefficients corresponding to multiple REs with column index X. The second function is used to fit the difference values ​​from column index XY to column index X and from column index X to column index X+Z, where Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14.

7. A communication method, characterized in that, The method includes: Information about the sending function, which is used to fit the power allocation coefficients corresponding to multiple resource elements (REs), wherein the function is a polynomial function, and the information of the function includes the coefficients corresponding to the function.

8. The method according to claim 7, characterized in that, The information of the function also includes the type of the function; if the type of the function is a piecewise function, the information of the function also includes the segmented intervals.

9. The method according to claim 7 or 8, characterized in that, The information about the function also includes the order of the function.

10. The method according to any one of claims 7-9, characterized in that, The information of the function includes information about the function corresponding to the non-zero elements; the information of the function also includes the subcarrier index corresponding to the zero elements and / or near-zero elements.

11. The method according to any one of claims 7-10, characterized in that, The function includes a first function and a second function. The first function is used to fit the power allocation coefficients corresponding to multiple REs with column index X. The second function is used to fit the difference values ​​from column index XY to column index X and from column index X to column index X+Z, where Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14.

12. A communication device, characterized in that, include: A transceiver unit is used to receive information about a function, which is used to fit the power allocation coefficients corresponding to multiple resource elements (REs). The function is a polynomial function, and the information about the function includes the coefficients corresponding to the function. The processing unit is used to determine the power allocation coefficient corresponding to each RE based on the subcarrier index and the function.

13. The apparatus according to claim 12, characterized in that, The information of the function also includes the type of the function; if the type of the function is a piecewise function, the information of the function also includes the segmented intervals.

14. The apparatus according to claim 12 or 13, characterized in that, The information about the function also includes the order of the function.

15. The apparatus according to claim 12 or 13, characterized in that, The processing unit is also used to determine the order of the function based on the number of coefficients corresponding to the function.

16. The apparatus according to any one of claims 12-15, characterized in that, The information of the function includes information about the function corresponding to the non-zero elements; the information of the function also includes the subcarrier index corresponding to the zero elements and / or near-zero elements.

17. The apparatus according to any one of claims 12-16, characterized in that, The function includes a first function and a second function. The first function is used to fit the power allocation coefficients corresponding to multiple REs with column index X. The second function is used to fit the difference values ​​from column index XY to column index X and from column index X to column index X+Z, where Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14.

18. A communication device, characterized in that, include: The transceiver unit is used to transmit information about a function, which is used to fit the power allocation coefficients corresponding to multiple resource elements (REs). The function is a polynomial function, and the information about the function includes the coefficients corresponding to the function.

19. The apparatus according to claim 18, characterized in that, The information of the function also includes the type of the function; if the type of the function is a piecewise function, the information of the function also includes the segmented intervals.

20. The apparatus according to claim 18 or 19, characterized in that, The information about the function also includes the order of the function.

21. The apparatus according to any one of claims 18-20, characterized in that, The information of the function includes information about the function corresponding to the non-zero elements; the information of the function also includes the subcarrier index corresponding to the zero elements and / or near-zero elements.

22. The apparatus according to any one of claims 18-21, characterized in that, The function includes a first function and a second function. The first function is used to fit the power allocation coefficients corresponding to multiple REs with column index X. The second function is used to fit the difference values ​​from column index XY to column index X and from column index X to column index X+Z, where Y is a positive integer less than or equal to X, Z is a positive integer greater than or equal to 1, and X+Z≤14.

23. A communication device, characterized in that, The device includes a processor, a memory, an input interface, and an output interface. The input interface is used to receive information from other communication devices besides the communication device, and the output interface is used to output information to other communication devices besides the communication device. When a stored computer program stored in the memory is invoked by the processor, the method described in any one of claims 1-11 is implemented.

24. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or computer instructions that, when executed by a processor, cause the method described in any one of claims 1-11 to be implemented.

25. A computer program product, characterized in that, The computer program product includes instructions that, when executed by a processor, cause the method as described in any one of claims 1-11 to be implemented.

26. A chip system, characterized in that, The method includes at least one processor, a memory, and an interface circuit, wherein the memory, the interface circuit, and the at least one processor are interconnected by a line, and the at least one memory stores instructions that, when executed by the processor, cause the method as described in any one of claims 1-11 to be implemented.

27. A communication system, characterized in that, It includes a terminal device and a network device, wherein the terminal device is used to implement the method as described in any one of claims 1-6, and the network device is used to implement the method as described in any one of claims 7-11.

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