Noma-based power grid node clustering and cooperative transmission optimization method, device and medium

Abstract

The application provides a power grid node clustering cooperative transmission optimization method based on NOMA, equipment and medium; according to node position, service priority and channel fading coefficient, a clustering algorithm is used to divide the nodes of a cell into multiple clusters and select a cluster head with the maximum gain, the gain of each node to the cluster head is measured, the uplink rate and upload delay are calculated in combination with the data volume, non-orthogonal multiple access pairing is carried out in each cluster based on the gain, inter-cluster cooperative forwarding decision is executed by the cluster head, cooperative transmission delay is output, and the total delay of each cluster and the global delay are calculated by comprehensively considering the upload delay and the cooperative transmission delay; a mixed integer nonlinear programming is established by taking the maximum weighted total throughput and the minimum global delay as the target; the improved particle swarm optimization algorithm is used for solving: the discrete and continuous variables are uniformly coded, the fitness function is designed, the speed position is adaptively updated, and high concurrency and low delay transmission are realized.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and cooperative transmission optimization technology for smart grids, specifically a method, device, and medium for optimizing cooperative transmission of power grid nodes clustered based on NOMA. Background Technology

[0002] With the accelerated construction of new power systems, the operation mode of distribution networks is gradually shifting from traditional passive monitoring and centralized dispatch to an intelligent operation mode that emphasizes multi-source collaboration, rapid perception, and real-time response. In scenarios such as distribution automation, condition monitoring, fault alarm, distributed power grid connection control, and flexible load coordinated regulation, a large number of grid nodes need to complete the collection, transmission, and interaction of condition information at high frequency. These services typically have obvious high-concurrency access characteristics and strict latency constraints, which place higher demands on the access capacity, spectrum utilization, and real-time transmission performance of communication networks. Especially against the backdrop of large-scale access of distributed power sources, power electronic equipment, and new loads, the number of nodes continues to increase, and service interactions become increasingly frequent. Traditional orthogonal access methods are no longer sufficient to meet the needs of high-concurrency access of massive distributed nodes and low-latency transmission of critical services.

[0003] Currently, there are two main approaches to improving the access capacity and transmission efficiency of smart grid nodes: one is to adopt the traditional orthogonal multiple access (OMA) method, which achieves access control by allocating time-frequency resources for nodes; the other is to introduce non-orthogonal multiple access (NOA) technology, supporting multiple nodes to share access within the same resource block. The former is relatively simple to implement, but when the number of nodes grows rapidly and access requests arrive in dense clusters, it is prone to problems such as insufficient resource blocks, increased access waiting time, and difficulty in guaranteeing the latency of critical services. While the latter can support more nodes under the same bandwidth conditions, it mostly focuses on pairing and power allocation designs for a few user scenarios, neglecting issues such as uneven node distribution, significant differences in service priorities, and insufficient inter-cluster coordination in scenarios with massive distributed nodes. When a large number of nodes directly compete for limited wireless resources, the system may still experience escalating access conflicts, increased scheduling complexity, and a continuous increase in overall transmission latency. Summary of the Invention

[0004] This invention provides a NOMA-based power grid node clustering cooperative transmission optimization method, which achieves spectrum reuse and high-concurrency access through clustering and non-orthogonal multiple access pairing, and achieves global latency minimization and load balancing through cooperative forwarding and particle swarm optimization, thereby solving the problems in the background technology.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A NOMA-based power grid node clustering cooperative transmission optimization method involves executing the following steps using computer equipment: S1: Using the location of each node, service priority, and channel fading coefficient to the base station as input, a clustering algorithm is used to divide the nodes in the same cell into multiple clusters; within each cluster, the node with the largest channel fading coefficient is selected as the cluster head, and the clustering results and cluster head set are output. S2: Based on the clustering results and cluster head information, measure the channel fading coefficient, distance, interference and noise of each node to the cluster head, and calculate the uplink transmission rate and upload delay of each node in combination with the node data volume, and output the node rate and intra-cluster upload delay. S3: Within each cluster, perform non-orthogonal multiple access pairing based on the measured channel fading coefficient, and output the pairing relationship; S4: Based on the cluster head set and pairing relationship, each cluster head makes inter-cluster cooperative forwarding decisions and outputs the inter-cluster cooperative transmission delay of each cluster; S5: Combine the intra-cluster upload latency and inter-cluster collaborative transmission latency to obtain the total latency of each cluster and the latency of each cell. Take the maximum value of the total latency of all clusters in the cell to calculate the global latency value. S6, with the goal of maximizing the weighted total throughput of the system and minimizing the global latency, establishes a mixed integer nonlinear programming problem that includes decision variables for clustering, pairing, cooperative transmission, bandwidth allocation, power allocation, as well as constraints on rate, latency, and resource capacity. S7 addresses nonlinear programming problems by uniformly encoding discrete and continuous variables as particle positions, designing a fitness function, updating particle velocity and position through adaptive inertia weights and dynamic learning factors, and introducing a feasible region repair mechanism. It iteratively outputs the optimal clustering scheme, NOMA pairing strategy, cooperative transmission selection, bandwidth allocation, and power allocation, enabling high-concurrency access and low-latency cooperative transmission for massive power grid nodes.

[0006] Preferably, the specific steps of S1 are as follows: Let the set of cell numbers be . J represents the total number of cells, and the th cell is... The set of power grid nodes within a community is ,in Indicates the first The total number of nodes in each community; Each node collects switch status, voltage, current, and power status data through electrical measurements. In the community Data attributes in the data are represented as ,in Indicates the community The size of the node data, Indicates the community The latency constraints of node data; Based on the number of nodes in the cell, the amount of node data, and the link status, the first... The power grid nodes within each community are divided into There are 3 clusters, each considered as a relatively independent cooperative transmission unit. The clustering results for each cell are represented as follows:

[0007] in, Indicates the first Clustering results for each cell Indicates the first The first in the community A cluster of nodes, The number of nodes in a cluster is expressed as follows:

[0008] Within each cluster, a node is selected as the cluster head to handle intra-cluster coordination, information aggregation, and collaborative forwarding. Let the first node be the cluster head. The first community The cluster head of each cluster is denoted as The cluster head selection strategy is then expressed as:

[0009] in, Indicates in Nodes within the community The channel fading coefficient to the base station, express Service priority of node n within the community. Represents a node In the The first community The average distance from each cluster to other nodes within the cluster. , and These are the weighting coefficients.

[0010] Preferably, the specific steps of S2 are as follows: Node data is uploaded to the cluster head node via a wireless link and transmitted within the cluster using NOMA. Let the node... With nodes In the The first community If a cluster shares the same resource block for NOMA upload, The corresponding received signal-to-interference-plus-noise ratios are expressed as follows:

[0011]

[0012] The corresponding uplink transmission rates are expressed as follows:

[0013]

[0014] in, and They are nodes and nodes In the The first community The received signal-to-interference-plus-noise ratio within each cluster and They are nodes and nodes In the The first community Uplink transmission rate within a cluster, and Assigned to nodes respectively and nodes In the The first community Upload bandwidth within a cluster; and They are nodes and nodes In the The first community Transmission power within a cluster, and They are nodes and nodes In the The first community Channel fading coefficient of each cluster head and They are nodes and nodes In the From the first community to the first Communication distance of each cluster head; This is the path loss index. The power of Gaussian white noise. In the first The first community Interference within a cluster; node Uploaded to The transmission delay of each cluster head is expressed as:

[0015] in, Let n be the size of the status data that node n in the j-th cell needs to upload. The first community The intra-cluster upload latency of a cluster is represented as follows:

[0016] Where u represents the uplink transmission delay from the node to the cluster head, i.e., the intra-cluster upload phase. For nodes The transmission latency for uploading to the cluster head, For the first The first community Intra-cluster upload latency for each cluster.

[0017] Preferably, the specific steps of S3 are as follows: Within each cluster, user pairing is performed using the NOMA method, with a binary variable. Indicates the first The first community Nodes within a cluster With nodes Whether they are paired with the same resource block, i.e.:

[0018] To ensure NOMA decoding performance, a pairing strategy based on channel fading coefficient sorting is adopted within the cluster. Nodes are arranged in descending order of channel fading coefficient, and the node at the top of the sorting is paired with the node at the bottom of the sorting that meets the set service priority conditions.

[0019] Preferably, the specific steps of S4 are as follows: Let the cooperative transmission strategy be: ,like Then it means the first The first community The data for the cluster is from the _th The base stations in each cell coordinate to receive or forward data; otherwise, they do not coordinate to transmit data. This represents the index of the target cooperative reception cell, and its value range includes all other cells except the source cell j. cluster The aggregated data volume is represented as:

[0020] in, Let be the set of power grid nodes contained in the k-th cluster within the j-th cell. This represents the total aggregated data of the j-th cell and the k-th cluster. The transmission rate from the cluster head to the cooperating receiving node is expressed as:

[0021] Wherein, the superscript r represents the cooperative forwarding delay, that is, the forwarding phase in which the cluster head forwards data to the other cluster heads. For the cluster head of the k-th cluster in the j-th cell to the... Inter-cell forwarding bandwidth For the cluster head of the k-th cluster in the j-th cell to the... Inter-cell forwarding power; For the cluster head of the k-th cluster in the j-th cell to the... Channel coefficients between cells to the cooperating receiver, For the cluster head of the k-th cluster in the j-th cell to the... Communication distance between individual cells Interference during the forwarding process; cluster The cooperative transmission delay is expressed as:

[0022] in, For the j-th cell and k-th cluster, data is sent from the cluster head to the k-th cluster. The transmission latency required for each cell base station.

[0023] Preferably, the specific steps of S5 are as follows: No. The first community The total transmission delay of a cluster consists of the intra-cluster upload delay and the inter-cluster cooperative transmission delay, expressed as:

[0024] Since the transmission processes of each cluster within the same cell are performed in parallel, therefore the first... The total transmission delay for each cell is:

[0025] The maximum transmission delay among all cells is taken as the global transmission delay, expressed as:

[0026] in, The total latency of the j-th cell and the k-th cluster is given by [the total latency of the j-th cell and the k-th cluster]. Let T be the total delay of the j-th cell, and T be the global transmission delay. Let j be the number of clusters in the j-th cell. For the j-th cell The total transmission delay of each cluster, i.e., the last cluster, is defined. After defining the total transmission delay of each cluster, the total transmission delay of the cell, and the global transmission delay, the following objective function is constructed based on the node rate and the global delay:

[0027] in, This represents the joint optimization objective function. and The weighting coefficients for rate gain and latency penalty are specified. Preferably, the specific steps of S7 are as follows: Let the particle position vector be represented as:

[0028] Where X represents the position vector of the particle, B is the bandwidth allocation vector, including the upload bandwidth of all nodes and the forwarding bandwidth of all cluster heads, and p is the power allocation vector, including the transmit power of all nodes and the forwarding power of all cluster heads; The fitness function is expressed as:

[0029] in, This indicates various constraints and penalties for breach of contract. To correspond to the penalty coefficient, Q represents the total number of categories of all constraints that need to be processed in the optimization problem, and q is a loop index variable that iterates from 1 to Q to represent each type of constraint in turn; The formulas for updating particle velocity and position are as follows:

[0030]

[0031] Where t represents the number of iterations in the particle swarm optimization algorithm. Let be the velocity vector of particle i at the t-th iteration. Let be the position vector of particle i at the t-th iteration. For adaptive inertia weights, and These are dynamic individual learning factors and group learning factors, respectively. and A random number uniformly distributed within the interval [0,1]. This represents the optimal position for an individual particle. The globally optimal position; During particle update, the discrete variables are rounded and thresholded, and the clustering results, NOMA pairing relationship and cooperative transmission strategy are guaranteed to meet the constraints through the feasible region repair mechanism. After multiple iterations, the optimal node clustering scheme, optimal user pairing strategy, optimal cooperative transmission selection, optimal bandwidth allocation and optimal power allocation are output.

[0032] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0033] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0034] As can be seen from the above technical solution compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves concurrent uploading of multiple nodes on the same resource block and eliminates decoding interference from strong signal nodes to weak signal nodes by using node clustering and non-orthogonal multiple access pairing based on intra-cluster channel fading coefficient sorting. This results in an increase in spectrum reuse multiple and enhanced high-concurrency access capability for massive nodes.

[0035] 2. This invention uses cluster heads to make real-time judgments on link quality and load status, and then forwards data to the nearest base station or cluster head with the best signal power. It also combines particle swarm optimization to jointly optimize clustering, pairing, coordination, bandwidth and power, thereby realizing the dynamic migration of load from poor links to good links, achieving the minimization of global transmission latency and effective mitigation of network congestion. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the method steps of the present invention; Figure 2 This is a schematic diagram of the S7 solution optimization process of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0038] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but should not be used to limit the scope of the present invention.

[0039] A NOMA-based power grid node clustering cooperative transmission optimization method, such as Figure 1 As shown, perform the following steps using a computer device: S1: Using the location of each node, service priority, and channel fading coefficient to the base station as input, a clustering algorithm is used to divide the nodes in the same cell into multiple clusters; within each cluster, the node with the largest channel fading coefficient is selected as the cluster head, and the clustering results and cluster head set are output. S2: Based on the clustering results and cluster head information, measure the channel fading coefficient, distance, interference and noise of each node to the cluster head, and calculate the uplink transmission rate and upload delay of each node in combination with the node data volume, and output the node rate and intra-cluster upload delay. S3: Within each cluster, perform non-orthogonal multiple access pairing based on the measured channel fading coefficient, and output the pairing relationship; S4: Based on the cluster head set and pairing relationship, each cluster head makes inter-cluster cooperative forwarding decisions and outputs the inter-cluster cooperative transmission delay of each cluster; S5: Combine the intra-cluster upload latency and inter-cluster collaborative transmission latency to obtain the total latency of each cluster and the latency of each cell. Take the maximum value of the total latency of all clusters in the cell to calculate the global latency value. S6, with the goal of maximizing the weighted total throughput of the system and minimizing the global latency, establishes a mixed integer nonlinear programming problem that includes decision variables for clustering, pairing, cooperative transmission, bandwidth allocation, power allocation, as well as constraints on rate, latency, and resource capacity. S7 addresses nonlinear programming problems by uniformly encoding discrete and continuous variables as particle positions, designing a fitness function, updating particle velocity and position through adaptive inertia weights and dynamic learning factors, and introducing a feasible region repair mechanism. It iteratively outputs the optimal clustering scheme, NOMA pairing strategy, cooperative transmission selection, bandwidth allocation, and power allocation, enabling high-concurrency access and low-latency cooperative transmission for massive power grid nodes.

[0040] Example: In this embodiment, an industrial park is undergoing smart grid transformation, deploying hundreds of distribution automation terminals across multiple adjacent power supply zones. Each terminal needs to collect switch status, voltage, current, and power data periodically, with the data packet size varying within a certain range, requiring high real-time performance. Due to the dense node distribution and varying service priorities (e.g., fault indication nodes have higher priority than ordinary metering nodes), traditional orthogonal frequency division multiple access (OFDMA) methods, with their limited resource block count, struggle to meet the dual demands of high-concurrency access and low-latency transmission. Therefore, this invention employs a NOMA-based power grid node clustering collaborative transmission optimization method, the specific implementation process of which is as follows: Let the set of cell numbers be . J represents the total number of cells, and the th cell is... The set of power grid nodes within a community is ,in Indicates the first The total number of nodes in each community; Each node collects switch status, voltage, current, and power status data through electrical measurements. In the community Data attributes in the data are represented as ,in Indicates the community The size of the node data, Indicates the community The latency constraints of node data; Based on the number of nodes in the cell, the amount of node data, and the link status, the first... The power grid nodes within each community are divided into There are 3 clusters, each considered as a relatively independent cooperative transmission unit. The clustering results for each cell are represented as follows:

[0041] in, Indicates the first Clustering results for each cell Indicates the first The first in the community A cluster of nodes, The number of nodes in a cluster is expressed as follows:

[0042] Within each cluster, a node is selected as the cluster head to handle intra-cluster coordination, information aggregation, and collaborative forwarding. Let the first node be the cluster head. The first community The cluster head of each cluster is denoted as The cluster head selection strategy is then expressed as:

[0043] in, Indicates in Nodes within the community The channel fading coefficient to the base station, express Service priority of node n within the community. Represents a node In the The first community The average distance from each cluster to other nodes within the cluster. , and These are the weighting coefficients.

[0044] Node data is uploaded to the cluster head node via a wireless link and transmitted within the cluster using NOMA. Let the node... With nodes In the The first community If a cluster shares the same resource block for NOMA upload, The corresponding received signal-to-interference-plus-noise ratios are expressed as follows:

[0045]

[0046] The corresponding uplink transmission rates are expressed as follows:

[0047]

[0048] in, and They are nodes and nodes In the The first community The received signal-to-interference-plus-noise ratio within each cluster and They are nodes and nodes In the The first community Uplink transmission rate within a cluster, and Assigned to nodes respectively and nodes In the The first community Upload bandwidth within a cluster; and They are nodes and nodes In the The first community Transmission power within a cluster, and They are nodes and nodes In the The first community Channel fading coefficient of each cluster head and They are nodes and nodes In the From the first community to the first Communication distance of each cluster head; This is the path loss index. The power of Gaussian white noise. In the first The first community Interference within a cluster; node Uploaded to The transmission delay of each cluster head is expressed as:

[0049] in, Let n be the size of the status data that node n in the j-th cell needs to upload. The first community The intra-cluster upload latency of a cluster is represented as follows:

[0050] in, For nodes The transmission latency for uploading to the cluster head, For the first The first community Intra-cluster upload latency for each cluster.

[0051] Within each cluster, user pairing is performed using the NOMA method, with a binary variable. Indicates the first The first community Nodes within a cluster With nodes Whether they are paired with the same resource block, i.e.:

[0052] To ensure NOMA decoding performance, a pairing strategy based on channel fading coefficient sorting is adopted within the cluster. Nodes are arranged in descending order of channel fading coefficient, and the node at the top of the sorting is paired with the node at the bottom of the sorting that meets the set service priority conditions.

[0053] This invention organizes power grid nodes into clusters and selects cluster head nodes within each cluster to undertake coordination, aggregation, and forwarding functions, thereby realizing the transformation of massive distributed nodes from disordered competitive access to ordered access within clusters. By employing a non-orthogonal multiple access pairing mechanism based on channel fading coefficient sorting within the cluster, concurrent uploading and spectrum reuse of multiple nodes on the same resource block are achieved; Because of differences in load levels and link quality among different clusters, cluster head nodes can make coordinated transmission selections based on the load status of their cluster and the link conditions to the base station. When the link quality from a cluster head to the base station of its own cell is poor or the transmission latency is high, cluster data can be coordinated and forwarded to a neighboring cell base station or a neighboring cluster head can assist in uploading. Let the cooperative transmission strategy be: ,like Then it means the first The first community The data for the cluster is from the _th The base stations in each cell coordinate to receive or forward data; otherwise, they do not coordinate to transmit data. This represents the index of the target cooperative reception cell, and its value range includes all other cells except the source cell j. cluster The aggregated data volume is represented as:

[0054] in, Let be the set of power grid nodes contained in the k-th cluster within the j-th cell. This represents the total aggregated data of the j-th cell and the k-th cluster. The transmission rate from the cluster head to the cooperating receiving node is expressed as:

[0055] in, For the cluster head of the k-th cluster in the j-th cell to the... Inter-cell forwarding bandwidth For the cluster head of the k-th cluster in the j-th cell to the... Inter-cell forwarding power; For the cluster head of the k-th cluster in the j-th cell to the... Channel coefficients between cells to the cooperating receiver, For the cluster head of the k-th cluster in the j-th cell to the... Communication distance between individual cells Interference during the forwarding process; cluster The cooperative transmission delay is expressed as:

[0056] in, For the j-th cell and k-th cluster, data is sent from the cluster head to the k-th cluster. The transmission latency required for each cell base station.

[0057] No. The first community The total transmission delay of a cluster consists of the intra-cluster upload delay and the inter-cluster cooperative transmission delay, expressed as:

[0058] Since the transmission processes of each cluster within the same cell are performed in parallel, therefore the first... The total transmission delay for each cell is:

[0059] The maximum transmission delay among all cells is taken as the global transmission delay, expressed as:

[0060] in, The total latency of the j-th cell and the k-th cluster is given by [the total latency of the j-th cell and the k-th cluster]. Let T be the total delay of the j-th cell, and T be the global transmission delay. Let j be the number of clusters in the j-th cell. For the j-th cell The total transmission delay of the last cluster.

[0061] This invention jointly optimizes node clustering results, intra-cluster user pairing relationships, inter-cluster cooperative transmission strategies, bandwidth allocation, and power allocation. After obtaining the transmission rates of each node and the global transmission latency, to balance system transmission efficiency and low latency, the following objective function is constructed:

[0062] in, This represents the joint optimization objective function. and The weighting coefficients represent the rate gain and latency penalty. This invention uses cluster heads to collaboratively assess link quality and load status, and collaboratively forwards service data to nearby high-quality links or target base stations, achieving dynamic migration and collaborative transmission of inter-cluster service traffic, thus alleviating local congestion and reducing overall system latency.

[0063] Since the aforementioned optimization problem simultaneously incorporates discrete variables such as node clustering, NOMA user pairing, and cooperative transmission selection, as well as continuous variables such as bandwidth allocation and power allocation, and these variables are significantly coupled, this problem is a typical mixed-integer nonlinear programming problem. To improve solution efficiency while also considering global optimization capabilities, this invention employs an improved particle swarm optimization algorithm to solve this problem.

[0064] like Figure 2 As shown, let the particle position vector be represented as:

[0065] Where X represents the position vector of the particle, B is the bandwidth allocation vector, including the upload bandwidth of all nodes and the forwarding bandwidth of all cluster heads, and p is the power allocation vector, including the transmit power of all nodes and the forwarding power of all cluster heads; The fitness function is expressed as:

[0066] in, This indicates various constraints and penalties for breach of contract. To correspond to the penalty coefficient, Q represents the total number of categories of all constraints that need to be processed in the optimization problem, and q is a loop index variable that iterates from 1 to Q to represent each type of constraint in turn; The formulas for updating particle velocity and position are as follows:

[0067]

[0068] Where t represents the number of iterations in the particle swarm optimization algorithm. Let be the velocity vector of particle i at the t-th iteration. Let be the position vector of particle i at the t-th iteration. For adaptive inertia weights, and These are dynamic individual learning factors and group learning factors, respectively. and A random number uniformly distributed within the interval [0,1]. This represents the optimal position for an individual particle. The globally optimal position; During particle update, the discrete variables are rounded and thresholded, and the clustering results, NOMA pairing relationship and cooperative transmission strategy are guaranteed to meet the constraints through the feasible region repair mechanism. After multiple iterations, the optimal node clustering scheme, optimal user pairing strategy, optimal cooperative transmission selection, optimal bandwidth allocation and optimal power allocation are output.

[0069] This invention achieves collaborative optimization of discrete decision variables and continuous resource variables by jointly modeling node clustering, NOMA pairing, cooperative transmission, bandwidth allocation, and power allocation, and using an improved particle swarm optimization algorithm for unified solution. Compared with existing technologies, this makes resource allocation more reasonable, improves optimization solution efficiency, and enhances the overall transmission performance of the system.

[0070] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0071] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0072] This invention enables concurrent uploading of the same resource block by multiple nodes through clustering and non-orthogonal multiple access pairing, and achieves dynamic load migration through inter-cluster collaborative forwarding and particle swarm optimization, thereby improving spectrum reuse, minimizing global latency, and alleviating network congestion.

[0073] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the NOMA-based power grid node clustering cooperative transmission optimization methods described in the above embodiments.

[0074] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0075] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0076] For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media.

[0077] The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0078] It should be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0079] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0081] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A NOMA-based power grid node clustering cooperative transmission optimization method, characterized in that, Perform the following steps using a computer device: S1: Using the location of each node, service priority, and channel fading coefficient to the base station as input, a clustering algorithm is used to divide the nodes in the same cell into multiple clusters; within each cluster, the node with the largest channel fading coefficient is selected as the cluster head, and the clustering results and cluster head set are output. S2: Based on the clustering results and cluster head information, measure the channel fading coefficient, distance, interference and noise from each node to the cluster head, obtain the uplink transmission rate of each node, and calculate the upload delay according to the node data volume, and output the node rate and intra-cluster upload delay. S3: Within each cluster, perform non-orthogonal multiple access pairing based on the measured channel fading coefficient, and output the pairing relationship; S4: Based on the cluster head set and pairing relationship, each cluster head makes inter-cluster cooperative forwarding decisions and outputs the inter-cluster cooperative transmission delay of each cluster; S5: Combine the intra-cluster upload latency and inter-cluster collaborative transmission latency to obtain the total latency of each cluster and the latency of each cell. Take the maximum value of the total latency of all clusters in the cell to calculate the global latency value. S6, with the goal of maximizing the weighted total throughput of the system and minimizing the global latency, establishes a mixed integer nonlinear programming problem that includes decision variables for clustering, pairing, cooperative transmission, bandwidth allocation, power allocation, as well as constraints on rate, latency, and resource capacity. S7 addresses nonlinear programming problems by uniformly encoding discrete and continuous variables as particle positions, designing a fitness function, updating particle velocity and position through adaptive inertia weights and dynamic learning factors, and introducing a feasible region repair mechanism. It iteratively outputs the optimal clustering scheme, NOMA pairing strategy, cooperative transmission selection, bandwidth allocation, and power allocation, enabling high-concurrency access and low-latency cooperative transmission for massive power grid nodes.

2. The NOMA-based power grid node clustering cooperative transmission optimization method as described in claim 1, characterized in that: The specific steps of S1 are as follows: Let the set of cell numbers be . J represents the total number of cells, and the th cell is... The set of power grid nodes within a community is ,in Indicates the first The total number of nodes in each community; Each node collects switch status, voltage, current, and power status data through electrical measurements. In the community Data attributes in the data are represented as ,in Indicates the community The size of the node data, Indicates the community The latency constraints of node data; Based on the number of nodes in the cell, the amount of node data, and the link status, the first... The power grid nodes within each community are divided into There are 3 clusters, each considered as a relatively independent cooperative transmission unit. The clustering results for each cell are represented as follows: in, Indicates the first Clustering results for each cell Indicates the first The first in the community A cluster of nodes, The number of nodes in a cluster is expressed as follows: Within each cluster, a node is selected as the cluster head to handle intra-cluster coordination, information aggregation, and collaborative forwarding. Let the first node be the cluster head. The first community The cluster head of each cluster is denoted as The cluster head selection strategy is then expressed as: in, Indicates in Nodes within the community The channel fading coefficient to the base station, express Service priority of node n within the community. Represents a node In the The first community The average distance from each cluster to other nodes within the cluster. , and These are the weighting coefficients.

3. The NOMA-based power grid node clustering cooperative transmission optimization method as described in claim 2, characterized in that: The specific steps of S2 are as follows: Node data is uploaded to the cluster head node via a wireless link and transmitted within the cluster using NOMA. Let the node... With nodes In the The first community If a cluster shares the same resource block for NOMA upload, The corresponding received signal-to-interference-plus-noise ratios are expressed as follows: The corresponding uplink transmission rates are expressed as follows: in, and They are nodes and nodes In the The first community The received signal-to-interference-plus-noise ratio within each cluster and They are nodes and nodes In the The first community Uplink transmission rate within a cluster, and Assigned to nodes respectively and nodes In the The first community Upload bandwidth within a cluster; and They are nodes and nodes In the The first community Transmission power within a cluster, and They are nodes and nodes In the The first community Channel fading coefficient of each cluster head and They are nodes and nodes In the From the first community to the first Communication distance of each cluster head; This is the path loss index. The power of Gaussian white noise. In the first The first community Interference within a cluster; node Uploaded to The transmission delay of each cluster head is expressed as: in, Let n be the size of the status data that node n in the j-th cell needs to upload. The first community The intra-cluster upload latency of a cluster is represented as follows: Where u represents the uplink transmission delay from the node to the cluster head, i.e., the intra-cluster upload phase. For nodes The transmission latency for uploading to the cluster head, For the first The first community Intra-cluster upload latency for each cluster.

4. The NOMA-based power grid node clustering cooperative transmission optimization method as described in claim 3, characterized in that: The specific steps of S3 are as follows: Within each cluster, user pairing is performed using the NOMA method, with a binary variable. Indicates the first The first community Nodes within a cluster With nodes Whether they are paired with the same resource block, i.e.: To ensure NOMA decoding performance, a pairing strategy based on channel fading coefficient sorting is adopted within the cluster. Nodes are arranged in descending order of channel fading coefficient, and the node at the top of the sorting is paired with the node at the bottom of the sorting that meets the set service priority conditions.

5. The NOMA-based power grid node clustering cooperative transmission optimization method as described in claim 4, characterized in that: The specific steps of S4 are as follows: Let the cooperative transmission strategy be: ,like Then it means the first The first community The data for the cluster is from the _th The base stations in each cell coordinate to receive or forward data; otherwise, they do not coordinate to transmit data. This represents the index of the target cooperative reception cell, and its value range includes all other cells except the source cell j. cluster The aggregated data volume is represented as: in, Let be the set of power grid nodes contained in the k-th cluster within the j-th cell. This represents the total aggregated data of the j-th cell and the k-th cluster. The transmission rate from the cluster head to the cooperating receiving node is expressed as: Wherein, the superscript r represents the cooperative forwarding delay, that is, the forwarding phase in which the cluster head forwards data to the other cluster heads. For the cluster head of the k-th cluster in the j-th cell to the... Inter-cell forwarding bandwidth For the cluster head of the k-th cluster in the j-th cell to the... Inter-cell forwarding power; For the cluster head of the k-th cluster in the j-th cell to the... Channel coefficients between cells to the cooperating receiver, For the cluster head of the k-th cluster in the j-th cell to the... Communication distance between individual cells Interference during the forwarding process; cluster The cooperative transmission delay is expressed as: in, For the j-th cell and k-th cluster, data is sent from the cluster head to the k-th cluster. The transmission latency required for each cell base station.

6. The NOMA-based power grid node clustering cooperative transmission optimization method as described in claim 5, characterized in that: The specific steps of S5 are as follows: No. The first community The total transmission delay of a cluster consists of the intra-cluster upload delay and the inter-cluster cooperative transmission delay, expressed as: Since the transmission processes of each cluster within the same cell are performed in parallel, therefore the first... The total transmission delay for each cell is: The maximum transmission delay among all cells is taken as the global transmission delay, expressed as: in, The total latency of the j-th cell and the k-th cluster is given by [the total latency of the j-th cell and the k Let T be the total delay of the j-th cell, and T be the global transmission delay. Let j be the number of clusters in the j-th cell. For the j-th cell The total transmission delay of each cluster, i.e., the last cluster, is defined. After defining the total transmission delay of each cluster, the total transmission delay of the cell, and the global transmission delay, the following objective function is constructed based on the node rate and the global delay: in, This represents the joint optimization objective function. and The weighting coefficients for rate gain and latency penalty.

7. The NOMA-based power grid node clustering cooperative transmission optimization method as described in claim 6, characterized in that: The specific steps of S7 are as follows: Let the particle position vector be represented as: Where X represents the position vector of the particle, B is the bandwidth allocation vector, including the upload bandwidth of all nodes and the forwarding bandwidth of all cluster heads, and p is the power allocation vector, including the transmit power of all nodes and the forwarding power of all cluster heads; The fitness function is expressed as: in, This indicates various constraints and penalties for breach of contract. To correspond to the penalty coefficient, Q represents the total number of categories of all constraints that need to be processed in the optimization problem, and q is a loop index variable that iterates from 1 to Q to represent each type of constraint in turn; The formulas for updating particle velocity and position are as follows: Where t represents the number of iterations in the particle swarm optimization algorithm. Let be the velocity vector of particle i at the t-th iteration. Let be the position vector of particle i at the t-th iteration. For adaptive inertia weights, and These are dynamic individual learning factors and group learning factors, respectively. and A random number uniformly distributed within the interval [0,1]. This represents the optimal position for an individual particle. The globally optimal position; During particle update, the discrete variables are rounded and thresholded, and the clustering results, NOMA pairing relationship and cooperative transmission strategy are guaranteed to meet the constraints through the feasible region repair mechanism. After multiple iterations, the optimal node clustering scheme, optimal user pairing strategy, optimal cooperative transmission selection, optimal bandwidth allocation and optimal power allocation are output.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

Images