Communication method and communication apparatus

Abstract

A communication method and a communication apparatus. When a plurality of AI-based prediction tasks are executed by a terminal device, the terminal device determines a prediction result on the basis of the plurality of prediction tasks, wherein the prediction result is obtained by means of executing a certain prediction task among the plurality of prediction tasks, but can represent prediction results of other prediction tasks, and thus the terminal device reports a single prediction result for the plurality of prediction tasks, and the prediction result is also associated with the other prediction tasks, thereby realizing the reporting of a single prediction result for a plurality of prediction tasks, and thus reducing the air interface overhead of terminal devices when sending CSI reports.

Description

Communication method and communication apparatus

[0001] The present application claims priority from the Chinese patent application No. 202411237206.6 filed on September 3, 2024, and entitled "Communication method and communication apparatus", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

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

[0003] The application of artificial intelligence (AI) based beam management mainly lies in two aspects, namely, spatial domain prediction and time domain prediction. In the spatial domain prediction, the input of the AI model is the beam information of a specific pattern scanned at a certain time, usually the reference signal received power (RSRP), and the AI model predicts the information of all beams and selects K beams and related information from all beams to report to the network side. In the time domain prediction, a sliding time window collects the input information of the AI model, the AI model processes the input information and outputs the prediction results of all beams in the time window at a future time (for example, the AI model is a regression model) or the identification of K beams from all beams (for example, the AI model is a classification model), and finally selects K beams and related information to report to the network side, K being a positive integer.

[0004] When multiple prediction tasks are performed simultaneously, the network side usually configures reference signal resources for the terminal device to independently perform the two prediction tasks, and the two prediction tasks do not affect each other, and finally the prediction results of the two prediction tasks are reported. However, the current prediction result reporting mechanism has a large air interface overhead. SUMMARY

[0005] The present application provides a communication method and a communication apparatus, which are beneficial to reduce the air interface overhead.

[0006] In a first aspect, a communication method is provided for execution by a communication apparatus or a module (e.g., a processor, a chip, a circuit, an AI entity, etc., which can also be a logical module, hardware and / or software, etc. capable of realizing all or part of the functions of the communication apparatus) for a communication apparatus, which can correspond to a terminal device in the method embodiments. The method comprises: determining a first prediction result based on a first prediction task and a second prediction task, the first prediction result being obtained based on the first prediction task and being associated with the second prediction task, or the first prediction result being obtained based on the second prediction task and being associated with the first prediction task; and transmitting a channel state information (CSI) report, the CSI report containing the first prediction result.

[0007] In the technical solution of the present application, when there are multiple prediction tasks executed by the terminal device, the terminal device determines a prediction result to be reported based on the multiple prediction tasks. The prediction result is obtained by executing one of the multiple prediction tasks, but can represent the prediction results of other prediction tasks in the multiple prediction tasks, and thus the prediction result is associated with the other prediction tasks. Therefore, when the terminal device transmits the CSI report, one prediction result can be reported for the multiple prediction tasks. Compared with reporting the prediction results of each prediction task respectively, the air interface overhead of the terminal device transmitting the CSI report can be reduced.

[0008] The multiple prediction tasks can be two or more prediction tasks.

[0009] In combination with the first aspect, in some implementation manners of the first aspect, determining the first prediction result based on the first prediction task and the second prediction task comprises: obtaining a second prediction result based on the first prediction task; obtaining a third prediction result based on the second prediction task; and obtaining the first prediction result based on the second prediction result and the third prediction result.

[0010] In this implementation manner, the terminal device respectively obtains the prediction results of two prediction tasks, and determines one prediction result to be reported based on the prediction results of the two prediction tasks.

[0011] In combination with the first aspect, in some implementation manners of the first aspect, the first prediction task corresponds to a first prediction period, and the second prediction task corresponds to an observation time window and a prediction time window; the first prediction result is the prediction result that is closer to the observation time window among a second prediction result and a third prediction result, the second prediction result is a prediction result of the first prediction task in the first prediction period, and the third prediction result is a prediction result of the second prediction task in a second prediction period within the prediction time window, the prediction time window comprising multiple prediction periods, and the second prediction period being one of the multiple prediction periods.

[0012] In some implementations of the first aspect, the method further includes: determining the similarity between the second prediction result and the third prediction result when the length of the prediction time window is less than or equal to a threshold.

[0013] In this implementation, it is considered that the longer the length of the prediction time window of the time domain prediction task (corresponding to the second prediction task) is, the lower the possibility that the prediction result of the time domain prediction task is the same or similar to the prediction result of the space domain prediction task (corresponding to the first prediction task) is, and thus the greater the probability that the prediction result is not suitable for being reported by being merged (or referred to as being reported by being deduplicated or being reported by being de-redundant, etc.). Because in this case, it cannot be guaranteed that the accuracy of the prediction result after being merged is used to represent the prediction results of the multiple prediction tasks, therefore, by limiting the length of the prediction time window of the second prediction task to be less than or equal to a threshold, unnecessary similarity determination of the prediction results of the two prediction tasks can be reduced, and thus even if the similarity determination is performed, the possibility that the two prediction results are not similar is greater, and the two prediction results cannot be reported by being merged. By reducing the unnecessary similarity determination, the amount of computation related to the similarity determination of the terminal device can be reduced.

[0014] In some implementations of the first aspect, the method further includes: the first prediction result is a second prediction result obtained by the first prediction task in a first prediction period, and a third prediction result obtained by the second prediction task in a second prediction period is discarded; or the first prediction result is the third prediction result obtained by the second prediction task in the second prediction period, and the second prediction result obtained by the first prediction task in the first prediction period is discarded.

[0015] In this implementation, when the first prediction task is based on the second prediction result obtained by the first prediction task, it is indicated that between the second prediction result obtained based on the first prediction task and the third prediction result obtained based on the second prediction task, the second prediction result is retained as the reported prediction result, and the third prediction result is discarded; conversely, the third prediction result is retained as the reported prediction result, and the second prediction result is discarded, so as to reduce the air interface overhead of the reported prediction result, and reduce the waste of air interface resources.

[0016] The second aspect provides a communication method, which can be executed by a communication device or a module (for example, a processor, a chip, a circuit, an AI entity, etc., which can also be a logical module, hardware and / or software, etc. that can realize all or part of the functions of the communication device) for the communication device. The communication device can correspond to the network device in the method embodiment. The method includes: receiving a channel state information (CSI) report, the CSI report containing a first prediction result, the first prediction result being obtained based on a first prediction task, and the first prediction result being associated with a second prediction task; or the first prediction result being obtained based on the second prediction task, and the first prediction result being associated with the first prediction task.

[0017] The second aspect is a method of a network side corresponding to the first aspect, and beneficial technical effects can be referred to the description of the first aspect, which will not be repeated.

[0018] Based on the technical solutions of the present application, when the CSI report received by the network device contains the first prediction result for the first prediction task, and the first prediction result is associated with the second prediction task, the network device can obtain the prediction results of the first prediction task and the second prediction task, i.e., the first prediction result. Because, although the first prediction result is obtained based on a certain prediction task, since the prediction result is associated with another prediction task, it indicates that the prediction result can represent the prediction result of the associated prediction task. Therefore, the network device receives one prediction result, which is equivalent to obtaining the prediction results of two prediction tasks.

[0019] In some implementations of the first aspect or the second aspect, the first prediction task corresponds to a first prediction period, and the second prediction task corresponds to an observation time window and a prediction time window; the first prediction result is a prediction result that is closer to the observation time window among a second prediction result and a third prediction result, the second prediction result is a prediction result of the first prediction task in the first prediction period, and the third prediction result is a prediction result of the second prediction task in a second prediction period within the prediction time window, the prediction time window includes a plurality of prediction periods, and the second prediction period is one of the plurality of prediction periods.

[0020] In this implementation, the first prediction task can be a spatial domain prediction, and the second prediction task can be a time domain prediction. When the terminal device determines the first prediction result to be reported based on the prediction results of the two prediction tasks, it can select a prediction result that is closer to the observation time window of the time domain prediction task from the prediction results of the two prediction tasks, thereby saving air interface overhead while ensuring the accuracy of the reported prediction result.

[0021] In some implementations of the first aspect or the second aspect, the first prediction result is associated with the second prediction task, including: the first prediction result is used to represent the prediction result of the second prediction task.

[0022] In the embodiments of the present application, the first prediction result is associated with a prediction task, which can represent one of the following: the first prediction result can be used to represent the prediction result of the associated prediction task; the prediction result of the first prediction result is the same as the prediction result of the second prediction task, or the similarity between the prediction result of the first prediction result and the prediction result of the second prediction task is less than a similarity threshold.

[0023] In some implementations of the first aspect or the second aspect, the first prediction task and the second prediction task satisfy a set condition, which includes: a time interval between a starting time of the first prediction task and a starting time of the second prediction task is less than or equal to a threshold; and a similarity between the second prediction result and the third prediction result is less than or equal to a similarity threshold.

[0024] In this implementation, the prediction tasks for which the prediction results can be reported in a combined manner should satisfy a set condition. For the multiple prediction tasks that satisfy the set condition, the prediction results are reported in the manner of "combined reporting" provided in the present application, so that the air interface overhead can be reduced while the accuracy of the reported prediction results is ensured.

[0025] In addition, if the starting times of the two prediction tasks are the same, for example, the threshold is 0, the execution instructions of the two prediction tasks can be issued at the same time, the reference signal used for beam sweeping (or referred to as reference signal measurement) can be the same, thereby saving signaling overhead and reference signal resources.

[0026] In some implementations of the first aspect or the second aspect, the CSI report further includes prediction results corresponding to the remaining prediction periods in the prediction time window except the second prediction period.

[0027] In this implementation, for the prediction results that can be reported in a combined manner, the prediction results can be for the same or adjacent prediction periods of the two prediction tasks, that is, if a prediction task is a time-domain prediction task, the prediction result of the time-domain prediction task can be multiple prediction results output in the prediction time window, and only one or part of the prediction results coincide with the prediction period of another prediction task (for example, another spatial-domain prediction task) or are different but adjacent. Therefore, the terminal device reports the prediction results of the two prediction tasks in the coinciding or adjacent prediction periods in a combined manner, but reports the prediction results of the two prediction tasks in other prediction periods that are not in the coinciding or adjacent prediction periods separately.

[0028] In some implementations of the first aspect or the second aspect, the first prediction task and the second prediction task satisfy a set condition, which includes: a starting time of the first prediction task is located after a starting time of the second prediction task and before an ending time of the second prediction task; and the prediction time window contains the first prediction period.

[0029] In this implementation, the first prediction task and the second prediction task are asynchronous and parallel, and the triggering time of the first prediction task is after the start of the beam sweeping phase of the second prediction task and before the end of the prediction phase. In this case, the mechanism of combined reporting provided in the present application can be applied.

[0030] In some implementations of the first aspect or the second aspect, the first prediction result is a prediction result of the second prediction task corresponding to the first prediction period.

[0031] In some implementations of the first aspect or the second aspect, the first prediction task and the second prediction task each correspond to an observation time window and a prediction time window, and the first prediction task and the second prediction task satisfy a set condition, the set condition comprising: the first prediction task and the second prediction task correspond to one or more same prediction periods, at any one of the one or more prediction periods, a second prediction result corresponding to the first prediction task and a third prediction result corresponding to the second prediction task are the same, the first prediction result is a prediction result selected from the second prediction result and the third prediction result and closer to the first observation time window, and the first observation time window is a later observation time window in time among the observation time windows corresponding to the first prediction task and the second prediction task.

[0032] In this implementation, both prediction tasks can correspond to time domain prediction tasks. In this implementation, the prediction periods of the two time domain prediction tasks can overlap, so that a prediction result can be reported for both prediction tasks in the overlapping prediction period to reduce air interface overhead. In the overlapping prediction period, the terminal device can only perform one of the prediction tasks, and the other prediction task can not be performed, so that one prediction result is obtained for both prediction tasks, thereby saving air interface resources.

[0033] In some implementations of the first aspect or the second aspect, the first prediction task and the second prediction task are periodic, or the first prediction task and the second prediction task are aperiodic.

[0034] In some implementations of the first aspect or the second aspect, the one or more prediction periods are determined based on information of the observation time windows and the prediction time windows corresponding to the first prediction task and the second prediction task.

[0035] In this implementation, when both prediction tasks are periodically executed, the prediction periods of the two prediction tasks can be determined by the lengths of the observation time windows and the prediction time windows of the two prediction tasks.

[0036] In some implementations of the first aspect or the second aspect, the CSI report comprises a prediction result of the first prediction task in a prediction period other than the one or more prediction periods in the corresponding first prediction time window, or the CSI report comprises a prediction result of the second prediction task in a prediction period other than the one or more prediction periods in the corresponding second prediction time window.

[0037] In the implementation, if both the two prediction tasks are time domain prediction tasks, prediction results of other prediction time periods of the two prediction tasks other than the overlapping prediction time period also need to be reported.

[0038] In a third aspect, a communication apparatus is provided. The communication apparatus has the function of implementing the method in the first aspect or any possible implementation of the first aspect. The function can be implemented by hardware, or by software, or by a combination of hardware and software. The hardware or software includes one or more units corresponding to the functions described above.

[0039] In a fourth aspect, a communication apparatus is provided. The communication apparatus has the function of implementing the method in the second aspect or any possible implementation of the second aspect. The function can be implemented by hardware, or by software, or by a combination of hardware and software. The hardware or software includes one or more units corresponding to the functions described above.

[0040] In a fifth aspect, a communication apparatus is provided. The communication apparatus includes at least one processor configured to cause the communication apparatus to perform the method in the first aspect or any possible implementation of the first aspect; or perform the method in the second aspect or any possible implementation of the second aspect; or perform the method in the third aspect or any possible implementation of the third aspect. Optionally, the at least one processor is coupled to at least one memory for storing computer programs or instructions, and the at least one processor is configured to call and run the computer programs or instructions from the at least one memory, so that the communication apparatus performs the method in the first aspect or any possible implementation of the first aspect; or performs the method in the second aspect or any possible implementation of the second aspect. Optionally, the at least one processor can be included in the communication apparatus or configured outside the communication apparatus. Optionally, the communication apparatus further includes the at least one memory. Optionally, the communication apparatus further includes a communication interface.

[0041] In a sixth aspect, a communication apparatus is provided. The communication apparatus includes a communication interface and a circuit. The communication interface is configured to receive a signal to be processed, and transmit the signal to the circuit. The circuit is configured to process the signal to perform the method in the first aspect or any possible implementation of the first aspect; or perform the method in the second aspect or any possible implementation of the second aspect. Optionally, the communication interface is further configured to output the signal processed by the circuit. As an example, the communication interface can be a transceiver, a hardware circuit, a bus, a module, a pin, or other types of communication interfaces. The signal includes information and / or data. Optionally, the communication apparatus can be a chip.

[0042] In a seventh aspect, a computer readable storage medium is provided, having computer program codes or instructions stored therein, which when executed on a computer, cause the method according to the first aspect or any possible implementation thereof to be implemented; or the method according to the second aspect or any possible implementation thereof to be implemented.

[0043] In an eighth aspect, a computer program product is provided, comprising computer program codes or instructions, which when executed on a computer, cause the method according to the first aspect or any possible implementation thereof to be implemented; or the method according to the second aspect or any possible implementation thereof to be implemented.

[0044] In a ninth aspect, a wireless communication system is provided, comprising the communication device according to the third aspect and the communication device according to the fourth aspect. BRIEF DESCRIPTION OF DRAWINGS

[0045] FIG. 1 is a schematic diagram of a communication system suitable for embodiments of the present application.

[0046] FIG. 2 is another schematic diagram of a communication system suitable for embodiments of the present application.

[0047] FIG. 3 is a schematic diagram of a possible application framework in a communication system.

[0048] FIG. 4 is a schematic diagram of another possible application framework in a communication system.

[0049] FIG. 5 is a schematic diagram of a prediction procedure of BM case 1.

[0050] FIG. 6 is a schematic diagram of a prediction procedure of BM case 2.

[0051] FIG. 7 is a schematic diagram of a communication method 700 provided by the present application.

[0052] FIG. 8 is a schematic diagram of an application scenario suitable for embodiments of the present application.

[0053] FIG. 9 is an example of a communication method provided by the present application.

[0054] FIG. 10 is a schematic diagram of another application scenario suitable for embodiments of the present application.

[0055] FIG. 11 is another example of a communication method provided by the present application.

[0056] FIG. 12 is a schematic diagram of yet another application scenario suitable for embodiments of the present application.

[0057] FIG. 13 is yet another example of a communication method provided by the present application.

[0058] FIG. 14 is a schematic block diagram of a communication apparatus 1000 provided by the present application.

[0059] FIG. 15 is a schematic block diagram of another communication apparatus 1100 provided by the present application.

[0060] FIG. 16 is a schematic structural diagram of a chip provided by the present application.

[0061] FIG. 17 is a schematic diagram of a system architecture of a chip provided by the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0063] The technical solutions provided by 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, etc. In addition, it 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, future communication system or fusion system of multiple systems, etc.

[0064] A network element in a communication system can send a signal to another network element or receive a signal from another network element. The signal can include information, signaling or 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. The network element is taken as an example for description in the embodiments of the present application. 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.

[0065] FIG. 1 is a schematic diagram of a communication system applicable to embodiments of the present application. As shown in FIG. 1, the communication system 100 can include at least one network device, such as the network device 110 shown in FIG. 1, and at least one terminal device, such as the terminal device 120 and the terminal device 130 shown in FIG. 1. The network device 110 and the terminal devices (such as the terminal device 120 and the terminal device 130) can communicate with each other through wireless links. The communication devices in the communication system, for example, the network device 110 and the terminal device 120, can communicate with each other through multi-antenna technology.

[0066] Optionally, the communication system can further include at least one AI node.

[0067] FIG. 2 is another schematic diagram of a communication system applicable to embodiments of the present application. 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, such as constructing a training data set, training an AI model, or inference of the AI model, etc.

[0068] In an implementation manner, the network device 110 can send data related to AI model training to the AI network element 140, and the AI network element 140 can construct a training data set and train an AI model. As an example, the data related to AI model training can include data reported by the terminal device. The AI network element 140 can send the result of the AI model-related operation to the network device 110 and forward it to the terminal device through the network device 110. 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, etc. As an 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. Optionally, 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 only illustrates the case 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 one or more of the network device 110 and the terminal device through a third-party network element. Embodiments of the present application do not limit the connection relationship between the AI network element and other network elements.

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

[0071] It should be noted that FIG. 1 and FIG. 2 are merely schematic diagrams drawn for the purpose of understanding, and other devices can also be included in the communication system, for example, wireless relay devices and / or wireless backhaul devices, etc., which are not shown in FIG. 1 and FIG. 2. In addition, in actual applications, the communication system can include multiple network devices and / or multiple terminal devices. The number of network devices and terminal devices is not limited in the embodiments of the present application.

[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. 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 terminal devices are: a mobile phone, a tablet computer, 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., and the embodiments of the present application are not limited thereto.

[0073] 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 be used with other devices, such as a smart phone, such as various smart wristbands and smart jewelry for monitoring vital signs.

[0074] 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 corresponding function, such as a processor, a circuit, or a chip, etc. The device can be configured in the terminal device or used with the terminal device. In embodiments of the present application, only the device for implementing the function of the terminal device is taken as an example to illustrate the terminal device, which does not limit the schemes of the embodiments of the present application.

[0075] The network device in the embodiments of the present application can be a device for communicating with a terminal device, and can be a radio access network (RAN) node, such as a base station, for accessing a terminal device to a wireless network. The base station can broadly cover various names or replace the following names: Node B (Node B), evolved Node B (eNB), next generation Node B (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), primary station, secondary station, multi-standard radio (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), and the like. 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 for being disposed in the foregoing devices or apparatuses. The base station can also be a mobile switching center, a device assuming a base station function in D2D, V2X, M2M communication, a device assuming a base station function in a future communication system, and the like. The base station can support networks of the same or different access technologies. Alternatively, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, and the like. 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 adopted by the network device.

[0076] 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 act as a device communicating with another base station.

[0077] In some deployments, the network device mentioned in 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.

[0078] In some deployments, wireless access is assisted by a plurality of 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.

[0079] In a possible design, a processing unit in a BBU for implementing baseband functions is referred to as a base band high (BBH) unit, and a processing unit in an RRU / AAU / RRH for implementing baseband functions is referred to as a base band low (BBL) unit.

[0080] 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 an 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 the present application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0081] In embodiments of the present application, the apparatus for implementing the functions of the network device can be a network device; or can be an apparatus capable of supporting the network device to implement the corresponding functions, such as a processor, a circuit or a chip, etc. The apparatus can be configured in the network device, or used in combination with the network device. In embodiments of the present application, only the apparatus for implementing the functions of the network device is taken as an example for illustration, and the scheme of embodiments of the present application is not limited.

[0082] 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 scene where the network device and the terminal device are located is 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 form of the terminal device and the network device is not limited in the present application.

[0083] 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. Alternatively, the AI node can also be deployed separately, for example, 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.

[0084] The number of AI nodes is not limited in the present application. 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.

[0085] Optionally, 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 special hardware, or can be a virtualized function instantiated on a platform (for example, a cloud platform). The specific form of the AI node is not limited in the present application. The AI node can also be referred to as an AI network element or an AI module.

[0086] FIG. 3 is a schematic diagram of a possible application framework in a communication system. As shown in FIG. 3, the network elements in the communication system are connected through interfaces (for example, NG, Xn) or air interfaces. One or more AI modules (only 1 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 OAM. 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 the 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 the CU-UP.

[0087] 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: a structure parameter (for example, at least one of a neural network layer number, a neural network width, a connection relationship between layers, a neuron weight, a neuron activation function, or a bias in the activation function), an input parameter (for example, a type of input parameter and / or a dimension of the input parameter), or an output parameter (for example, a type of output parameter and / or a dimension of the output parameter). The bias in the activation function can also be referred to as a bias of the neural network.

[0088] One AI module can have one or more models. One model can infer an output including one parameter or multiple parameters. The learning process, the training process, or the inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0089] The network device can be a network device provided with one or more AI modules, for example, the network device can be one or more of the core network device, the access network node (RAN node), or the OAM shown in FIG. 3. The AI module can be a RAN intelligent controller (RIC) shown in FIG. 4, 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, the DU), and the non-real-time RIC is arranged in the OAM, the cloud server, the core network device, or other network devices.

[0090] FIG. 4 is a schematic diagram of another possible application framework in a communication system. As shown in FIG. 4, the communication system includes a RIC. For example, the RIC can be an AI module in the RAN node shown in FIG. 1, 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 in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, which can be in the order of tens of milliseconds.

[0091] The near-real-time RIC is used for model training and inference. For example, the AI model is trained, and inference is performed using the AI model. The near-real-time RIC can obtain network-side and / or terminal-side information from a RAN node (e.g., a CU, a CU-CP, a CU-UP, a DU, and / or a RU) and / or a terminal. The information can be used as training data or data for inference.

[0092] Optionally, the near-real-time RIC can submit the inference result to the RAN node and / or the terminal.

[0093] Optionally, the inference result can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the near-real-time RIC submits the inference result to the DU, and the DU sends the inference result to the RU.

[0094] The non-real-time RIC is also used for model training and inference. For example, the AI model is trained, and inference is performed using the AI model. The non-real-time RIC can obtain network-side and / or terminal-side information from a RAN node (e.g., a CU, a CU-CP, a CU-UP, a DU, and / or a RU) and / or a terminal. The information can be used as training data or data for inference, and the inference result can be submitted to the RAN node and / or the terminal.

[0095] Optionally, the inference result can be exchanged between the CU and the DU, and / or between the DU and the RU. For example, the non-real-time RIC submits the inference result to the DU, and the DU sends the inference result to the RU.

[0096] The near-real-time RIC and the non-real-time RIC can also be separately arranged as a device. Optionally, 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 is arranged in a RAN node (e.g., a CU, a DU), and the non-real-time RIC is arranged in an OAM, a cloud server, a core network device, or other devices.

[0097] Optionally, the AI model can be implemented in hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include program code, programs, subprograms, instructions, instruction sets, code, code segments, software modules, applications, or software applications.

[0098] In order to facilitate the understanding of the embodiments of the present application, the related technologies involved are introduced as follows.

[0099] In order to realize beam management, methods such as hierarchical scanning can be used to reduce the overhead of beam scanning, for example, first scanning a wide beam, and then scanning part of the narrow beam under the wide beam. The selection of the beam is mainly completed through the reference signal and the corresponding beam measurement. The reference signal mainly includes a synchronization signal block (SSB) and a channel state information-reference signal (CSI-RS). The SSB is a cell broadcast signal, which contains a primary synchronization signal (PSS), a secondary synchronization signal (SSS), a physical broadcast channel (PBCH), and a de-modulation reference signal (DMRS). The SSB is periodically transmitted according to the cell configuration, and its function is not only used for beam management, but also for initial access, time-frequency synchronization, etc. Simply, the SSB signal can be considered as a wide beam signal. Correspondingly, the CSI-RS signal is a UE-level signal, and the network side configures one or more groups of CSI-RS signals for the UE according to the actual situation. Similarly, the CSI-RS signal is not only used for beam management, but also for channel quality measurement, etc. The CSI-RS signal can be understood as a narrow beam signal.

[0100] The conventional beam management system performs two-step beam scanning in the serving beam selection stage: the first stage scans the SSB (i.e., scans the wide beam), in which the UE measures and reports the reference signal received power (RSRP) of the SSB beam to the network side; the second stage is that the network side screens out the SSB beam with the maximum RSRP according to the RSRP of the SSB beam reported by the UE, and configures the CSI-RS signal, which is used for the terminal device to scan the narrow beam under the SSB beam with the maximum RSRP to determine the best beam.

[0101] In recent years, artificial intelligence (AI) has played a great role in beam management, especially in reducing the overhead of beam sweeping. Generally, the AI model takes the received power of wide beams or sparsely scanned narrow beams measured by the UE as input, and the AI model infers the output of the top-K best narrow beams, which can be referred to as Top-K candidate beams, as an example, the RSRP value or ID of the beam set can be output. The network side performs scanning according to the Top-K candidate beams to finally determine the best beam, and K is a positive integer equal to or greater than 1. Generally, the AI model can be deployed on the UE side or the network side.

[0102] The configuration of CSI-RS resources and the feedback of CSI reporting support three configuration modes of periodic, semi-static and aperiodic. Among them, in the periodic CSI-RS configuration, the network side configures the transmission period (for example, every n slots, n is a positive integer) and offset (symbol offset within the period) of CSI and indicates it to the UE, and transmits according to the configured period and offset. In the semi-static CSI-RS configuration, the network side configures the transmission period and offset of CSI and indicates the UE, but whether to actually transmit is determined by the medium access control-control element (MAC CE). The MAC CE can activate or deactivate the transmission of CSI-RS and notify the UE. In the aperiodic CSI-RS configuration, the network side signals the transmission of each CSI-RS to the UE through downlink control information (DCI), and the configuration of aperiodic CSI-RS also supports the transmission of multiple CSI RS resources configured at a time.

[0103] At present, the application of AI beam management (BM) mainly includes two aspects, namely spatial domain prediction and time domain prediction, which can be denoted as BM case 1 and BM case 2 respectively.

[0104] FIG. 5 is a schematic diagram of the prediction process of BM case 1. The input of the AI model is the beam information (usually the RSRP value) of a certain pattern scanned at a certain time, and the set of this beam information is referred to as set B (set B); after AI model prediction, the complete beam set output is referred to as set A (set A), and the top-K beams and their related information are selected and reported to the network side.

[0105] FIG. 6 is a schematic diagram of a prediction process of BM case 2. A sliding time window is used to collect input information of the AI model, such as the RSRP of set B from time (t-N+1) to time (t) shown in FIG. 6. Time (t-N+1) to time (t) corresponds to the observation time window T1. The AI model processes the input information to output the prediction result of the future time window. The future time window is shown as time (t+1) to time (t+M) in FIG. 6, also denoted as time window T2. If the AI model is a regression model, the prediction result is set A; if the AI model is a classification model, the prediction result is the ID of the top-K beams. Accordingly, the terminal device finally selects the top-K beams and their related information, or selects the beam ID of the top-K beams to report to the network side. top-K(t+1) represents the prediction result at time (t+1), which is the top-K beams output by the AI model.

[0106] The current AI model supports both BM case 1 and BM case 2 prediction tasks. When multiple tasks are performed simultaneously, the gNB usually configures the CSI-RS resource to independently indicate the UE to perform BM case 1 and BM case 2 prediction, and the prediction tasks do not affect each other, and finally each reports its prediction result.

[0107] As described in the background section, when multiple cases are involved in parallel prediction tasks, if the mechanism of reporting the prediction results of each prediction task separately is continued, it will cause a large air interface overhead and waste of air interface resources.

[0108] The present application proposes a scheme for reducing air interface overhead for the combined reporting (or "de-duplication" or "de-redundancy" reporting) of UE-side AI models for similar multiple prediction tasks such as BM case 1 and BM case 2 described above. In the embodiments of the present application, combined reporting can mean that for multiple prediction results that are the same or similar, only one prediction result is reported, rather than reporting all the same or similar prediction results. Assuming that the reported prediction result is obtained based on a first prediction task in multiple prediction tasks, the prediction result and other prediction tasks except the first prediction task are associated in the CSI report. For the network side, after obtaining the CSI report, the prediction result is obtained therefrom, and the association between the prediction result and other prediction tasks is obtained, so that the prediction results of the multiple prediction tasks are also obtained, or in other words, the one prediction result represents the prediction results of the multiple prediction tasks. Thus, one prediction result is reported for multiple prediction tasks, thereby reducing the air interface overhead.

[0109] Optionally, in this application, the prediction task based on the AI model is taken as an example for description, and in other training or inference tasks based on AI, if one network element needs to send multiple task information and / or data to another network element, the multiple task information and / or data can also be sent based on the technical solution provided in this application, for example, sending the information and / or data of one task and associating it with other tasks, so as to realize sending for multiple tasks once, thereby saving air interface overhead.

[0110] The time period or time unit in the embodiments of this application can be understood as any one or more of a time slot, a subframe, a frame, an OFDM symbol, or a longer time period with one of them as a granularity. For example, the prediction period corresponding to the prediction task A can represent a time slot or a subframe or a frame or an OFDM symbol corresponding to the prediction task A, or a time period with a time slot, a subframe, a frame or an OFDM symbol as a granularity, for example, 2 time slots or 4 time slots as a time period or time unit, without limitation.

[0111] In the embodiments of this application, the input codebook (referred to as set B) and the output full codebook (referred to as set A) of the AI model used by each prediction task are the same.

[0112] The technical solution provided in this application will be described in detail below.

[0113] FIG. 7 is a schematic flowchart of a communication method 700 provided in this application. The method 700 involves a network device and a terminal device. Optionally, the communication device (for example, the network device or the terminal device) involved in the method 700 can also be replaced by an apparatus for the communication device, for example, the network device can be replaced by a first apparatus, which can be a chip, a processor, a circuit applied to communication, or an AI entity serving the network device, and the AI entity can be deployed on the network device or outside the network device. As an example, the AI entity can be an over the top (OTT) server or a cloud server. The terminal device can also be replaced by a corresponding apparatus, which will not be described again. In the following embodiments, the network device and the terminal device are taken as examples for description.

[0114] 710, the terminal device determines a first prediction result based on the first prediction task and the second prediction task.

[0115] The first prediction result is obtained based on the first prediction task, and the first prediction result is associated with the second prediction task; or the first prediction result is obtained based on the second prediction task, and the first prediction result is associated with the first prediction task.

[0116] As described above, the present application mainly relates to the case of multiple prediction tasks, wherein the first prediction task and the second prediction task can refer to two different prediction tasks. In addition, the embodiments of the present application only take two prediction tasks as an example, and based on the same concept, the scheme of the embodiments of the present application is also applicable when there are more than two prediction tasks. The two prediction tasks can be two different prediction tasks of the same type, for example, two different time domain prediction tasks, or two different types of prediction tasks, for example, a spatial domain prediction task and a time domain prediction task.

[0117] In the embodiments of the present application, the first prediction result is associated with a prediction task, which can mean that the first prediction result can be used to represent the prediction result of the prediction task. In addition, the first prediction result is obtained based on another prediction task, for example, the first prediction result is obtained based on the first prediction task, and the first prediction result is associated with the second prediction task, which means that the first prediction task is based on the prediction result obtained by the first prediction task, and the first prediction task can be used to represent the prediction result of the second prediction task. Or, the first prediction result is obtained based on the second prediction task, and the first prediction result is associated with the first prediction task, which means that the first prediction result is based on the prediction result obtained by the second prediction task, and the first prediction result can be used to represent the prediction result of the first prediction task. In other words, one prediction result reflects the prediction results of two prediction tasks.

[0118] In one implementation, the terminal device obtains a second prediction result based on the first prediction task, and obtains a third prediction result based on the second prediction task. The terminal device obtains the first prediction result based on the second prediction result and the third prediction result. For example, the first prediction result can be one of the second prediction result and the third prediction result. Wherein, the first prediction result is the second prediction result or the third prediction result, which is related to the specific situation of the first prediction task and the second prediction task, which will be described in detail below.

[0119] In another implementation, the terminal device can determine the first prediction result based on the result of one prediction task.

[0120] For example, the terminal device obtains a second prediction result based on the first prediction task, and determines that the second prediction result can be used to represent the prediction result of the second prediction task based on the related information of the first prediction task and the second prediction task, such as respective observation periods, prediction periods, and the like. At this time, the second prediction result is the first prediction result. For another example, the terminal device obtains a third prediction result based on the second prediction task, and determines that the third prediction result can be used to represent the prediction result of the first prediction task based on the related information of the first prediction task and the second prediction task. At this time, the third prediction result is the first prediction result. In this implementation manner, the terminal device obtains the prediction result of one of the two prediction tasks as the first prediction result, without obtaining the prediction result of the other prediction task.

[0121] In general, when the prediction result obtained based on one prediction task can represent the prediction result of another prediction task, the prediction result is the first prediction result in the embodiments of the present application.

[0122] Optionally, one prediction result (for example, the first prediction result described above) that can be used to represent the prediction result of one prediction task can include that the one prediction result can be used to represent the prediction result corresponding to one prediction period of the one prediction task, or represent the prediction results corresponding to multiple prediction periods of the one prediction task, without limitation. When the prediction result is used to represent the prediction result corresponding to one prediction period of the one prediction task, the prediction result can be associated with the identifier of the prediction period; when the prediction result is used to represent the prediction results corresponding to multiple prediction periods of the one prediction task, the prediction result can be associated with the identifiers of the multiple prediction periods, to indicate that the prediction results corresponding to the multiple prediction periods of the one prediction task can all be represented by the one prediction result.

[0123] 720. The terminal device sends a CSI report, and the CSI report contains the first prediction result.

[0124] As can be known from the description in step 710, when the prediction result obtained based on one prediction task can also be used to represent the prediction result of another prediction task, the terminal device can include one prediction result in the CSI report sent to the network side, instead of including one respective prediction result for the two prediction tasks. In other words, the first prediction result in the CSI report indicates that the first prediction result is the prediction result obtained by the terminal device performing a certain prediction task, and the first prediction result can also be used as (or represent) the prediction result of the associated another prediction task.

[0125] Based on the technical solutions of the present application, when multiple prediction tasks are executed by the terminal device, the terminal device can report only one prediction result based on the multiple prediction tasks, which is obtained by executing one of the multiple prediction tasks, but can reflect or represent the prediction results of the multiple prediction tasks. It can be seen that, compared with reporting the prediction results of each prediction task respectively, the air interface overhead of the terminal device sending the CSI report can be reduced.

[0126] Reporting one prediction result for multiple prediction tasks requires that some conditions are met between the multiple prediction tasks, and based on the differences between the prediction tasks, the conditions met between the multiple prediction tasks are also different, which will be described in detail below in conjunction with examples.

[0127] As described above, the current application of AI in beam management mainly involves spatial domain prediction and time domain prediction, so the following embodiments are described taking these two types of prediction as examples. When AI can be applied to more types of prediction in the future, the technical solutions in the embodiments of the present application can also be applicable to similar technical problems.

[0128] For the sake of simplicity in description, the following embodiments take the first prediction task as spatial domain prediction and the second prediction task as time domain prediction as examples.

[0129] Example 1

[0130] The following describes a scenario in which the first prediction task and the second prediction task are simultaneously issued, in conjunction with FIG. 8.

[0131] FIG. 8 is a schematic diagram of an application scenario applicable to the embodiments of the present application. In this scenario, the network device simultaneously issues two prediction tasks to the terminal device. The prediction time periods corresponding to the two prediction tasks are not completely the same, but are relatively close, which means that the two prediction tasks have a certain probability of outputting similar prediction results, so that the method provided by the present application can be considered for reporting the prediction results to reduce the air interface overhead of reporting. For example, in FIG. 8, at a certain time of time slot t, the UE receives instructions from the network device to execute prediction task 1 and prediction task 2. After receiving the instructions, the UE starts to execute prediction task 1 and prediction task 2. After obtaining the input information of the AI model, prediction task 1 performs model prediction and outputs the prediction result of prediction task 1. After passing through an observation time window, prediction task 2 performs model prediction and outputs the prediction result of prediction task 2. It can be seen that the prediction time period of prediction task 1 and the prediction time period of prediction task 2 are not completely the same, but if the prediction time window of prediction task 2 is relatively close to the prediction time period of prediction task 1, the two prediction tasks can output the same or similar prediction results.

[0132] FIG. 9 is an example of the communication method provided by the present application.

[0133] 301. The UE reports capability information.

[0134] The capability information is used to indicate the type of prediction task supported by the AI model deployed on the UE side. As an example, if the AI model deployed on the UE side supports both spatial prediction and temporal prediction, the UE can indicate both types of prediction in the capability information reported by the UE.

[0135] Optionally, the UE can also send a request message. The request message is used to indicate that the UE can start to perform the prediction task. In this way, the network device can issue the prediction task supported by the AI model on the UE side based on the request message of the UE, for example, issue multiple prediction tasks. The multiple prediction tasks can be issued synchronously or asynchronously, etc.

[0136] Step 301 is an optional step, for example, when the UE reports the capability information to the network side, the network device saves the capability information of the UE. In the subsequent AI-based prediction task, the UE does not need to report its own capability information every time. Unless the type of prediction task supported by the UE changes, the UE can indicate the changed capability information to the network device.

[0137] 302. The network device configures the reference signal resource, the instruction for performing the first prediction task and the second prediction task, and the similarity threshold of the prediction result of the first prediction task and the prediction result of the second prediction task.

[0138] Optionally, the configuration of the reference signal resource, the instruction for performing the prediction task, and the similarity determination threshold can be configured by one signaling or multiple signaling, which is not limited. The instruction for performing the first prediction task and the second prediction task can be one execution or two instructions, which is also not limited.

[0139] It should be understood that the network device sets a similarity threshold for the terminal device to determine whether the prediction results of the first prediction task and the second prediction task are similar or the degree of similarity, and the setting of the similarity threshold is case-dependent. As an example, if the AI model on the UE side is a classification model, it means that the result output by the AI model is top-K candidate beams. In this implementation, the network device sets a threshold M. If the number of coincidences of the top-K beams predicted by the first prediction task and the second prediction task is greater than M, it can be determined that the prediction result of the first prediction task and the prediction result of the second prediction task are similar, otherwise they are not similar. It should be understood that the number of top-K candidate beams of the two prediction tasks can be equal or not equal. For example, the best candidate beams of the two prediction tasks are both K. For another example, K of the first prediction task is set as K1, and K of the second prediction task is set as K2, which is not limited. As another example, if the AI model on the UE side is a regression model, the prediction result output by the AI model is the RSRP value of the full codebook, that is, set A. In this implementation, the network device sets a threshold H. The terminal device calculates the root mean squared error (RMSE) of the set A output by the first prediction task and the second prediction task, and if the RMSE is less than the threshold H, it can be determined that the prediction results of the two prediction tasks are similar, otherwise they are not similar.

[0140] Considering that the length of the observation time window of the second prediction task is configurable, and the first prediction task is usually predicted at the task issuance time, the longer the observation time window of the second prediction task is, the farther the first output result (that is, the first prediction result) in the prediction time window of the second prediction task is from the output time of the first prediction task (that is, the time when the first prediction task outputs the prediction result), the lower the possibility that the prediction results output by the two prediction tasks are similar, and the lower the necessity of similarity determination. In order to avoid unnecessary similarity determination, when the length of the observation time window of the second prediction task is equal to or greater than W, the terminal device does not determine the similarity of the prediction results of the two prediction tasks, and W is an integer. As an example, W is equal to 4.

[0141] It should be noted that the second prediction task is a time domain prediction task, which usually corresponds to an observation time window (also referred to as a measurement time window or an input time window) and a prediction time window (also referred to as an output time window). The observation time window refers to a time interval in which the terminal device performs beam sweeping to obtain input information of an AI model. For example, in the observation time window, the UE measures the reference signal configured by the network device, wherein the reference signal corresponds to a part of all beams, and thus is a sparse beam. After the UE completes the scanning of the reference signal, measurement information corresponding to the sparse beam is obtained, such as the RSRP of the sparse beam, and this part of the measurement information is input to the AI model deployed on the UE side. In the prediction time window, the AI model outputs a prediction result based on the measurement information of the sparse beam. As an example, the observation time window can include one or more time periods (which can be referred to as observation time periods), and the prediction time window can include one or more time periods (which can be referred to as prediction time periods). The one or more observation time periods and the one or more prediction time periods can be one-to-one corresponding, for example, in the order of time, the prediction result corresponding to the first prediction time period in the prediction time window, or in other words, the first output result in the prediction time window, is obtained by predicting the input information obtained based on the first observation time period; the prediction result corresponding to the second prediction time period is obtained by predicting the input information obtained based on the second observation time period, and so on.

[0142] 303. The network device sends a reference signal resource for the UE to perform measurement on the reference signal resource.

[0143] Alternatively, the reference signal resource can also be referred to as a beam or a beam resource. The process of measuring the reference signal resource by the UE can also be referred to as beam sweeping.

[0144] 304. The UE performs beam sweeping to obtain a first measurement information set.

[0145] The first measurement information set is the measurement information of the scanned beam obtained by the UE in the first observation time period corresponding to the first prediction task, such as RSRP. It should be understood that in example 1, the starting time of the first prediction task and the second prediction task is the same, and the first prediction task is a spatial domain prediction task and the second prediction task is a time domain prediction, so the first observation time period corresponding to the first prediction task is one observation time period in the observation time window corresponding to the second prediction task. Therefore, the measurement information in the first measurement information set is the input information of the AI model of the first prediction task, and also part of the input information of the AI model of the second prediction task.

[0146] 305. The UE obtains a prediction result corresponding to the first prediction task based on the AI model and the first measurement information set.

[0147] The UE takes the measurement information contained in the first measurement information set as the input of the AI model, performs prediction, and obtains a prediction result corresponding to the first prediction task. In order to distinguish from the first prediction result described above, it is recorded as a second prediction result.

[0148] 306. The UE performs beam scanning to obtain a second measurement information set.

[0149] As described in step 304, the UE performs beam scanning in the first prediction period to obtain a part of the input information of the AI model of the second prediction task; in step 306, the UE performs beam scanning in the remaining period of the first prediction period to obtain another part of the input information of the AI model of the second prediction task, that is, the second measurement information set. Therefore, the sum of the first measurement information set and the second measurement information set is the input information of the AI model of the second prediction task. In addition, the reference signal resources for the first prediction task and the second prediction task can be the same, the first prediction task measures the reference signal resources in the first observation period, and the second prediction task measures the reference signal resources in each observation period within the prediction time window.

[0150] 307. The UE obtains a prediction result corresponding to the second prediction task based on the AI model, the first measurement information set and the second measurement information set.

[0151] As described above, the prediction time window of the second prediction task can include one or more prediction periods, and each prediction period outputs a prediction result, so there is one or more prediction results. The following embodiments take the one-to-one correspondence between multiple prediction periods and multiple prediction results as an example.

[0152] 308. The UE performs similarity determination on the prediction result of the first prediction task and the prediction result output by the second prediction task in the prediction time window.

[0153] As an example, the UE performs similarity determination on the second prediction result corresponding to the first prediction task and the multiple prediction results corresponding to the second prediction task one by one. If the second prediction result is similar to a certain prediction result among the multiple prediction results corresponding to the second prediction task, the second prediction result and the certain prediction result are deduplicated. Specifically, the prediction result farther away from the observation time window of the second prediction task is discarded, and the prediction result closer to the observation time window of the second prediction task is retained. In addition, an association is established between the prediction task corresponding to the retained prediction result and the discarded prediction result. For example, a task identifier can be configured for each of the two prediction tasks, and the task identifier corresponding to the retained prediction result and the discarded prediction result is associated through the task identifier. For example, if the second prediction result corresponding to the first prediction task is farther away from the observation time window of the second prediction task, the second prediction result corresponding to the first prediction task is discarded, and a third prediction result corresponding to the second prediction task closer to the observation time window of the second prediction task is retained, and the retained third prediction result is associated with the first prediction task. It should be understood that in this example, the certain prediction result of the second prediction task similar to the second prediction result is actually a prediction result corresponding to a certain prediction period within the prediction time window corresponding to the second prediction task. For the convenience of description, in the embodiments of the present application, the prediction result corresponding to a certain prediction period obtained based on the second prediction task and used for similarity determination with the second prediction result corresponding to the first prediction task is referred to as a third prediction result, and the prediction period corresponding to the third prediction result is referred to as a second prediction period, in order to distinguish from the above-mentioned first prediction period. Therefore, the terminal device performs similarity determination on the second prediction result corresponding to the first prediction task and the third prediction result corresponding to the second prediction task, and if similar, one prediction result closer to the prediction time window of the second prediction task is retained from the second prediction result and the third prediction result, and the other is discarded. Further, the retained prediction result and the discarded prediction result are associated with the prediction task corresponding to the retained prediction result and the discarded prediction result, for example, if the second prediction result is retained, the second prediction result is associated with the second prediction task; if the third prediction result is retained, the third prediction result is associated with the first prediction task. The third prediction result is a prediction result corresponding to any one of the prediction periods within the prediction time window corresponding to the second prediction task. As an example, it is assumed that the prediction time window corresponding to the second prediction task includes multiple prediction results, denoted as prediction result 1, prediction result 2, …, prediction result M.If the second prediction result corresponding to the first prediction task and a prediction result x corresponding to the second prediction task are similar, and the prediction result x is farther away from the observation time window of the second prediction task, the prediction result x is discarded, the second prediction result corresponding to the first prediction task is retained, and the second prediction result and the second prediction task are associated, for example, the second prediction result and the second prediction task are associated, and the prediction period x can be marked to indicate that the second prediction result is the prediction result x corresponding to the prediction period x of the second prediction task. In an example, the third prediction result can be the prediction result corresponding to the first prediction period in the prediction time window.

[0154] 309. According to the similarity determination result of the prediction result of the first prediction task and the prediction result of the second prediction task, the CSI report is sent.

[0155] In a possible case, if the prediction result of the first prediction task and a certain prediction result of the second prediction task are similar, the CSI report contains the retained prediction result, and the association relationship between the retained prediction result and another prediction task. In addition, the prediction results of other prediction periods of the second prediction task are reported.

[0156] In another possible case, if the prediction result of the first prediction task and the prediction result of the second prediction task are not similar, the UE reports respectively for the first prediction task and the second prediction task.

[0157] In example 1, through similarity determination, if the prediction result of the first prediction task and the prediction result of the second prediction task are similar, the reporting of one prediction result of the terminal device can correspond to two prediction tasks, and the air interface overhead caused by repeated transmission of the prediction result can be reduced. In addition, if the network device indicates two prediction tasks through one instruction, the overhead of the reference signal can be saved. In addition to this, the similarity determination mechanism of the prediction results of different prediction tasks can also achieve the balance between the efficiency and accuracy of the prediction result reporting.

[0158] Compared with the method of reporting the prediction results respectively by the UE in the case of simultaneously issuing the first prediction task and the second prediction task by the network side, the technical scheme of the present application adds a similarity determination mechanism of the prediction results after completing the prediction based on the AI model, and judges the possible similar prediction results. For similar prediction results, the reporting is combined, so that the air interface transmission overhead can be reduced.

[0159] Example 2

[0160] Involving the asynchronous parallel scene of the first prediction task and the second prediction task, which is explained below in conjunction with 10.

[0161] FIG. 10 is a schematic diagram of another application scenario applicable to the embodiments of the present application. As shown in FIG. 10, the UE receives the first prediction task issued by the network device when performing the second prediction task. In this scenario, the prediction periods of the two prediction tasks can be the same. If the UE detects the first prediction period corresponding to the first prediction task within the prediction time window corresponding to the second prediction task, the UE retains one of the two prediction results obtained in the same prediction period according to the similarity determination result when configuring the CSI report, discards the other, and indicates the correlation between the retained prediction result and the other prediction task to the network device.

[0162] FIG. 11 is another example of the communication method provided by the present application.

[0163] 501. The UE reports capability information.

[0164] For details, refer to the description of step 301.

[0165] 502. The network device configures a second prediction task and a reference signal resource corresponding to the second prediction task, denoted as reference signal resource 2.

[0166] The network device configuring the second prediction task can include the network device issuing an instruction to the UE to perform the second prediction task and related information of the second prediction task.

[0167] 503. The network device sends the reference channel resource 2 for the UE to perform beam sweeping corresponding to the second prediction task.

[0168] 504. The UE performs beam sweeping (i.e., measures the reference signal resource 2) in each observation period of the observation time window corresponding to the second prediction task to obtain a first measurement information set.

[0169] The first measurement information set is the measurement information of the scanned beam obtained by performing beam sweeping in the observation time window corresponding to the second prediction task, such as the RSRP of the scanned beam. For details, refer to the description of step 304.

[0170] 505. The UE enters the prediction phase of the second prediction task.

[0171] Step 505 indicates that the UE enters the prediction phase from the beam sweeping phase after obtaining the input information of the AI model. In other words, the prediction phase starts.

[0172] 506. The network device configures a first prediction task and a reference signal resource corresponding to the first prediction task, denoted as reference signal resource 1.

[0173] 507. The network device sends the reference signal resource 1.

[0174] The UE performs measurement on the reference signal resource 1 and outputs the prediction result of the first prediction task at the first prediction period. The first prediction period of the first prediction task is denoted as period (t) in the following.

[0175] It can be seen that the trigger instruction of the first prediction task (i.e. the instruction indicating the UE to perform the first prediction task) is after the beginning of the prediction phase of the second prediction task and before the end of the prediction phase of the second prediction task.

[0176] 508、The UE records the first prediction period corresponding to the first prediction task. Taking FIG. 10 as an example, the prediction period corresponding to the first prediction task corresponds to period (t). The UE retrieves the timestamp information of the prediction result of the second prediction task. If the timestamp information contains period (t), the UE discards the prediction result obtained by the first prediction task at period (t) and the prediction result farther away from the observation time window of the second prediction task in the prediction result of the second prediction task corresponding to period (t), and retains the prediction result closer to the observation time window of the second prediction task. In addition, the retained prediction result is associated with another prediction task. Here, the other prediction task refers to the prediction task corresponding to the discarded prediction result. It should be understood that the timestamp information of the prediction result of the second prediction task is retrieved, i.e. whether the prediction time window of the second prediction task contains the first prediction period, because each prediction result of the second prediction task corresponds to a timestamp information indicating the prediction period of the prediction result.

[0177] As an example, the UE can discard the prediction result farther away from the observation time window of the second prediction task after obtaining the prediction result of the first prediction task and the prediction result of the second prediction task and comparing. As another example, the UE receives the instruction to perform the first prediction task during the execution of the prediction phase of the second prediction task. By retrieving the timestamp information of the prediction result of the second prediction task, if a prediction period corresponding to the first prediction period of the first prediction task, e.g. the second prediction period, is found, the UE can not perform the first prediction task, but directly retain the prediction result of the second prediction task at the second prediction period, and associate the retained prediction result with the first prediction task.

[0178] It can be seen that in the embodiment, the trigger time of the first prediction task needs to be after the beginning of the scanning phase of the second prediction task and before the end of the prediction phase of the second prediction task, so as to correspond to the same period. In step 508, if the prediction result of period (t) is retrieved in the second prediction task, the UE does not perform the first prediction task, which can further reduce the additional overhead.

[0179] 509、The second prediction task is completed.

[0180] 510、UE sends the CSI report, wherein the CSI report contains the reserved prediction result, and the association relationship between the reserved prediction result and the prediction task corresponding to the discarded prediction result.

[0181] In this embodiment, in the case of the same prediction period, the reporting of the prediction result of one time corresponds to two prediction tasks, reducing the air interface overhead caused by repeated transmission of the prediction result. In addition, the first prediction task omits the prediction process based on the AI model at the same time corresponding to the second prediction task, further saving the additional overhead.

[0182] Example 3

[0183] The following describes a scenario involving the simultaneous execution of two second prediction tasks (whether they are simultaneously issued or not).

[0184] FIG. 12 is a schematic diagram of another application scenario applicable to the embodiments of the present application. As shown in FIG. 12, in one possible case, if both the second prediction tasks are triggered and periodically executed based on periodic or semi-persistent reference signals, such as periodic / semi-persistent CSI RS, and the lengths of the observation time windows and the prediction task windows of the two second prediction tasks are different, the two second prediction tasks can have overlapping prediction periods. For example, as shown in FIG. 12, the observation time window and the prediction time window of the second prediction task 1 are one time unit, for example, one time slot, and are alternately performed; and the observation time window and the prediction time window of the second prediction task 2 are two time units, for example, two time slots. The terminal device can know through calculation that every 4 time units, the prediction results of the two second prediction tasks will be repeated. In another possible case, if both the second prediction tasks are triggered and executed based on aperiodic reference signals, such as aperiodic CSI RS, the method described in Example 2 above can be used to determine whether the prediction results can be reported after being repeated.

[0185] FIG. 13 is another example of the communication method provided by the present application.

[0186] 601、UE reports capability information.

[0187] Reference is made to step 301, which is not described herein.

[0188] 602、The network device configures the second prediction task 1 and the reference signal resource corresponding thereto, denoted as reference signal resource 1.

[0189] 603、The network device sends the reference signal resource 1.

[0190] 604、UE scans the reference signal resource 1 in the second prediction task 1 observation time window, and obtains the measurement information of the reference signal resource 1 in each observation period, for example, RSRP. The measurement information of the reference signal resource 1 is the input information of the AI model of the second prediction task 1. The ellipsis after step 604 indicates that the observation stage includes scanning of the reference signal resource 1 in multiple observation periods.

[0191] 605、UE enters the prediction stage of the second prediction task 1.

[0192] 606、The network device configures a second prediction task 2 and its corresponding reference signal resource, denoted as reference signal resource 2.

[0193] 607、The network device sends the reference signal resource 2.

[0194] 608、UE scans the reference signal resource 2 in the observation time window of the second prediction task 2, and obtains the measurement information of the reference signal resource 2 in each observation period of the second prediction task 2. The measurement information of the reference signal resource 2 is the input information of the AI model of the second prediction task 2.

[0195] 609、UE enters the prediction stage of the second prediction task 2.

[0196] 610、UE calculates the overlapping prediction period of the two second prediction tasks according to the information of the observation time window of each second prediction task and the information of the prediction time window. The overlapping prediction period can be one or more.

[0197] It should be understood that when both second prediction tasks are periodically executed, the overlapping prediction period occurs once every period T. For example, in FIG. 12, the overlapping period T is 4 time units. The calculation period T is limited to the case where both second prediction tasks are periodically executed. If at least one second prediction task is not periodically executed, the method of retrieving the overlapping prediction period (or retrieving the timestamp information of the prediction result) in example 2 is still used to query whether there is an overlapping prediction period for the two second prediction tasks, so that the prediction results of the two second prediction tasks are de-duplicated at the overlapping prediction time.

[0198] 611、In the overlapping prediction period, UE performs similarity determination on the prediction results of the two prediction tasks respectively, and if the determination result is similar, discards the set of prediction results farther away from the first prediction time window, and retains the set of prediction results closer to the first observation time window. The first prediction time window is the observation time window that is more time-lagged in the observation time window corresponding to each of the two second prediction tasks.

[0199] It should be understood that in this example, since both prediction tasks are time-domain prediction, in the overlapping period, each second prediction task can correspond to a set of prediction results, containing one or more prediction results. Therefore, compared with the other embodiments described above, in this embodiment, one of the second prediction tasks is discarded (or retained) in the set of prediction results corresponding to the overlapping period.

[0200] 612, the UE completes the prediction task, i.e., completes the two second prediction tasks.

[0201] 613, the UE sends a CSI report to the network device, and the CSI report includes a set of prediction results in the overlapping period. The set of prediction results is obtained based on one of the two second prediction tasks, and the set of prediction results of the other prediction task in the overlapping period is discarded. The set of retained prediction results is associated with the second prediction task corresponding to the discarded set of prediction results. In addition, the CSI report also includes prediction results of other prediction periods of the two second prediction tasks outside the overlapping period.

[0202] In Example 3, in the case where the prediction periods corresponding to the prediction results overlap, the terminal device performs deduplication processing on the prediction results in the overlapping prediction period. Specifically, if the prediction results of the two second prediction tasks in the overlapping prediction period are similar, the prediction result obtained by one of the second prediction tasks is discarded, so that in the overlapping prediction period, one prediction result is reported for the two prediction tasks, reducing the air interface overhead caused by repeated transmission of the same or similar prediction results.

[0203] In addition, optionally, Example 3 is taken as an example in which the prediction results of the two prediction tasks are obtained respectively, and then the prediction results are deduplicated. In another alternative, after the UE obtains the configuration information of the two second prediction tasks, it has obtained the information of the observation time window and the prediction time window of each of the two second prediction tasks, so that the overlapping prediction period can be determined by calculation. In the subsequent execution process of the second prediction task, in each overlapping prediction period, the UE can omit the prediction process based on the AI model corresponding to a certain prediction task. That is, in the overlapping prediction period, the UE only performs the inference process of the model for one second prediction task, and the obtained prediction result is reported as the retained prediction result, while being associated with the other second prediction task which does not perform the inference. In this way, additional overhead can be further saved. In the scenario of periodic execution of prediction tasks, the overlapping period T is introduced, so that deduplication reporting is more convenient and efficient.

[0204] In addition, in the above embodiments or examples, the CSI report contains the prediction results of the merged reporting of the plurality of prediction tasks, and the terminal device also reports to the network device for other prediction results of the plurality of prediction tasks that cannot be reported by merging. As an example, for the prediction results of each prediction task that cannot be reported by merging, a CSI report of each prediction task can be sent. The CSI report of each prediction task can be configured by the network side.

[0205] The communication method provided by the present application is described in detail above, and the corresponding communication device is introduced below.

[0206] FIG. 14 is a schematic block diagram of a communication device 1000 provided by the present application. As shown in FIG. 14, the communication device 1000 can include a processing module 1001 and a communication module 1002. The communication device 1000 can be a terminal device, or a communication device applied to or matched with the terminal device and capable of realizing the corresponding functions of the terminal device, such as a processor, a chip, a circuit, or an AI entity. Alternatively, the communication device 1000 can be a network device, or a communication device applied to or matched with the network device and capable of realizing the corresponding functions of the network device, such as a processor, a chip, a circuit, or an AI entity.

[0207] The communication module can also be referred to as a transceiver module, a transceiver, a transceiver, or a transceiver device. The processing module can also be referred to as a processor, a processing board, a processing unit, or a processing device. Optionally, the communication module is used to perform the sending operation and the receiving operation of the terminal device side or the network device side in the above method, and the device in the communication module for realizing the receiving function can be regarded as a receiving unit, and the device in the communication module for realizing the sending function can be regarded as a sending unit, that is, the communication module includes a receiving unit and a sending unit.

[0208] When the communication device 1000 is applied to a terminal device, the processing module 1001 can be used to realize the processing functions of the terminal device in each of the embodiments described in FIGS. 7-13, and the communication module 1002 can be used to realize the transceiver functions of the terminal device in each of the embodiments described in FIGS. 7-10.

[0209] For example, the processing module 1001 is configured to determine a first prediction result based on a first prediction task and a second prediction task, the first prediction result is obtained based on the first prediction task, and the first prediction result is associated with the second prediction task; or the first prediction result is obtained based on the second prediction task, and the first prediction result is associated with the first prediction task; and the communication module 1002 is configured to send a channel state information (CSI) report, the CSI report containing the first prediction result.

[0210] For another example, the processing module 1001 is configured to: obtain a second prediction result based on the first prediction task; obtain a third prediction result based on the second prediction task; and obtain the first prediction result based on the second prediction result and the third prediction result.

[0211] For another example, the processing module 1001 is configured to: determine the similarity between the second prediction result and the third prediction result in a case where the length of the prediction time window is less than or equal to a set threshold. The prediction time window can refer to the description in the foregoing method embodiments.

[0212] For another example, the processing module 1001 is configured to: discard the third prediction result obtained by the second prediction task in the second prediction period in a case where the first prediction result is the second prediction result obtained by the first prediction task in the first prediction period; or discard the second prediction result obtained by the first prediction task in the first prediction period in a case where the first prediction result is the third prediction result obtained by the second prediction task in the second prediction period.

[0213] When the communication apparatus 1000 is applied to a network device, the processing module 1001 can be configured to implement the processing functions of the network device in each of the embodiments of FIGS. 7-13, and the communication module 1002 can be configured to implement the transceiving functions of the network device in each of the embodiments of FIGS. 7-13.

[0214] For example, the communication module 1002 is configured to: receive a channel state information (CSI) report, wherein the CSI report contains a first prediction result, the first prediction result is obtained based on the first prediction task, and the first prediction result is associated with the second prediction task; or the first prediction result is obtained based on the second prediction task, and the first prediction result is associated with the first prediction task.

[0215] For another example, the processing module 1001 is configured to determine the prediction result corresponding to each of the first prediction task and / or the second prediction task based on the CSI report.

[0216] In addition, it needs to be noted that the foregoing communication module and / or processing module can be implemented by a virtual module, for example, the processing module can be implemented by a software function unit or a virtual device, and the communication module can be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented by an entity device, for example, if the device is implemented by a chip / chip circuit, the communication module can be an input / output circuit and / or a communication interface, which performs an input operation (corresponding to the foregoing receiving operation) and an output operation (corresponding to the foregoing sending operation); and the processing module is an integrated processor or a microprocessor or an integrated circuit.

[0217] The division of the modules in the present application is illustrative, and is only a logical function division. In actual implementation, another division manner can be used. In addition, the function modules in each example in the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of a software function module, or in the form of a combination of hardware and software.

[0218] FIG. 15 is a schematic block diagram of another communication apparatus 1100 provided in the present application. Optionally, the communication apparatus 1100 can be a chip or a chip system. Optionally, in the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices.

[0219] The communication apparatus 1100 can be used to implement the functions of any one of the network elements (for example, a network device or a terminal device) described in the foregoing embodiments. The communication apparatus 1100 can include at least one processor 1110. Optionally, the processor 1110 is coupled with a memory, which can be located in the communication apparatus 1100, or the memory can be integrated with the processor, or the memory can also be located outside the communication apparatus 1100. As an example, the communication apparatus 1100 can further include at least one memory 1120. The memory 1120 stores computer programs (or computer instructions) and / or data necessary for implementing the corresponding functions of any one of the network elements in any one of the method embodiments described above; the processor 1110 can execute the computer programs stored in the memory 1120 to complete the method implemented by any one of the network elements in any one of the method embodiments described above.

[0220] The communication apparatus 1100 can further include a communication interface 1130, and the communication apparatus 1100 can exchange information with other devices through the communication interface 1130. As an example, the communication interface 1130 can be a transceiver, a circuit, a bus, a module, a pin or other types of communication interfaces. When the communication apparatus 1100 is a chip-type apparatus or a circuit, the communication interface 1130 in the apparatus 1100 can also be an input-output circuit, which can input information (or receive information) and output information (or send information). The processor is an integrated processor or a microprocessor or an integrated circuit or a logic circuit, and the processor can determine the output information according to the input information.

[0221] The coupling in the present application is an indirect coupling or communication connection between devices, units or modules, which can be electrical, mechanical or other forms, for information interaction between devices, units or modules. The processor 1110 can operate in cooperation with the memory 1120 and the communication interface 1130. The specific connection medium between the processor 1110, the memory 1120 and the communication interface 1130 in the present application is not limited.

[0222] Optionally, as shown in FIG. 12, the processor 1110, the memory 1120 and the communication interface 1130 are connected with each other through a bus 1140. The bus 1140 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one bus 1140 is represented by a line in FIG. 12, but it does not mean that there is only one bus or only one type of bus.

[0223] In an implementation manner, the communication apparatus 1100 can be applied to a network side, for example, a network device in the embodiments of the present application, or a host or a cloud device in an OTT system. Specifically, the communication apparatus 1100 can be a network device, and can also be a device capable of supporting the network device to implement the corresponding functions of the network device in the above-described any one method embodiment. The memory 1120 stores computer programs (or computer instructions) and / or data for implementing the corresponding functions of the network device. The processor 1110 can execute the computer programs or instructions stored in the memory 1120 to complete the method performed by the network device in the above-described any method embodiment. The communication interface in the communication apparatus 1100 can be used to interact with a terminal device, for example, receive a CSI report from the terminal device, etc.

[0224] In another implementation manner, the communication apparatus 1100 can be applied to a terminal side. For example, the communication apparatus 1100 can be a terminal device, and can also be a device capable of supporting the terminal device to implement the corresponding functions of the terminal device in the above-described any one method embodiment. The memory 1120 stores computer programs (or computer instructions) and / or data for implementing the corresponding functions of the terminal device in the above-described any one method embodiment. The processor 1110 can execute the computer programs stored in the memory 1120 to complete the method performed by the terminal device in the above-described any method embodiment. The communication interface in the communication apparatus 1100 can be used to interact with a network device (for example, a base station), for example, send information to the network device or receive information from the network device, for example, send a CSI report to the network device, etc.

[0225] FIG. 16 is a schematic structural diagram of a chip provided in the present application. The chip 30 includes a circuit 31 and a communication interface 32. The circuit 31 can be a logic circuit, an integrated circuit, etc., and the communication interface 32 can also be referred to as an input / output circuit, an input / output interface, an interface circuit, etc., and can input information (or receive information) or output information (or send information). The chip 30 can perform the method performed by the network device or the terminal device in the embodiments of the present application.

[0226] In the present application, the processor can be a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. The general processor can be a microprocessor or any conventional processor, etc.

[0227] In the present application, the memory can be a non-volatile memory such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., and can also be a volatile memory such as a random-access memory (RAM). The memory can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but is not limited to this. The memory in the present application can also be a circuit or any other device capable of realizing a storage function, used for storing computer programs and / or data.

[0228] FIG. 17 is a schematic diagram of a system architecture of a chip provided in the present application. In the figure, an input / output control is used to manage input and output signals of a communication device (such as a network device or a terminal device), for example, the input / output control can be in the form of a modem, a keyboard, a mouse, a touch screen, etc. The input / output control can also be part of a processor. A receiver / transmitter is used to communicate with other devices, and the receiver / transmitter can include a modem for modulating information (transmitting side device) or demodulating modulated information (receiving side device). An antenna is used to transmit or receive signals. A storage can be used to store computer code that can be executed by a processor to implement corresponding functions of the communication device. The processor can include intelligent hardware devices such as a general-purpose processor, a digital signal processor (DSP), a central processing unit (CPU), a field-programmable gate array (FPGA), a graphics processing unit (GPU), a neural processing unit (NPU), etc. The chip provided in FIG. 17 can be used to implement corresponding functions of the network device or the terminal device in the embodiments of the present application.

[0229] In addition, the present application further provides a computer readable storage medium, wherein computer instructions are stored in the computer readable storage medium, and when the computer instructions are run on a computer, operations and / or processes performed by a terminal device or a network device in the methods of the embodiments of the present application are executed.

[0230] The present application further provides a computer program product, wherein the computer program product includes computer program codes or instructions, and when the computer program codes or instructions are run on a computer, operations and / or processes performed by a terminal device or a network device in the methods of the embodiments of the present application are executed.

[0231] The present application further provides a chip, wherein the chip includes a processor, a memory for storing computer programs is provided independently of the chip, and the processor is used to execute the computer programs stored in the memory, so that operations and / or processes performed by a terminal device or a network device in any one of the methods are executed. Further, the chip can further include a communication interface. The communication interface can be an input / output interface, or an interface circuit, etc. Further, the chip can further include a memory.

[0232] The application also provides a chip, which can include a circuit and an input / output interface. The circuit can be a logic circuit, an integrated circuit, etc. For example, the circuit can be one or more processors, or all or part of the circuit in the one or more processors for implementing one or more of processing, control, or calculation functions. The input / output interface can also be an input / output circuit, or an interface circuit that can input (or receive) and / or output (or send) information. The chip can include a chip system. Alternatively, the chip system can be composed of the chip, or can include the chip and other discrete devices. The chip can be used to implement the method performed by the terminal device or the network device in the embodiments of the application. Alternatively, the chip can be a baseband chip, also known as a modem.

[0233] In addition, the application provides a communication system including the terminal device and the network device in any one of the embodiments of the application. The communication system can implement the communication method provided in any one of the embodiments of FIGS. 7-13.

[0234] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device, and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0235] The processor in the embodiments of the application has a signal processing capability, and can be a central processing unit (CPU), and can also be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and can implement or execute the disclosed methods, steps, and logic block diagrams in the application. The general processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method.

[0236] In the embodiments of the present application, the memory is any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer, but is not limited to this. The memory in the present application can also be a circuit or any other device capable of realizing a storage function, used to store computer programs and / or data; or can also be a circuit or any other device capable of realizing a storage function, used to store computer programs and / or data. As an example, the memory 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 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 are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM) and direct rambus RAM (DR RAM). It should be noted that the memory of the system and method described herein is intended to include but not limited to the above types or any other suitable types of memory.

[0237] The technical solutions provided in the present application can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the technical solutions can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal device, an access network device or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as digital video disc (DVD)), or semiconductor media, etc.

[0238] At least one (item) involved in the embodiments of the present application means one (item) or more (items). More (items) means two (items) or more than two (items). "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone.

[0239] The term "comprising" mentioned in the embodiments of the present application and any variation thereof is intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally includes other steps or units not listed, or optionally includes other steps or units inherent to the process, method, product or device.

[0240] In the present application, the methods and / or terms between the method embodiments can be mutually referred to each other without logical contradiction, for example, the functions and / or terms between the device embodiments can be mutually referred to each other, for example, the functions and / or terms between the device examples and the method examples can be mutually referred to each other.

[0241] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0242] 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 device embodiments described above are only schematic. The division of the units is only a logical function division. In actual implementation, there can be 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.

[0243] 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, i.e. they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0244] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0245] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art 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 a number of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0246] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A communication method characterized by comprising: The method comprises: determining a first prediction result based on a first prediction task and a second prediction task, the first prediction result being obtained based on the first prediction task and being associated with the second prediction task, or the first prediction result being obtained based on the second prediction task and being associated with the first prediction task; sending a channel state information (CSI) report, the CSI report comprising the first prediction result.

2. The method of claim 1, wherein, The determining of the first prediction result based on the first prediction task and the second prediction task comprises: obtaining a second prediction result based on the first prediction task; obtaining a third prediction result based on the second prediction task; obtaining the first prediction result based on the second prediction result and the third prediction result.

3. The method of claim 2, wherein, The first prediction task corresponds to a first prediction period, the second prediction task corresponds to an observation time window and a prediction time window, the first prediction result is a prediction result that is closer to the observation time window among the second prediction result and the third prediction result, the second prediction result is a prediction result corresponding to the first prediction period of the first prediction task, the third prediction result is a prediction result corresponding to a second prediction period within the prediction time window of the second prediction task, the prediction time window comprises a plurality of prediction periods, and the second prediction period is one of the plurality of prediction periods.

4. The method of claim 3, wherein, The first prediction task and the second prediction task satisfy a set condition, and the set condition comprises: a time interval between a start time of the first prediction task and a start time of the second prediction task is less than or equal to a threshold value; a similarity between the second prediction result and the third prediction result is less than or equal to a similarity threshold value.

5. The method according to claim 3 or 4, characterized in that, The CSI report further comprises prediction results corresponding to remaining prediction periods within the prediction time window except for the second prediction period.

6. The method according to any one of claims 3 to 5, characterized in that, The method further comprises: in a case where a length of the prediction time window is less than or equal to a set threshold value, determining the similarity between the second prediction result and the third prediction result.

7. The method according to any one of claims 1 to 3, characterized in that, The first prediction task and the second prediction task satisfy a set condition, and the set condition comprises: a start time of the first prediction task is located after a start time of the second prediction task and before an end time of the second prediction task; and the prediction time window comprises the first prediction period.

8. The method of claim 7, wherein, The first prediction result is a prediction result corresponding to the first prediction period of the second prediction task.

9. The method of claim 1, wherein, The first prediction task and the second prediction task each correspond to an observation time window and a prediction time window, and the first prediction task and the second prediction task satisfy a set condition, and the set condition comprises: The first prediction task and the second prediction task correspond to the same one or more prediction periods, at any one of the one or more prediction periods, a second prediction result corresponding to the first prediction task and a third prediction result corresponding to the second prediction task are the same, the first prediction result is a prediction result selected from the second prediction result and the third prediction result and closer to a first observation time window, and the first observation time window is a later observation time window in time among the observation time windows corresponding to the first prediction task and the second prediction task respectively.

10. The method of claim 9, wherein, The first prediction task and the second prediction task are periodic, or the first prediction task and the second prediction task are aperiodic.

11. The method according to claim 9 or 10, characterized in that, The one or more prediction periods are determined based on information of the observation time windows corresponding to the first prediction task and the second prediction task respectively and the prediction time window.

12. The method according to any one of claims 9 to 11, characterized in that, The CSI report includes prediction results of the first prediction task in other prediction periods in a corresponding first prediction time window except the one or more prediction periods, or the CSI report includes prediction results of the second prediction task in other prediction periods in a corresponding second prediction time window except the one or more prediction periods.

13. The method according to any one of claims 3 to 11, characterized in that, The method further includes: The first prediction result is the second prediction result obtained by the first prediction task in the first prediction period, and the third prediction result obtained by the second prediction task in the second prediction period is discarded. Or, The first prediction result is the third prediction result obtained by the second prediction task in the second prediction period, and the second prediction result obtained by the first prediction task in the first prediction period is discarded.

14. A communication method, comprising: Including: Receiving a channel state information (CSI) report, the CSI report containing a first prediction result, the first prediction result being obtained based on a first prediction task and being associated with a second prediction task, or the first prediction result being obtained based on the second prediction task and being associated with the first prediction task.

15. The method of claim 14, wherein, The first prediction result is obtained based on a second prediction result and a third prediction result, the second prediction result being obtained based on the first prediction task, and the third prediction result being obtained based on the second prediction task.

16. The method of claim 15, wherein, The first prediction task corresponds to a first prediction period, and the second prediction task corresponds to an observation time window and a prediction time window; the first prediction result is a prediction result closer to the observation time window among the second prediction result and the third prediction result, the second prediction result is a prediction result corresponding to the first prediction period of the first prediction task, and the third prediction result is a prediction result corresponding to a second prediction period in the prediction time window of the second prediction task, the prediction time window including a plurality of prediction periods, and the second prediction period being one of the plurality of prediction periods.

17. The method of claim 16, wherein, The first prediction task and the second prediction task satisfy a set condition, and the set condition includes: a time interval between a starting time of the first prediction task and a starting time of the second prediction task is less than or equal to a threshold value; a similarity between the second prediction result and the third prediction result is less than or equal to a similarity threshold value.

18. The method of claim 16 or 17, wherein, the CSI report further comprises prediction results corresponding to remaining prediction periods within the prediction time window other than the second prediction period.

19. The method of any one of claims 14-16, wherein, the first prediction task and the second prediction task satisfy a set condition, the set condition comprising: a starting time of the first prediction task is after a starting time of the second prediction task and before an ending time of the second prediction task; and the prediction time window comprises the first prediction period.

20. The method of claim 19, wherein, the first prediction result is a prediction result corresponding to the first prediction period of the second prediction task.

21. The method of claim 14, wherein, the first prediction task and the second prediction task each correspond to one observation time window and one prediction time window, the first prediction task and the second prediction task satisfy a set condition, the set condition comprising: the first prediction task and the second prediction task correspond to one or more prediction periods, in any one of the one or more prediction periods, a second prediction result corresponding to the first prediction task and a third prediction result corresponding to the second prediction task are the same, the first prediction result is a prediction result selected from the second prediction result and the third prediction result which is closer to a first observation time window, the first observation time window is a later observation time window in time among the observation time windows corresponding to the first prediction task and the second prediction task respectively.

22. The method of claim 21, wherein, the first prediction task and the second prediction task are periodic, or the first prediction task and the second prediction task are aperiodic.

23. The method of claim 21 or 22, wherein, the one or more prediction periods are determined based on information of the observation time windows and the prediction time windows corresponding to the first prediction task and the second prediction task respectively.

24. The method of any one of claims 21-23, wherein, the CSI report comprises prediction results of the first prediction task in a corresponding first prediction time window for other prediction periods other than the one or more prediction periods, or the CSI report comprises prediction results of the second prediction task in a corresponding second prediction time window for other prediction periods other than the one or more prediction periods.

25. The method of any one of claims 16-23, wherein, the method further comprises: the first prediction result is the second prediction result obtained by the first prediction task in the first prediction period, and the third prediction result obtained by the second prediction task in the second prediction period is discarded; or the first prediction result is the third prediction result obtained by the second prediction task in the second prediction period, and the second prediction result obtained by the first prediction task in the first prediction period is discarded.

26. A communications device, characterized by include a module or unit for implementing the method of any one of claims 1-13, or include a module or unit for implementing the method of any one of claims 14-25.

27. A communications device, characterized by comprising at least one processor for executing computer programs or instructions stored in a memory so that the method of any one of claims 1-13 is performed, or so that the method of any one of claims 14-25 is performed.

28. A chip, characterized by comprising a circuit and a communication interface for receiving a signal to be processed and sending the signal to be processed to the circuit; the circuit is configured to process the received signal so that the method of any one of claims 1-13 is performed, or so that the method of any one of claims 14-25 is performed.

29. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer programs or instructions, which, when running on a communication device, cause the communication device to perform the method of any one of claims 1-13, or cause the communication device to perform the method of any one of claims 14-25.

30. A computer program product, characterised in that, The computer program product comprises computer program codes or instructions for performing the method of any one of claims 1-13, or comprises computer program codes or instructions for performing the method of any one of claims 14-25.

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