Method performed by network node in communication system and network node

Abstract

The embodiment of the invention provides a method executed by a network node in a communication system and the network node, and relates to the field of artificial intelligence. The method comprises the following steps: determining a first resource allocation pattern corresponding to at least one cell; and adjusting the first resource allocation pattern of the at least one cell based on the inter-cell interference related information to obtain a second resource allocation pattern corresponding to the at least one cell. Optionally, the method performed by the electronic device may be performed using an artificial intelligence model.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method executed by a network node in a communication system and a network node. Background Art

[0002] In the 5G system, the distributed unit (DU) of the base station periodically sends a communication prediction request to the centralized unit (CU). After receiving the communication prediction request, the CU predicts which time domain resources will have data traffic in the future by collecting information such as the reference signal (RS) information, the physical resource block (PRB) utilization rate, and the service load from the DU, that is, predicting the resource allocation pattern (pattern) (also known as the energy-saving pattern), and feeding back the prediction results to the DU. Thereafter, the DU performs energy-saving operations based on the received prediction results (i.e., the resource allocation pattern), specifically, the time domain resource allocation of the centralized data traffic, that is, the data traffic dispersed in the time dimension is concentrated as much as possible in the same time domain for transmission, so as to increase more idle time domains; then, the cell resources of the idle time domain are shut down, that is, the radio unit (Radio Unit) device shuts down the power amplifier.

[0003] How to generate a resource allocation pattern more reasonably, and then better allocate resources to meet communication needs, is a technical problem that technicians in this field have been working hard to study. Summary of the invention

[0004] In order to at least solve the above problems existing in the prior art, the present invention provides a method executed by a network node and a network node.

[0005] According to a first aspect of an embodiment of the present application, a method performed by a first network node in a communication system is provided, including: determining a first resource allocation pattern corresponding to at least one cell; adjusting the first resource allocation pattern of the at least one cell based on interference-related information between cells to obtain a second resource allocation pattern corresponding to the at least one cell.

[0006] Optionally, the first resource allocation pattern includes a movable level corresponding to the resource unit, and the movable level indicates a level of resource adjustment of the resource unit in the time domain.

[0007] Optionally, the mobility level is related to at least one of the following: service load, service priority, and delay-related characteristics of the service.

[0008] Optionally, determining a first resource allocation pattern corresponding to at least one cell includes: acquiring attribute-related information of at least one cell; and determining a first resource allocation pattern corresponding to at least one cell based on the attribute-related information.

[0009] Optionally, the attribute-related information includes historical service information and / or energy-saving time granularity information.

[0010] Optionally, based on the attribute-related information, a first resource allocation pattern corresponding to at least one cell is determined, including: allocating available resources to the at least one cell; based on historical service information, determining, via a first neural network, a predicted service load of the at least one cell and a mobility level corresponding to resource units of the allocated resources; and determining the first resource allocation pattern corresponding to the at least one cell according to the energy-saving time granularity information, the allocated available resources, the predicted service load, and the mobility level corresponding to resource units of the allocated resources.

[0011] Optionally, the first neural network includes a first sub-neural network, a second sub-neural network and a third sub-neural network, and based on historical business information, determines the predicted business load of the at least one cell and the mobility level corresponding to the resource units of the allocated resources via the first neural network, including: determining the predicted business load of the at least one cell via the first sub-neural network based on historical business information; determining the delay-related characteristics of the resource units of the allocated resources via the second sub-neural network based on the predicted business load and the business type corresponding to the resource units of the allocated resources; determining the mobility level corresponding to the resource units of the allocated resources via the third sub-neural network based on the delay-related characteristics of the resource units of the allocated resources.

[0012] Optionally, the historical service information includes at least one historical service log; based on the historical service information, the predicted service load of the at least one cell is determined through the first sub-neural network, including: based on at least one historical service log of the at least one cell, the predicted service load of each resource unit corresponding to each historical service log is determined through the temporal convolutional network in the first sub-neural network; for the at least one cell, the predicted service load of each resource unit corresponding to each historical service log is integrated through the fusion network in the first sub-neural network to obtain the predicted service load of the at least one cell.

[0013] Optionally, the delay-related characteristics include at least one of resource type, service priority and acceptable maximum delay.

[0014] Optionally, based on the interference-related information between cells, the first resource allocation pattern of the at least one cell is adjusted, including: clustering the at least one cell to obtain at least one cell cluster; based on the interference-related information between cells within each cell cluster, adjusting the first resource allocation pattern of at least one cell in the cell cluster to obtain a corresponding second resource allocation pattern.

[0015] Optionally, clustering the at least one cell includes: clustering the at least one cell based on interference-related information of the at least one cell.

[0016] Optionally, the interference intensity between cells within a cell cluster is not less than a set threshold; and the interference intensity between cells in different cell clusters is less than the set threshold.

[0017] Optionally, adjusting the first resource allocation pattern of the at least one cell based on the interference-related information between cells includes: adjusting the first resource allocation pattern of the at least one cell based on the mobility level corresponding to the resource unit and the interference relationship between cells.

[0018] Optionally, based on the mobility level corresponding to the resource unit and the interference relationship between the cells, the first resource allocation pattern of the at least one cell is adjusted, including: based on the mobility level corresponding to the resource unit and the interference relationship between the cells, adjusting the position of the resource unit in the first resource allocation pattern of the at least one cell in the time domain through a second neural network.

[0019] Optionally, based on the mobility level corresponding to the resource unit and the interference relationship between cells, the position of the resource unit in the first resource allocation pattern of the at least one cell in the time domain is adjusted through a second neural network, including: segmenting the first resource allocation pattern of the at least one cell according to a first length to obtain at least one group of resource allocation pattern fragments; based on the mobility level corresponding to the resource unit and the interference relationship between cells, the position of the resource unit in the at least one group of resource allocation pattern fragments in the time domain is adjusted through a second neural network; and the corresponding second resource allocation pattern is obtained by splicing the at least one group of adjusted resource allocation pattern fragments.

[0020] Optionally, the method further includes: compressing continuous resource units having the same mobility level in the second resource allocation pattern of the at least one cell to obtain a third resource allocation pattern of the at least one cell.

[0021] Optionally, compressing continuous resource units with the same mobility level in the second resource allocation pattern of the at least one cell includes: obtaining an energy-saving weight factor of the at least one cell; and compressing continuous resource units with the same mobility level in the second resource allocation pattern of the at least one cell according to the energy-saving weight factor of the at least one cell.

[0022] Optionally, the method also includes: performing the following iterative operations: determining whether the third resource allocation pattern of the at least one cell meets preset criteria; if the iteration end condition is not met, adjusting the third resource allocation pattern, and compressing continuous resource units with the same movable level in the adjusted third resource allocation pattern.

[0023] Optionally, the iteration end condition includes: the number of iteration operations meets a predetermined number; and / or the throughput gain and energy saving gain of the at least one cell no longer increase.

[0024] Optionally, the method further includes: sending a second resource allocation pattern corresponding to the at least one cell to a second network node.

[0025] Optionally, the first network node is a radio access network intelligent controller RIC, and the second network node is a distribution unit DU.

[0026] According to a second aspect of an embodiment of the present application, a method executed by a second network node in a communication system is provided, the method comprising: receiving a second resource allocation pattern of at least one cell sent by a first network node, wherein the second resource allocation pattern is obtained by the second network node after adjusting the first resource allocation pattern of the at least one cell based on interference-related information between cells; and performing corresponding resource scheduling processing based on the second resource allocation pattern.

[0027] Optionally, the method further includes: sending attribute-related information of the at least one cell to the first network node, so that the first network node determines a first resource allocation pattern corresponding to the at least one cell based on the attribute-related information.

[0028] Optionally, the attribute-related information includes historical service information and / or energy-saving time granularity information.

[0029] Optionally, the method further includes: sending interference-related information of the at least one cell to the first network node.

[0030] Optionally, the method also includes: selecting a corresponding energy-saving weight factor from multiple preset energy-saving weight factors based on the deployment scenario and / or service load-related information of at least one cell corresponding to the second network node; and sending the selected energy-saving weight factor to the first network node.

[0031] Optionally, the first network node is a radio access network intelligent controller RIC, and the second network node is a distribution unit DU.

[0032] According to a third aspect of an embodiment of the present application, a first network node is provided, comprising: a transceiver for sending and receiving signals; and a processor coupled to the transceiver and configured to execute the method performed by the first network node as described above.

[0033] According to a fourth aspect of an embodiment of the present application, a second network node is provided, comprising: a transceiver for sending and receiving signals; and a processor coupled to the transceiver and configured to execute the method performed by the second network node as described above.

[0034] According to a fifth aspect of an embodiment of the present application, a computer-readable storage medium storing instructions is provided, wherein, when the instructions are executed by at least one processor, the at least one processor is prompted to execute the method executed by the first network node or the method executed by the second network node as described above.

[0035] The beneficial effects brought about by the technical solutions provided by the embodiments of the present application will be described below in conjunction with specific optional embodiments, or can be learned from the description of the embodiments, or can be known through the implementation of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly and easily illustrate and understand the technical solutions in the embodiments of the present application, the following is a brief introduction to the drawings required for use in describing the embodiments of the present application.

[0037] Figure 1 is a flow chart illustrating a method performed by a first network node in a communication system according to an exemplary embodiment of the present application.

[0038] Figure 2A is a flowchart illustrating a process of determining a first resource allocation pattern corresponding to at least one cell according to an exemplary embodiment of the present application.

[0039] Figure 2B is a flowchart illustrating a process of determining a first resource allocation pattern corresponding to at least one cell according to another exemplary embodiment of the present application.

[0040] Figure 3is a schematic diagram showing the structure of a first neural network according to an exemplary embodiment of the present application.

[0041] Figure 4 Detailed description is a flowchart illustrating a process of determining, by a first neural network, a predicted traffic load of at least one cell and a movable level corresponding to a resource unit of allocated resources according to an exemplary embodiment of the present application.

[0042] Figure 5 is a schematic diagram showing a process of determining a predicted traffic load of at least one cell by a first sub-neural network according to an exemplary embodiment of the present application.

[0043] Figure 6 is a schematic diagram illustrating a determined predicted traffic load according to an exemplary embodiment of the present application.

[0044] Figure 7 is a schematic diagram showing a process in which a second sub-neural network determines delay-related characteristics of resource units of allocated resources for a cell according to an exemplary embodiment of the present application.

[0045] Figure 8 is a schematic diagram illustrating a process of determining a movable level of a resource unit of allocated resources by a third sub-neural network according to an exemplary embodiment of the present application.

[0046] Fig. 9A is a diagram illustrating a first resource allocation pattern corresponding to a cell generated by a first network node according to an exemplary embodiment of the present application.

[0047] Fig. 9B The diagram is a schematic diagram showing a process of determining a first resource allocation pattern corresponding to at least one cell according to an exemplary embodiment of the present application.

[0048] Fig.10 The flowchart shows a process of adjusting the first resource allocation pattern of the at least one cell based on the inter-cell interference related information according to an exemplary embodiment of the present application.

[0049] Fig.11A A schematic diagram showing a process of segmenting a first resource allocation pattern according to an exemplary embodiment of the present application.

[0050] Fig. 11B is a schematic diagram illustrating a process of adjusting each group of resource allocation pattern fragments by the second neural network according to an exemplary embodiment of the present application.

[0051] Fig. 11C A diagram showing a comparison between a first resource allocation pattern before adjustment for at least one cell and a corresponding second resource allocation pattern obtained after adjustment of the first resource allocation pattern.

[0052] Fig.12 is a flowchart illustrating a process of adjusting a first resource allocation pattern of at least one cell based on inter-cell interference-related information according to another exemplary embodiment of the present application.

[0053] Fig.13 is a schematic diagram showing that a first network node receives interference-related information of a cell from a base station according to an exemplary embodiment of the present application.

[0054] Fig.14 is a diagram illustrating an example of a neighbor list and an interference information list according to an exemplary embodiment of the present application.

[0055] Fig.15 FIG. 4 is a schematic diagram showing the result of cell clustering according to an exemplary embodiment of the present application.

[0056] Fig.16 is a flow chart illustrating a method performed by a first network node according to yet another exemplary embodiment of the present application.

[0057] Fig.17A is a schematic diagram showing the relationship between the average data transmission rate of UE and SNIR.

[0058] Fig. 17B is a diagram showing the relationship among interference between cells, traffic transmission duration Time, and transmission rate f(SINR).

[0059] Fig.18 2 is a schematic diagram of determining the energy saving duration by the energy saving weight factor β according to an exemplary embodiment of the present application.

[0060] Fig.19 2 is a diagram showing energy-saving effects when the energy-saving weight factor takes different values ​​according to an exemplary embodiment of the present application.

[0061] Fig. 20 is a schematic diagram illustrating a process of adjusting an anti-interference energy-saving pattern based on an energy-saving weight factor according to an exemplary embodiment of the present application.

[0062] Fig.21 is a flow chart illustrating a method performed by a second network node in a communication system according to another exemplary embodiment of the present application.

[0063] Fig. 22 is a flowchart illustrating a process in which a first network node and a second network node determine a final resource allocation pattern according to an exemplary embodiment of the present application.

[0064] Fig.23is a schematic diagram illustrating a process of adjusting a resource allocation pattern according to an exemplary embodiment of the present application.

[0065] Fig.24 is a diagram showing a deployment scenario between a network node and a base station according to an exemplary embodiment of the present application.

[0066] Fig.25A is a block diagram illustrating a first network node according to an exemplary embodiment of the present application.

[0067] Fig.25B is a block diagram illustrating a second network node according to an exemplary embodiment of the present application.

[0068] Fig.26 A schematic structural diagram of an electronic device applicable to an exemplary embodiment of the present application is shown in FIG.

[0069] Fig.27A is a diagram showing the comparison results between the solution of the present application and the prior art under the first evaluation condition.

[0070] Fig.27B It is a schematic diagram showing the distribution of three adjacent cells.

[0071] Fig.28 is a diagram showing the comparison results between the solution of the present application and the prior art under the second evaluation condition. DETAILED DESCRIPTION

[0072] The following description with reference to the accompanying drawings is provided to facilitate a comprehensive understanding of the various embodiments of the present disclosure as defined by the claims and their equivalents. This description includes various specific details to facilitate understanding but should be considered as exemplary only. Therefore, one of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, for the sake of clarity and conciseness, descriptions of well-known functions and structures may be omitted.

[0073] The terms and expressions used in the following specification and claims are not limited to their dictionary meanings, but are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Therefore, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purposes only and not for the purpose of limiting the present disclosure as defined in the appended claims and their equivalents.

[0074] It should be understood that the singular forms "a", "an", and "the" may also include plural references unless the context clearly indicates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more such surfaces. When we refer to an element as being "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or the one element and the other element may establish a connection relationship through an intermediate element. In addition, "connected" or "coupled" as used herein may include wireless connection or wireless coupling.

[0075] The term "include" or "may include" refers to the presence of the corresponding disclosed functions, operations or components that can be used in various embodiments of the present disclosure, rather than limiting the presence of one or more additional functions, operations or features. In addition, the term "include" or "have" may be interpreted as indicating certain characteristics, numbers, steps, operations, constituent elements, components or combinations thereof, but should not be interpreted as excluding the possibility of the presence of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0076] The term "or" used in various embodiments of the present disclosure includes any of the listed terms and all combinations thereof. For example, "A or B" may include A, may include B, or may include both A and B. When describing multiple (two or more) items, if the relationship between the multiple items is not clearly defined, the multiple items may refer to one, multiple, or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" may be implemented as parameter A including A1 or A2 or A3, or may be implemented as parameter A including at least two of the three items A1, A2, and A3.

[0077] Unless defined differently, all terms (including technical terms or scientific terms) used in the present disclosure have the same meanings as understood by those skilled in the art described in the present disclosure. Common terms as defined in dictionaries are interpreted as having meanings consistent with the context in the relevant technical field, and should not be interpreted ideally or overly formally unless clearly defined in the present disclosure.

[0078] At least some functions of the device or electronic device provided in the embodiments of the present disclosure can be implemented by an AI model, such as at least one module among multiple modules of the device or electronic device can be implemented by an AI model. Functions associated with AI can be performed by non-volatile memory, volatile memory and processor.

[0079] The processor may include one or more processors. In this case, the one or more processors may be general-purpose processors, such as a central processing unit (CPU), an application processor (AP), etc., or pure graphics processing units, such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-specific processor, such as a neural processing unit (NPU).

[0080] The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence (AI) models stored in non-volatile memory and volatile memory. The predefined operating rules or artificial intelligence models are provided by training or learning.

[0081] Here, providing by learning means obtaining a predefined operating rule or an AI model with desired characteristics by applying a learning algorithm to a plurality of learning data. The learning can be performed in the device or electronic device itself in which the AI ​​according to the embodiment is executed, and / or can be implemented by a separate server / system.

[0082] The AI ​​model may include multiple neural network layers. Each layer has multiple weight values, and each layer performs neural network calculations by calculating between the input data of the layer (such as the calculation results of the previous layer and / or the input data of the AI ​​model) and the multiple weight values ​​of the current layer. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q networks.

[0083] A learning algorithm is a method of using a plurality of learning data to train a predetermined target device (e.g., a robot) to enable, allow, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0084] According to the present disclosure, at least one step in the method performed by the network node, such as predicting the service load and service type, extracting delay-related features, etc., can be implemented using an artificial intelligence model. The processor of the electronic device can perform preprocessing operations on the data to convert it into a form suitable for use as an input to the artificial intelligence model. The artificial intelligence model can be obtained through training. Here, "obtained through training" means training a basic artificial intelligence model with multiple training data through a training algorithm to obtain a predefined operation rule or artificial intelligence model configured to perform the desired feature (or purpose).

[0085] The existing solutions suffer from system performance loss and insufficient energy-saving efficiency. The main reasons are as follows:

[0086] (1) System performance loss: Existing solutions bring about interference increase, resulting in a decrease in cell throughput. Specifically, existing solutions are all carried out independently within the cell, and each cell is unaware of the data traffic distribution of other cells. Each cell adjusts resources according to the principle of data traffic concentration, and it is easy for resources between cells to be concentrated in the same time domain for scheduling, thereby forming inter-cell interference. The increase in interference causes the signal to interference plus noise ratio (SINR) to decrease, which leads to an increase in the block error rate (BLER). In addition, based on the link adaptation mechanism, the system will reduce the modulation and coding scheme (MCS) to maintain the convergence of the BLER. Accordingly, the reduction in MCS will lead to a decrease in transmission efficiency, so that the same service traffic will take more time to transmit, resulting in a decrease in throughput.

[0087] (2) Insufficient energy-saving efficiency: The existing solution will make the interference change more obviously over time, causing obvious fluctuations in SINR, affecting transmission efficiency, resulting in waste of time domain resources, and thus causing insufficient energy-saving efficiency. Specifically, the existing solution does not conduct inter-cell collaboration, so the impact of resource adjustment on the channel environment cannot be accurately assessed within the cell. If the SINR of part of the time domain range increases after the resource adjustment, but the cell cannot accurately assess the change in SINR, it still uses a lower MCS for service transmission, thereby failing to exert the expected transmission efficiency, prolonging the service transmission time, reducing the time that can be used to shut down resources, and reducing energy-saving efficiency.

[0088] To this end, the present application provides a method for generating a resource allocation pattern executed by a network node. Specifically, the method can predict the service load and service delay characteristics of each cell in the future, obtain the position and mobility level of the service in the time domain in the future, and generate a service delay-aware resource allocation pattern (also referred to as an energy-saving pattern, a resource allocation pattern, an energy-saving pattern, etc.) for each cell. The mobility level refers to the adjustable range of service resources in the time domain. Unlike the energy-saving patterns of traditional solutions, the service delay-aware energy-saving pattern reflects the resource mobility of different services while taking into account service flows and service characteristics; in addition, the method can adjust the service delay-aware energy-saving pattern of each cell based on the interference between cells, and generate an adjusted energy-saving pattern (also referred to as an anti-interference energy-saving pattern, an anti-interference energy-saving pattern, etc.). Specifically, when there are service resources to be sent between cells within the same service transmission time interval, since the service resources of the cells are movable, the service resources of some of the cells can be moved to the previous or later transmission time so as to be compatible with other cells. The service resources of the area are staggered in the time domain, and the energy-saving pattern of service delay perception of all cells is adjusted within the cell cluster based on the minimum interference criterion. This adjustment will reduce the interference between cells and increase the SINR of each cell. In addition, since the SINR of each cell increases, the channel quality becomes better, so a higher-order MCS can be selected for encoding during service transmission, so that the time required to transmit the same amount of data as before is reduced. On this basis, the method can further adjust the anti-interference energy-saving pattern, thereby further reducing the duration of service transmission in the anti-interference energy-saving pattern, thereby further improving the energy-saving efficiency of the cell.

[0089] The following describes several optional embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0090] Figure 1 is a flow chart illustrating a method performed by a first network node in a communication system according to an exemplary embodiment of the present application.

[0091] like Figure 1 As shown in , in step S110, a first resource allocation pattern corresponding to at least one cell is determined.

[0092] In an exemplary embodiment of the present application, the first resource allocation pattern corresponding to the cell may be determined based on an existing known method.

[0093] In another exemplary embodiment of the present application, the first resource allocation pattern corresponding to the cell may be determined according to the inventive concept proposed in the present application, where the first resource allocation pattern includes a mobility level corresponding to the resource unit, and the mobility level indicates a level of resource adjustment of the resource unit in the time domain. The resource unit indicates a transmission time interval (TTI) used for transmitting services in the time domain, indicating a time granularity that can be used for energy saving.

[0094] Specifically, the step of determining the first resource allocation pattern corresponding to at least one cell may include: acquiring attribute-related information of at least one cell.

[0095] In the present application, the first network node may receive attribute-related information of the cell from the base station (for example, from the DU of the base station), wherein the attribute-related information includes historical service information and / or energy-saving time granularity information. In the present application, the energy-saving time granularity information may be power amplifier (PA) shutdown time granularity information (in the present application, it may also be referred to as RU shutdown time granularity information, and in addition, in the present application, "PA shutdown", "PA shutdown", "RU shutdown" and "RU shutdown" may be used interchangeably, and "PA on", "PA on", "RU on" and "RU on" may also be used interchangeably), the historical service information may include at least one historical service log (for example, historical service load and service type), and the attribute-related information may also include common signals and reference signals (RS). Among them, the first network node may be a radio access network (RAN) intelligent controller (RAN Intelligent Controller, RIC), but the present application is not limited thereto.

[0096] The step of determining the first resource allocation pattern corresponding to at least one cell may further include: determining the first resource allocation pattern corresponding to at least one cell based on the attribute related information. Figure 2A Describe this. Figure 2A is a flowchart illustrating a process of determining a first resource allocation pattern corresponding to at least one cell according to an exemplary embodiment of the present application.

[0097] In step S210, available resources are allocated to at least one cell.

[0098] In the present application, available resources may be determined for at least one cell based on the common signal and the RS. Specifically, available resources may be allocated to the cell in resources not occupied by the common signal and the RS. In detail, the first network node may determine available resources for the cell using the common signal and the RS included in the attribute-related information of the cell received from the base station, so as not to conflict with the resources occupied by the common signal and the RS.

[0099] In step S220, based on historical service information, the predicted service load of at least one cell and the mobility level corresponding to the resource unit of the allocated resources are determined via the first neural network. In the present application, the resource unit of the allocated resources indicates that the PA is not turned off for the resource unit, and the resource unit of the unallocated resources indicates that the PA is turned off for the resource unit.

[0100] Specifically, the historical service information of the cell received by the first network node from the base station is input into the first neural network, and the first neural network performs a prediction operation based on the input historical service information, thereby obtaining the predicted service load of the cell in the future and the mobility level corresponding to the resource unit of the allocated resources. Figure 3 As shown in , the first neural network includes a first sub-neural network, a second sub-neural network and a third sub-neural network, wherein the first sub-neural network can be composed of a temporal convolutional network (TCN) and a concatenation network (CN), the second sub-neural network can be composed of a deep learning convolutional neural network (CNN), and the third sub-neural network can be composed of a Bayesian classifier ( Bayes Classifier, NB) is composed, but the present application is not limited to this. Figure 3 and Figure 4 Step S220 is described in detail.

[0101] Figure 4 Detailed description is a flowchart illustrating a process of determining, by a first neural network, a predicted traffic load of at least one cell and a movable level corresponding to a resource unit of allocated resources according to an exemplary embodiment of the present application.

[0102] In step S410, based on the historical service information, the predicted service load of at least one cell is determined by the first sub-neural network, wherein the predicted service load is used to indicate the resource units that each service of the corresponding cell will occupy. As described above, the historical service information includes at least one historical service log.

[0103] Specifically, based on at least one historical service log of at least one cell, the predicted service load of each resource unit corresponding to each historical service log is determined through the TCN in the first sub-neural network.

[0104] like Figure 5 As shown in , the input of the first sub-neural network is historical business information, specifically, K historical business logs (hereinafter referred to as log entities). Accordingly, the first sub-neural network may include K TCNs and a fusion network (such as CN), wherein each of the K log entities is input into a corresponding one of the K TCNs, and each TCN may adopt Figure 5 The operation process in the lower middle diagram processes an input log entity. Specifically, the log entity input to each TCN is divided into N inputs, that is, N time series data (that is, t to t-(N-1) inputs). Then, the hidden layer in the TCN sequentially calculates the convolution of these N inputs, and finally the output of the TCN is obtained, that is, the predicted business load of each resource unit corresponding to each historical business log is determined.

[0105] Then, through the fusion network (e.g., CN) in the first sub-neural network, for at least one cell, the predicted service load of each resource unit corresponding to each historical service log is fused to obtain the predicted service load of at least one cell, that is, the service load of at least one cell in the future is predicted, such as Figure 6 As shown in , white represents no service, and gray represents service, but the service is not distinguished by characteristics at this time. At this point, the predicted service load of each cell in at least one cell can be obtained.

[0106] Return to reference Figure 4 In step S420, based on the predicted service load and the service type corresponding to the resource unit of the allocated resources, the delay-related characteristics of the resource unit of the allocated resources are determined by the second sub-neural network.

[0107] like Figure 3 As shown in , the predicted service load determined by the first sub-neural network for the cell and the service type corresponding to the resource unit of the allocated resources are input into the second sub-neural network, such as Figure 7As shown in , the second sub-neural network determines the delay-related characteristics of the resource unit of the allocated resources for the cell based on the input information. The delay-related characteristics may include, for example, at least one of the resource type, service priority, and acceptable maximum delay, wherein the resource type may be, for example, a guaranteed bit rate (GBR) type, a non-GBR (Non-GBR) type, a delay critical GBR (Delay Critical GBR), the service priority may be, for example, 5, 7, 10, 15, 19, 20..., and the acceptable maximum delay may be, for example, 5ms, 10ms, 30ms, 50ms.... At this point, the delay-related characteristics of the resource unit of the allocated resources of each cell in at least one cell can be obtained.

[0108] In step S430, based on the delay-related characteristics of the resource units of the allocated resources, the movable levels corresponding to the resource units of the allocated resources are determined by the third sub-neural network.

[0109] like Figure 3 As shown in , the delay-related features of the resource units of the allocated resources determined by the second sub-neural network are input into the third sub-neural network, and then the third sub-neural network performs a classification operation using the input delay-related features to predict the movable level corresponding to the resource units of the allocated resources, such as Figure 8 As shown in , the third sub-neural network can classify the input delay-related features to obtain the mobility level of the resource units of the allocated resources for each cell. As described above, the third sub-neural network can be a Bayesian classifier NB, which uses the probability of different features appearing in historical service transmissions to define different probability combinations as multiple mobility levels, such as 4 mobility levels: mobility level I indicating that it is not movable, mobility level II indicating that at most one resource unit can be moved, mobility level III indicating that at most three resource units can be moved, and mobility level IV indicating that at most six resource units can be moved, but the present application is not limited thereto, and the number of movable levels can be other values, such as 3, 5, 7, etc., and the number of resource units that can be moved at most for each movable level is also not limited thereto.

[0110] Through step S220 described above, the predicted service load of at least one cell and the mobility level corresponding to the resource unit of the allocated resources can be determined. As described above, the mobility level may be related to at least one of the following: service load, service priority, and delay-related characteristics of the service.

[0111] Return to reference Figure 2AIn step S230, a first resource allocation pattern corresponding to at least one cell is determined according to the energy-saving time granularity information, the allocated available resources, the predicted service load, and the mobility level corresponding to the resource units of the allocated resources.

[0112] Specifically, the first network node may determine the size of the resource unit in the resource allocation pattern for the corresponding cell according to the energy-saving time granularity information of the cell received from the base station, for example, determine whether the resource unit is at a time slot level or a subframe level, etc. Thereafter, the first network node determines a first resource allocation pattern corresponding to the cell based on the available resources allocated to the cell, the predicted service load, and the mobility level corresponding to the resource unit of the allocated resources. Fig. 9A A first resource allocation pattern corresponding to a cell generated by a first network node is shown, wherein each resource unit in the first resource allocation pattern has a mobility level, wherein orange indicates mobility level I, blue indicates mobility level II, yellow indicates mobility level III, green indicates mobility level IV, and white indicates no service.

[0113] In the above reference Figure 2A In the process of determining the first resource allocation pattern corresponding to at least one cell based on the attribute related information described above, step S210 is first described, and then step S220 is described. However, the present application is not limited thereto, and the execution order of steps S210 and S220 is not limited. Step S220 may be executed first, and then step S210 may be executed. Figure 2B As shown in FIG. , step S210 and step S220 are performed simultaneously.

[0114] Fig. 9B is a schematic diagram showing a process of determining a first resource allocation pattern corresponding to at least one cell according to an exemplary embodiment of the present application, which summarizes Figures 6 to 9A the overall process.

[0115] like Fig. 9B As shown in FIG. 1 , first, the first sub-neural network (eg, TCN and the fusion network (eg, CN)) determines the predicted traffic load of at least one cell (ie, Figure 6 Then, the second sub-neural network (e.g., CNN) determines the delay-related characteristics of the resource units of the allocated resources (i.e., Figure 7 ), thereafter, the third sub-neural network (e.g., NB classifier) ​​determines the movable level (i.e., Figure 8), thereafter, the first network node determines a first resource allocation pattern (i.e., Fig. 9A ).

[0116] Return to reference Figure 1 In step S120, based on the interference-related information between cells, the first resource allocation pattern of at least one cell is adjusted to obtain a second resource allocation pattern corresponding to the at least one cell, wherein the interference-related information between cells includes the interference relationship between cells.

[0117] Specifically, the first resource allocation pattern of the at least one cell may be adjusted based on the mobility level corresponding to the resource unit and the interference relationship between cells.

[0118] In an exemplary embodiment of the present application, the step of adjusting the first resource allocation pattern of the at least one cell based on the mobility level corresponding to the resource unit and the interference relationship between the cells may include: adjusting the position of the resource unit in the first resource allocation pattern of the at least one cell in the time domain through a second neural network based on the mobility level corresponding to the resource unit and the interference relationship between the cells. Fig.10 This is described in detail.

[0119] Fig.10 The flowchart shows a process of adjusting the first resource allocation pattern of the at least one cell based on the inter-cell interference related information according to an exemplary embodiment of the present application.

[0120] like Fig.10 As shown in, in step S1010, a first resource allocation pattern of at least one cell is segmented according to a first length to obtain at least one group of resource allocation pattern fragments, wherein the first length is a data input length supported by the second neural network.

[0121] like Fig.11A As shown in, according to the data input length supported by the second neural network, the first resource allocation pattern of at least one cell is segmented to obtain at least one group of resource allocation pattern fragments, wherein the next group of resource allocation pattern fragments starts from the end of the previous group of resource allocation pattern fragments.

[0122] In step S1020, based on the mobility level corresponding to the resource unit and the interference relationship between cells, the positions of the resource units in at least one group of resource allocation pattern segments in the time domain are adjusted through a second neural network.

[0123] Specifically, based on the mobility level corresponding to the resource unit and the interference relationship between cells, the second neural network is used to adjust the position of the resource unit in each group of resource allocation pattern fragments in the time domain with the goal of minimizing the interference between cells. In an exemplary embodiment of the present application, the adjustment criterion of each group of resource allocation pattern fragments can be set to the following formula (1):

[0124]

[0125] Where T is the duration of a set of resource allocation pattern segments; N represents the number of cells; Indicates cell S j For cell S i interference (also known as "interference relationship"); In the set of resource allocation pattern fragments, if cell S i If no resources are allocated on resource unit t (i.e., RU is turned off), then is 0. If cell Si allocates resources on resource unit t (i.e., turns on RU), then is 1; similarly, Have similar meanings.

[0126] Therefore, in order to minimize the criterion expressed by formula (1), as many items as possible need to be 0. If the interference between cells is large, then Not 0. If or If it is 0, it means in the community and community If there is no interference between them, the entire sub-item is 0, so it is necessary to make the number of k=0 as large as possible. By solving the above formula (1), the adjustment of each group of resource allocation pattern fragments can be finally achieved.

[0127] like Fig. 11B As shown in , the first network node inputs a set of resource allocation pattern fragments and the interference relationship between cells of each cell corresponding to the set of resource allocation pattern fragments into the second neural network, wherein each resource unit in the set of resource allocation pattern fragments has a movable level. The second neural network aims to minimize the interference between cells, and based on the movable level corresponding to the resource unit and the interference relationship between cells, adjusts the position of the resource units in the set of resource allocation pattern fragments in the time domain, and finally, the adjustment of the set of resource allocation pattern fragments can be achieved. In a similar manner, the adjustment of each set of resource allocation pattern fragments can be achieved. In the present application, the second neural network can be, for example, a feed forward neural network (FFNN), but the present application is not limited thereto.

[0128] In step S1030, the at least one set of adjusted resource allocation pattern segments are spliced ​​to obtain a corresponding second resource allocation pattern.

[0129] Specifically, the first network node may splice the at least one set of adjusted resource allocation pattern fragments in time sequence, thereby obtaining a second resource allocation pattern of at least one cell. Fig. 11C A comparison diagram is shown between a first resource allocation pattern before adjustment for at least one cell and a corresponding second resource allocation pattern obtained after adjustment of the first resource allocation pattern. Fig. 11C As shown in (b), it can be seen that after adjusting the first resource allocation pattern, for resource units that originally had inter-cell interference, after adjusting the positions of these resource units in the time domain based on the mobility levels corresponding to the resource units and the interference relationship between cells, the inter-cell interference can be reduced, thereby improving the SINR of each cell and further improving the transmission efficiency.

[0130] Through steps S1010 to S1030, a second resource allocation pattern for at least one cell can be obtained, thereby improving the transmission efficiency of the cell. Under the same traffic situation, the cell can shorten the service transmission time and provide more idle time for shutting down RU to achieve energy saving.

[0131] In addition, when the above reference Fig.10 When the number of cells of the at least one cell described is large, in order to further reduce the complexity and improve the transmission efficiency of the cell, at least one cell may be clustered and a similar method as described above may be performed for each cell cluster. Fig.10 The adjustment process of the first resource unit allocation pattern described in Fig.12 Describe this.

[0132] Fig.12 is a flowchart illustrating a process of adjusting a first resource allocation pattern of at least one cell based on inter-cell interference-related information according to another exemplary embodiment of the present application.

[0133] In step S1210, at least one cell is clustered to obtain at least one cell cluster. In an exemplary embodiment of the present application, the first network node may cluster at least one cell based on the interference-related information of the at least one cell. In the present application, the "interference-related information" here has the same meaning as the interference-related information between cells mentioned above, and both refer to the interference of other cells to the present cell.

[0134] First, the first network node may receive interference-related information of at least one cell from a base station, wherein the interference-related information of a cell may be used to indicate the interference of other cells to the cell, for example, the interference may be indicated by reference signal receiving power (RSRP). Fig.13 As shown in , the first network node may periodically receive interference-related information of a cell from a base station. In addition, the first network node may also receive cell geographic location information of the cell from the base station when the base station is initially deployed.

[0135] Then, the first network node may generate a neighbor cell list and an interference information list based on the interference-related information of the at least one cell and the cell geographic location information. Fig.14 An example of a neighboring cell list and an interference information list according to an exemplary embodiment of the present application is shown, wherein in the neighboring cell list, No represents the number of cells, the cell identification number (IDentity, ID) represents the physical ID of the cell, and Neighb.X represents the neighboring cell ID centered on the cell ID; in the interference information list, No represents the number of cells, the cell ID represents the physical ID of the cell, and Neighb.RSRP.X represents the interference generated by the neighboring cell ID centered on the cell ID, for example, RSRP 112,110 It represents the interference caused by cell 110 to cell 112.

[0136] Thereafter, the first network node clusters at least one cell based on the neighbor cell list and the interference information list.

[0137] In the present application, after at least one cell is clustered, the interference intensity between cells within a cell cluster is not less than a set threshold, and the interference intensity between cells in different cell clusters is less than the set threshold, wherein the set threshold is pre-set or can be set at any time according to the actual environment, and the present application does not make specific limitations on this.

[0138] For example, multiple cells can be clustered based on the K-average algorithm with the goal of maximizing the interference within the cell cluster, so that the cells whose interference is not less than the set threshold are divided into the same cell cluster, that is, the cells with strong interference are divided into the same cell cluster. Specifically, assuming that the final cell clusters are C1, C2, .. Ck, the following objective function (2) is set using the K-average algorithm, and the final clustering result can be determined by solving the problem with the goal of minimizing the objective function, where k represents the number of final clusters, which is a positive integer greater than or equal to 1.

[0139]

[0140]

[0141] Among them, C p represents the pth cell cluster; S i and S j represent the cell S in the pth cell cluster respectively i and cell S j ; RSRP Si,Sj Indicates cell S i Neighboring cells S reported to the network node j For cell S i The interference can be obtained by searching the neighboring cell list and interference information list generated above; μ p represents the average interference power in the p-th cell cluster; N p Indicates that in cell cluster C p The total number of cells that receive the reported interference power of adjacent cells counted in the cluster. Through the above method, cells with strong interference between each other can be divided into the same cell cluster, such as Fig.15 As shown in the figure, the dark orange cells are divided into one cell cluster, the light orange cells are divided into another cell cluster, and the green cells are divided into another cell cluster. In this way, the interference between cell clusters can be weakened, and the strong interference is concentrated within the cell cluster, which facilitates the effective processing of the strong interference within the cell cluster.

[0142] Return to reference Fig.12 In step S1220, based on the interference related information between cells in each cell cluster, the first resource allocation pattern of at least one cell in the cell cluster is adjusted to obtain a corresponding second resource allocation pattern.

[0143] Since the interference between cells in each cell cluster is large, and the resources in the first resource allocation pattern may be scheduled in the same time domain, it is necessary to adjust the first resource allocation pattern of the cells in the cell cluster. Fig.10 The described process of adjusting the first resource allocation pattern is applied to step S1220, that is, for at least one cell in each cell cluster, the first resource allocation pattern of the at least one cell may be adjusted based on the inter-cell interference related information in the at least one cell.

[0144] Specifically, first, a first resource allocation pattern of at least one cell in a cell cluster is segmented according to a first length to obtain at least one group of resource allocation pattern fragments, wherein the first length is a data input length supported by the second neural network.

[0145] Then, based on the mobility level corresponding to the resource unit and the interference relationship between the cells in the cell cluster, the second neural network is used to adjust the position of the resource unit in the at least one group of resource allocation pattern fragments in the time domain. In detail, the above formula (1) can still be used to represent the adjustment criteria of each group of resource allocation pattern fragments, where N represents the number of cells in the cell cluster, and other symbols in formula (1) (such as T, t, S j , S i etc.) with the above reference Fig.10 The same meaning as described above, in addition, due to the large interference within the cell cluster, Not 0, if or If it is 0, it means in the community and community If there is no interference between them, the entire sub-item is 0, so it is necessary to make the number of k=0 as large as possible. By solving the above formula (1), the adjustment of each group of resource allocation pattern fragments can be finally achieved.

[0146] Thereafter, the at least one group of adjusted resource allocation pattern fragments are spliced ​​to obtain a corresponding second adjusted resource allocation pattern, that is, to obtain a second resource allocation pattern for cells in the one cell cluster. Fig.10 This has been described in detail, so it will not be repeated here.

[0147] Return to reference Figure 1 Through the above steps S110 and S120, a second resource allocation pattern of at least one cell can be obtained, thereby improving the transmission efficiency of the cell. Under the same traffic situation, the cell can shorten the service transmission time and provide more idle time for shutting down RU to achieve energy saving. The method may also include: sending the second resource allocation pattern corresponding to the at least one cell to a second network node, wherein the second network node may be a distributed unit DU.

[0148] In order to further save energy, the present application can also use the SINR gain of each cell to compress the transmission time. Fig.16 A method performed by a first network node according to another exemplary embodiment of the present application is described. Figure 1 The transmission time is further compressed on the basis of the method shown.

[0149] Fig.16 is a flow chart illustrating a method performed by a first network node according to yet another exemplary embodiment of the present application.

[0150] Fig.16 Steps S1601 to S1602 in Figure 1Steps S110 to S120 are the same as those in the above description, and therefore, they will not be described in detail here.

[0151] In step 1603, an energy-saving weight factor of at least one cell is obtained, wherein the energy-saving weight factor may be selected by the corresponding cell from a plurality of preset energy-saving weight factors according to the cell deployment scenario and / or service load related information (also referred to as the busy or idle state of the cell time period). In the present application, the energy-saving weight factor may be pre-acquired by the first network node, and when the cell deployment scenario changes, or the service load related information (i.e., the busy state or idle state of the cell time period) changes, the energy-saving weight factor may be received from the base station in real time.

[0152] Specifically, according to Shannon's formula, the cell S i Average data transfer rate for users within for:

[0153]

[0154] Among them, S / N is the signal-to-noise power ratio, which is a dimensionless unit. In practical engineering applications, S / N can be replaced by SINR. Since the unit of SINR is dB, the conversion relationship between SINR and S / N is:

[0155] SINR = 101g (S / N), S / N = 10 SINR / 10 (5)

[0156] Therefore, cell S i Average data transmission rate of UEs within Can be turned into:

[0157]

[0158] like Fig.17A As shown in can be written as a monotonically increasing function of SNIR:

[0159]

[0160] Assume that the service throughput in a certain time period T is Tput, then Tput and The relationship can be expressed as:

[0161] Tput∝f(SINR) (8)

[0162] Assume that there are three cells Cell 0, Cell 1 and Cell 2, the interference between them and the relationship between the service transmission duration T and the transmission rate f(SINR) can be expressed as follows: Fig. 17BIn order to reduce inter-cell interference, the resources of different resource units can be staggered in the time domain, as shown in Fig. 17B As shown in the figure on the right. After the interference is reduced, f(SINR) will increase to f(SINR'), and accordingly, the time T can be shortened to T', where T' is the minimum transmission duration after the transmission rate is optimized. Therefore, T-T' represents the additional maximum energy saving duration brought by the f(SINR) gain. Therefore, the cell can determine how much time of the additional maximum energy saving duration T-T' can be used to save energy by setting the energy saving weight factor β, where the energy saving weight factor β can represent the optimization capability of the cell for energy saving.

[0163] In an exemplary embodiment of the present application, the energy-saving weight factor may be selected by the cell from a plurality of preset energy-saving weight factors according to the cell deployment scenario and / or information related to the business load, wherein the information related to the business load may be determined by whether the cell is in a busy period or an idle period. More specifically, different levels may be used to represent the busy period and the idle period. For example, the busy period may be divided into a busy period level 1 and a busy period level 2, and the idle period may be divided into an idle period level 1 and an idle period level 2. However, the present application is not limited thereto, and the busy period and the idle period may be divided into more or fewer levels. Table 1 below shows an example of an energy-saving weight factor β set according to a cell deployment scenario and / or information related to the business load:

[0164] [Table 1]

[0165]

[0166] In other words, the cell can select the energy saving weight factor β according to the following criteria:

[0167] (1) Based on the information related to the service load (i.e., the busy or idle status of the cell period): when the cell is in a busy period, the setting of β tends to increase capacity, and when the cell is in an idle period, the setting of β tends to increase energy saving;

[0168] (2) Deployment scenario of a cell: When there are many UEs in a cell, the setting of β tends to improve capacity. When there are few UEs in a cell, the setting of β tends to improve energy saving.

[0169] Therefore, the cell can select an energy-saving weight factor β from multiple preset energy-saving weight factors according to its deployment scenario and / or business load-related information, and then send the selected energy-saving weight factor β to the first network node. Then, the first network node further compresses the transmission time according to the energy-saving weight factor β.

[0170] Specifically, in step S1604, continuous resource units with the same mobility level in the second resource allocation pattern of the at least one cell are compressed according to the energy saving weight factor of the at least one cell, so as to obtain a third resource allocation pattern of the at least one cell.

[0171] In an exemplary embodiment of the present application, as described above, the energy saving weight factor β is used to determine how much time in the additional maximum energy saving duration T-T' can be used to save energy, such as Fig.18 As shown in , since Tput is constant, the adjusted energy saving duration can be calculated as:

[0172] ΔT=(TT′) β (9)

[0173] When β tends to 0, the energy-saving duration tends to 0, and thus is not used for energy saving; when β tends to 1, the energy-saving duration tends to the additional maximum energy-saving duration, and thus can maximize energy saving.

[0174] Accordingly, the compressed transmission duration T" is:

[0175] T”=T-ΔT=T-(T-T')β (10)

[0176] In this case, the corresponding SINR becomes SINR", such as Fig.18 As shown in , it is a schematic diagram showing how the energy-saving weight factor β determines the energy-saving duration.

[0177] Fig.19 is a diagram showing the energy saving effect when the energy saving weight factor takes different values ​​according to an exemplary embodiment of the present application. Fig.19 As shown in (a), assuming that the increase in SINR leads to a doubling of the average data transmission rate during this transmission time, accordingly, half of the transmission time can be used for energy saving. Fig.19 As shown in (b), when β is 100%, 50% and 25% respectively, the energy saving duration will decrease as β decreases, that is, different effects will be produced.

[0178] In the adjusted resource allocation pattern of the cell, there may be multiple continuous resource units of the same mobility level. In other words, resources of the same mobility level may occupy multiple continuous resource units. Therefore, the continuous resource units with the same mobility level in the second resource allocation pattern may be compressed according to the energy-saving weight factor β, that is, the transmission time of the continuous resource units with the same mobility level is compressed according to the energy-saving weight factor β, such as Fig. 20 as shown in .

[0179] Therefore, through the above step S1604, a third resource allocation pattern of at least one cell can be obtained.

[0180] Fig.16 The method shown may further include step S1605, in which it is determined whether the third resource allocation pattern of at least one cell meets a preset criterion.

[0181] If it is determined that the third resource allocation pattern of the at least one cell meets a preset criterion, then in step S1606, the third resource allocation pattern of the at least one cell is sent to the cell.

[0182] If the third resource allocation pattern of the at least one cell does not meet the preset criteria, the operations of step S1602 to step S1604 are iteratively performed, that is, returning to perform step S1602 to step S1604, specifically, in step S1602, the third resource allocation pattern of the at least one cell is adjusted again, and then step S1603 is selectively performed, that is, the receiving energy saving weight factor β of the at least one cell is selectively obtained, and then, in step S1604, the continuous resource units with the same movable level in the adjusted third resource allocation pattern are compressed again. Wherein, step S1603 is selectively performed, specifically, when the deployment scenario of the cell changes, or the service compliance related information (that is, the busy state or idle state of the cell time period) changes, the cell reselects the energy saving weight factor β, and resends the reselected energy saving weight factor β to the first network node.

[0183] Thereafter, the process proceeds to step S1605 again and the judgment operation is re-executed until the preset criterion is satisfied.

[0184] In an exemplary embodiment of the present application, the preset criterion may be one of the following criteria: a first criterion that the number of iterative operations meets a predetermined number, for example, the predetermined number may be 3, 4, 5, etc.; a second criterion that the throughput gain and energy saving gain of the at least one cell no longer increase, wherein, if the number of iterative operations meets the predetermined number, the throughput gain and energy saving gain of the at least one cell no longer increase. Fig.12 The method shown in adjusts the first resource allocation pattern of at least one cell, and the second criterion here indicates that the throughput gain and energy saving gain of all cells in a cell cluster no longer increase.

[0185] Fig.21 1 is a flowchart illustrating a method performed by a second network node in a communication system according to another exemplary embodiment of the present application. In this exemplary embodiment, the second network node may be, for example, a DU of a base station, but the present application is not limited thereto.

[0186] like Fig.21As shown in, in step S2110, a second resource allocation pattern of at least one cell sent by a first network node is received, wherein the second resource allocation pattern is obtained after the second network node adjusts the first resource allocation pattern of the at least one cell based on inter-cell interference related information, wherein the first network node may be executed as described above with reference to Figure 1 A network node of the described method is, for example, a RIC.

[0187] In the present application, the method may further include: sending attribute-related information of the at least one cell to the first network node, so that the first network node determines a first resource allocation pattern corresponding to the at least one cell based on the attribute-related information. The attribute-related information includes historical service information and / or energy-saving time granularity information. The method may further include: sending interference-related information of the at least one cell to the first network node. Figures 1 to 20 , a process in which the first network node receives information from the second network node and determines the first resource allocation pattern and the second resource allocation pattern based on the received information is described in detail, and therefore, it will not be described in detail here.

[0188] In step S2120, corresponding resource scheduling processing is performed based on the second resource allocation pattern.

[0189] also, Fig.21 The method shown may also include: selecting a corresponding energy-saving weight factor from a plurality of preset energy-saving weight factors according to the deployment scenario and / or service load related information of at least one cell corresponding to the second network node; sending the selected energy-saving weight factor to the first network node, wherein the energy-saving weight factor can be used by the first network node to compress continuous resource units with the same movable level in the second resource allocation pattern of at least one cell. Fig.16 This has been described in detail, so it will not be repeated here.

[0190] In order to facilitate those skilled in the art to understand the technical structure of the present application, the following reference Fig. 22 and Fig.23 The solution proposed in this application is described in general.

[0191] Fig. 22 is a flowchart illustrating a process in which a first network node and a second network node determine a final resource allocation pattern according to an exemplary embodiment of the present application. Fig.23 is a schematic diagram illustrating a process of determining a final resource allocation pattern according to an exemplary embodiment of the present application.

[0192] like Fig. 22As shown in, in step 1, the second network node (e.g., the DU of each base station) periodically sends attribute-related information to the first network node (e.g., the RIC), and the attribute-related information includes historical service information and / or energy-saving time granularity information. In addition, the attribute-related information may also include a common signal and RS.

[0193] In step 2, the first network node allocates available resources to the cell of the second network node based on the common signal and the RS, determines the predicted service load of at least one cell and the mobility level corresponding to the resource unit of the allocated resources via the first neural network based on the historical service information, and then determines the first resource allocation pattern (also referred to as the service delay-aware energy-saving pattern) corresponding to the at least one cell according to the energy-saving time granularity information, the allocated available resources, the predicted service load, and the mobility level corresponding to the resource unit of the allocated resources, such as Fig.23 The energy-saving pattern based on service delay perception not only reflects the shutdown status of cell resources, but also reflects the range of movable adjustment of resources in the time domain.

[0194] In step 3, the second network node may send interference-related information of the cell to the first network node. For example, the second network node periodically sends interference-related information of the cell to the first network node. In addition, the second network node may also send geographic location information of the cell to the first network node when the base station is initially deployed.

[0195] In step 4, the first network node clusters at least one cell based on the interference-related information (and / or cell geographic location information) of the at least one cell, such as Fig.23 As shown in (b), the cell interference between cell clusters is small, the strong interference is concentrated in the cell cluster, and the interference is subsequently processed in a cell cluster, so that the complexity of the algorithm is reduced. However, the present application may also not cluster the cells, but directly perform subsequent steps on at least one cell. In the following description, in order to fully reflect all the technical concepts proposed by the present application, the subsequent description is based on clustering the cells.

[0196] In step 5, based on the interference-related information between the cells, the first resource allocation pattern of at least one cell in the cell cluster is adjusted. Specifically, in order to avoid or reduce interference between adjacent cells in the cell cluster, the first network node adjusts the first resource allocation pattern (i.e., the service delay-aware energy-saving pattern) of the cells in the same cell cluster through, for example, FFNN, that is, the position of the resource units used for each cell is adjusted in the time domain so that the resources to be sent are staggered in the time domain, thereby generating a second resource allocation pattern (also referred to as an anti-interference energy-saving pattern), and calculating the SINR gain of the second resource allocation pattern, such as Fig.23As shown in (c), since the interference between cells in each cell cluster is large, in this step, based on the mobility level of the resource unit, the first network node jointly adjusts the first resource allocation pattern of the cell (i.e., the service delay-aware energy-saving pattern) to avoid or reduce interference between cells in the cell cluster.

[0197] Step 6: The second network node may select a corresponding energy-saving weight factor β from a plurality of preset energy-saving weight factors according to the deployment scenario and / or service load related information of the cell corresponding thereto, and send the selected energy-saving weight factor β to the first network node.

[0198] Step 7: The first network node uses the SINR gain to compress the transmission duration of the second resource allocation pattern (i.e., the anti-interference energy-saving pattern) of the cell according to the energy-saving weight factor β to obtain a third resource allocation pattern of at least one cell to meet the energy-saving target of the cell, such as Fig.23 As shown in (d) in the figure, the service transmission time can be shortened under the same traffic condition, and more time domain resources can be shut down for energy saving, thereby further saving energy.

[0199] In step 8, the first network node determines whether the third resource allocation pattern currently obtained (i.e., the compressed anti-interference energy-saving pattern) meets the preset criteria for the end of iteration. If not, steps 5 to 7 are repeated, wherein step 6 can be selectively performed according to whether the deployment scenario and / or service load-related information of the cell has changed. Through the iterative operation of steps 5 to 7, the anti-interference energy-saving pattern can be continuously compressed to maximize energy saving. The compressed transmission duration will change the anti-interference energy-saving pattern in the time domain, and the interference between cells will also change. Based on the new anti-interference energy-saving pattern, the interference and energy-saving duration can be continuously adjusted until the preset criteria are met.

[0200] In step 9, if the preset criteria for the end of iteration are met, the first network node can output its final anti-interference energy-saving pattern (i.e., the final third resource allocation pattern) to the second network node, and the second network node uses a scheduler to perform resource scheduling based on the anti-interference energy-saving pattern.

[0201] Through the above scheme, less cell power consumption can be achieved without sacrificing system performance, and inter-cell interference can be reduced, thereby improving transmission efficiency. In the above description, the first neural network and the second neural network used are fully connected networks obtained through training. The training method may be offline training, however, the present application is not limited to this, and the training method may also be online training, that is, the network node performs online training on the first neural network and the second neural network according to a preset period using information reported from the base station. In addition, the training may be performed by the above-mentioned network nodes, or by other network nodes. That is to say, the first neural network and the second neural network used in the method performed by the network node described above may also be neural networks obtained by direct training by the network node, or may be trained neural networks received from the outside. Refer to the following Fig.24 Describe the deployment scenarios between network nodes and base stations in detail.

[0202] Fig.24 is a diagram showing a deployment scenario between a network node and a base station according to an exemplary embodiment of the present application. Fig.24 In the embodiment described in, the network node is the first network node described above, namely, the RIC, but the present application is not limited thereto.

[0203] Reference Fig.24 , refer to the above Figure 1 The method described above and performed by the first network node is deployed as an xAPP on the RIC, as described above with reference to Fig.21 The described method performed by the second network node is deployed on the DU of the base station (e.g., gNB).

[0204] The pattern generation and adjustment module includes a pattern generation submodule and a pattern adjustment submodule. First, the pattern generation and adjustment module obtains the attribute-related information of the cell reported by the base station from the RIC database, such as but not limited to historical service information and energy-saving time granularity information, and may also include common signals and RS.

[0205] Thereafter, the pattern generation submodule allocates available resources to the cell based on the common signal and RS included in the received attribute-related information, determines the predicted service load of at least one cell and the mobility level corresponding to the resource units of the allocated resources through a trained first neural network based on the historical service information included in the attribute-related information, and then determines a first resource allocation pattern (hereinafter referred to as a service delay-aware energy-saving pattern) corresponding to at least one cell based on the energy-saving time granularity information, the allocated available resources, the predicted service load, and the mobility level corresponding to the resource units of the allocated resources included in the attribute-related information.

[0206] Optionally, based on the cell geographic location information and interference related information of the cell reported by the base station in the database, the clustering module may cluster at least one cell, and transmit the clustering result to the pattern generation and adjustment module.

[0207] Then, the pattern adjustment submodule in the pattern generation and adjustment module uses the trained second neural network to adjust the first resource allocation pattern of at least one cell (or at least one cell in the same cell cluster) to obtain the corresponding second resource allocation pattern (i.e., anti-interference energy-saving pattern).

[0208] Then, the pattern generation and adjustment module outputs the generated second resource allocation pattern to the pattern compression module. The pattern compression module can compress the second resource allocation pattern of at least one cell based on the energy-saving weight factor (also referred to as the weight factor) of the cell reported by the base station to obtain the final third resource allocation pattern (i.e., the final anti-interference energy-saving pattern). Thereafter, the pattern compression module sends the obtained final third resource allocation pattern to the corresponding DU.

[0209] The MAC layer in the DU executes a resource scheduler according to the received third resource allocation pattern to control the physical layer PHY-C to send data to the UE in the cell, and the PHY-C periodically sends measurement data to the MAC layer, which can be used to calculate the SINR and be reported by the base station to the first network node for subsequent resource allocation pattern generation.

[0210] Thereafter, the MAC layer collects the measurement data reported by the PHY-C layer, and may selectively report the collected measurement data (e.g., historical service information, common signals, RS, energy-saving time granularity information, interference-related information) to the first network node, and may report the cell geographic location information to the first network node when the base station is initially deployed. In addition, the MAC layer will also send the selected energy-saving weight factor to the first network node. The reported information may be stored in a database, and may be used by the first network node for inference operations of service delay-aware energy-saving patterns and anti-interference energy-saving patterns, and may be used by the first network node to train the first neural network and the second neural network using this information, however, the present application is not limited thereto, and the first network node may also send the reported information to other network nodes, and the other network nodes may train the first neural network and the second neural network.

[0211] Fig.25A is a block diagram illustrating a first network node according to an exemplary embodiment of the present application.

[0212] like Fig.25AAs shown in FIG. 1 , the first network node 2510 includes a transceiver 2511 and a processor 2512, wherein the processor 2512 is coupled to the transceiver 2511 and is configured to execute the above reference Figures 1 to 20 For details of the operation of the method performed by the first network node, see Figures 1 to 20 The description will not be repeated here.

[0213] Fig.25B is a block diagram illustrating a second network node according to an exemplary embodiment of the present application.

[0214] like Fig.25B As shown in FIG. 1 , the first network node 2520 includes a transceiver 2521 and a processor 2522, wherein the processor 2522 is coupled to the transceiver 2521 and is configured to execute the above reference Fig.21 For details of the operation of the method performed by the second network node, see Fig.21 The description will not be repeated here.

[0215] An embodiment of the present disclosure also provides an electronic device, which includes a processor and, optionally, may also include at least one transceiver and / or at least one memory coupled to the at least one processor, wherein the at least one processor is configured to execute the steps of the method provided in any optional embodiment of the present disclosure.

[0216] Fig.26 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present invention is shown in FIG. Fig.26 As shown, Fig.26 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, each of the processor 4001, the memory 4003 and the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present disclosure. Optionally, the electronic device may be a first network node, a second network node or a third network node.

[0217] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0218] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.26 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0219] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0220] The memory 4003 is used to store computer programs or executable instructions for executing the embodiments of the present disclosure, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer programs or executable instructions stored in the memory 4003 to implement the steps shown in the above method embodiments.

[0221] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program or instructions stored thereon. When the computer program or instructions are executed by at least one processor, the steps and corresponding contents of the aforementioned method embodiment can be executed or implemented.

[0222] The embodiments of the present disclosure also provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0223] The method executed by the network node of the present application and the corresponding network node are described above respectively. The effect of applying the method is compared with the prior art below.

[0224] Fig.27A is a diagram showing the comparison results between the solution of the present application and the prior art under the first evaluation condition.

[0225] First evaluation condition:

[0226] (1) Control group: three adjacent cells, multiple user equipment (UE), user service type is constant bit rate (CBR) service (1300 bytes per 10ms), where the distribution of the three adjacent cells is as follows: Fig.27B As shown in . The network node of the cell performs downlink communication with multiple UEs. The scheme proposed in the present application (which may be referred to as an energy-saving scheme based on inter-cell interference coordination) is adopted to set the load of each cell to be independent and different, and record the time domain resources used by the cell, the Internet Protocol (Internet Protocol, IP) throughput (Throughput, Tput) of the UE and the energy consumption of the network node. The time domain resource usage distribution of each cell under the scheme of the present application, the IP Tput of the UE and the energy consumption of the network node are used as control values. These control values ​​are used as a reference for the experimental group (that is, used as a reference for the time domain resource usage distribution, the IP Tput of the UE and the energy consumption of the network node under the traditional energy-saving scheme under the same load conditions of the experimental group).

[0227] (2) Experimental Group 1: The cell, UE distribution, number and service configuration are the same as those of the control group, except that the first energy-saving solution of traditional symbol / time slot on / off is adopted in Experimental Group 1. In the same service scenario (i.e., the number of UEs and service types are the same), the first energy-saving solution will turn off the RF transmission when there is no service transmission, that is, turn off the RU.

[0228] (3) Experimental Group 2: The distribution, number and service configuration of cells, UEs and services are the same as those of the control group, except that the experimental group 2 adopts the existing second energy-saving scheme which is different from the first energy-saving scheme. Under the same service scenario (i.e., the number of UEs and service types are the same), the second energy-saving scheme concentrates the transmission time of the cells through service prediction, and the concentrated energy-saving time of the cells can save more power.

[0229] from Fig.27A It can be seen that the time domain resource distribution of the first energy-saving scheme of experimental group 1 is relatively random, and the energy-saving effect is limited. The second energy-saving scheme of experimental group 2 realizes centralized time domain resource distribution after considering service prediction, but because each cell independently implements the energy-saving scheme, the time domain resources transmitted by each cell overlap a lot, which affects the IP Tput of the UE. However, the scheme of the present application can coordinate between cells, avoid interference between cells, and further save energy while improving transmission efficiency.

[0230] In addition, the solution of the present application has higher IP Tput and lower energy consumption.

[0231] Fig.28 is a diagram showing the comparison results between the solution of the present application and the prior art under the second evaluation condition.

[0232] Second evaluation criteria:

[0233] (1) Control group: three adjacent cells, multiple UEs, user service types are Voice Over Internet Protocol (VOIP) service and CBR service (1300 bytes per 10ms), where the distribution of the three adjacent cells is as follows: Fig.27B As shown in . Downlink communication is performed between the network node of the cell and multiple UEs. The solution proposed in this application (which can be called an energy-saving solution based on inter-cell interference coordination) is adopted to set the load of each cell independently and differently, and record the time domain resources used by the network node, the IP Tput of the UE and the energy consumption of the network node.

[0234] (2) Experimental Group 1 and Experimental Group 2: The cells, UE distribution, number and service configuration are the same as those of the control group. The difference is that Experimental Group 1 adopts the first energy-saving scheme of traditional symbol / time slot on / off, while Experimental Group 2 adopts the existing second energy-saving scheme different from the first energy-saving scheme.

[0235] Compared with the first evaluation condition, the second evaluation condition adds VOIP service. Fig.28 The yellow mark in the middle shows the time domain location of the delay-sensitive VOIP service. Fig.28 As shown in , even if there are time-domain sensitive services, the time-domain resource distribution of the solution proposed in this application can achieve the effect of further saving time-domain resources.

[0236] Compared with the second energy-saving solution, the solution proposed in this application further reduces energy consumption, and the IE average IP Tput performance is the best among the compared energy-saving solutions.

[0237] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in the text.

[0238] It should be understood that, although the flowchart of the embodiment of the present disclosure indicates each operation step by arrows, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios with different execution times, the execution order of these sub-steps or stages can be flexibly configured according to demand, and the embodiment of the present disclosure does not limit this.

[0239] The above text and drawings are provided only as examples to help readers understand the present disclosure. They are not intended and should not be interpreted as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the contents disclosed herein, it is obvious to those skilled in the art that the embodiments and examples shown can be changed without departing from the scope of the present disclosure, and other similar implementation means based on the technical ideas of the present disclosure are adopted, which also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method performed by a first network node in a communication system, comprising: Determine a first resource allocation pattern corresponding to at least one cell; Based on the inter-cell interference related information, the first resource allocation pattern of the at least one cell is adjusted to obtain a second resource allocation pattern corresponding to the at least one cell.

2. The method according to claim 1, wherein: The first resource allocation pattern includes a movable level corresponding to a resource unit, and the movable level indicates a level of resource adjustment of the resource unit in the time domain.

3. The method according to claim 2, wherein: The mobility level is related to at least one of the following: service load, service priority, and delay-related characteristics of the service.

4. The method according to any one of claims 1 to 3, wherein: Determining a first resource allocation pattern corresponding to at least one cell includes: Obtaining attribute-related information of at least one cell; Based on the attribute-related information, a first resource allocation pattern corresponding to at least one cell is determined.

5. The method according to claim 2, wherein: The attribute-related information includes historical service information and / or energy-saving time granularity information.

6. The method according to claim 5, wherein: Determining a first resource allocation pattern corresponding to at least one cell based on the attribute related information includes: allocating available resources to the at least one cell; Based on the historical service information, determining, via a first neural network, a predicted service load of the at least one cell and a mobility level corresponding to a resource unit of the allocated resources; A first resource allocation pattern corresponding to the at least one cell is determined according to the energy-saving time granularity information, the allocated available resources, the predicted service load, and the mobility level corresponding to the resource units of the allocated resources.

7. The method according to claim 6, wherein: The first neural network includes a first sub-neural network, a second sub-neural network and a third sub-neural network, Based on the historical service information, determining, via a first neural network, a predicted service load of the at least one cell and a mobility level corresponding to a resource unit of the allocated resources, comprising: Based on the historical service information, determining the predicted service load of the at least one cell by a first sub-neural network; Determining delay-related characteristics of the resource units of the allocated resources through a second sub-neural network based on the predicted service load and the service type corresponding to the resource units of the allocated resources; Based on the delay-related characteristics of the resource units of the allocated resources, the movable levels corresponding to the resource units of the allocated resources are determined through the third sub-neural network.

8. The method according to claim 7, wherein: The historical service information includes at least one historical service log; Determining the predicted service load of the at least one cell by a first sub-neural network based on the historical service information includes: Based on at least one historical service log of at least one cell, determining a predicted service load of each resource unit corresponding to each historical service log through a temporal convolutional network in a first sub-neural network; Through the fusion network in the first sub-neural network, for the at least one cell, the predicted service load of each resource unit corresponding to each historical service log is fused to obtain the predicted service load of the at least one cell.

9. The method according to claim 2 or 7, wherein: The delay-related characteristics include at least one of resource type, service priority, and acceptable maximum delay.

10. The method according to any one of claims 1 to 3, adjusting the first resource allocation pattern of the at least one cell based on the inter-cell interference related information, comprising: Clustering the at least one cell to obtain at least one cell cluster; Based on the interference related information between cells in each cell cluster, a first resource allocation pattern of at least one cell in the cell cluster is adjusted to obtain a corresponding second resource allocation pattern.

11. The method according to claim 10, clustering the at least one cell, comprising: The at least one cell is clustered based on the interference-related information of the at least one cell.

12. The method according to claim 11, wherein: The interference intensity between cells in a cell cluster is not less than a set threshold; the interference intensity between cells in different cell clusters is less than the set threshold.

13. The method according to claim 2 or 3, wherein: Adjusting the first resource allocation pattern of the at least one cell based on the inter-cell interference related information includes: The first resource allocation pattern of the at least one cell is adjusted based on the mobility level corresponding to the resource unit and the interference relationship between the cells.

14. The method according to claim 13, wherein: Adjusting the first resource allocation pattern of the at least one cell based on the mobility level corresponding to the resource unit and the interference relationship between the cells includes: Based on the mobility level corresponding to the resource unit and the interference relationship between cells, the position of the resource unit in the first resource allocation pattern of the at least one cell in the time domain is adjusted through the second neural network.

15. The method according to claim 14, adjusting the position of the resource unit in the first resource allocation pattern of the at least one cell in the time domain through a second neural network based on the mobility level corresponding to the resource unit and the interference relationship between the cells, comprising: Segmenting the first resource allocation pattern of the at least one cell according to the first length to obtain at least one group of resource allocation pattern segments; Based on the mobility level corresponding to the resource unit and the interference relationship between the cells, adjusting the position of the resource unit in the at least one group of resource allocation pattern fragments in the time domain through the second neural network; The at least one set of adjusted resource allocation pattern fragments are spliced ​​to obtain a corresponding second resource allocation pattern.

16. The method according to claim 2 or 3, further comprising: The continuous resource units with the same mobility level in the second resource allocation pattern of the at least one cell are compressed to obtain a third resource allocation pattern of the at least one cell.

17. A method performed by a second network node in a communication system, comprising: Receiving a second resource allocation pattern of at least one cell sent by the first network node, wherein the second resource allocation pattern is obtained after the second network node adjusts the first resource allocation pattern of the at least one cell based on inter-cell interference related information; Corresponding resource scheduling processing is performed based on the second resource allocation pattern.

18. A first network node, comprising: transceiver, used to send and receive signals; as well as A processor is coupled to the transceiver and configured to execute the method according to any one of claims 1 to 16.

19. A second network node, comprising: transceiver, used to send and receive signals; as well as A processor is coupled to the transceiver and configured to execute the method as claimed in any one of claims 17.

20. A computer-readable storage medium storing instructions, wherein: When the instructions are executed by at least one processor, the at least one processor is prompted to perform the method according to any one of claims 1 to 17.

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