Transmission line ad hoc network communication enhancement method, apparatus and device, and storage medium

Abstract

The invention provides a power transmission line ad hoc network communication enhancement method, device and equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: constructing a mixed noise model; constructing a wireless ad hoc network channel model; and based on the mixed noise model and the wireless ad hoc network channel model, performing communication enhancement on the wireless ad hoc network by adopting a distributed wave beam forming method. By means of the mode, in the communication enhancement process, the influence of complex noise in the power transmission line scene on communication and the characteristics of wireless ad hoc network channel transmission are fully considered, it can be ensured that the ad hoc network after communication enhancement can better adapt to the real power transmission line scene, the performance of the ad hoc network in the real power transmission line scene is optimized, and the communication enhancement efficiency is improved. The communication quality of the ad hoc network is improved, and the communication reliability of the ad hoc network is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, device, equipment and storage medium for enhancing communication of a self-organizing network of a power transmission line. Background Art

[0002] In the dense transmission channels in remote areas, there are large areas that are not covered by public networks and power fiber optic networks, making it difficult to support the collection, transmission, storage and application of full data during maintenance. The lack of communication means has become a key bottleneck restricting the stable operation of dense transmission channels.

[0003] As a flexible communication solution, wireless ad hoc networks can quickly transmit business data of electric poles and lines, expand network coverage through multi-hop interconnection, overcome the limitations of single equipment, adapt to different environments and needs, significantly reduce infrastructure costs, and improve communication reliability. At the same time, in complex environments, wireless ad hoc networks show strong adaptability and become an important means to ensure the safe and stable operation of dense transmission channels. However, since the communication paths between ad hoc network devices such as drones, ground terminals and vehicle-mounted terminals need to pass through dense areas of high-voltage transmission lines, they inevitably face strong electromagnetic interference generated by high-voltage transmission lines. In addition, the same-frequency interference between different communication links and the signal attenuation caused by the complex channel environment also have a negative impact on the high-quality transmission of communications.

[0004] Beamforming is a technology that controls the propagation direction and coverage of signals by adjusting the transmit power and phase of each antenna in the antenna array. It can effectively enhance the signal strength in the target direction while suppressing interference in other directions, thereby improving communication quality and system capacity. Distributed beamforming extends this concept to multiple nodes distributed in different locations, which work together to optimize the transmission and reception of signals. Unlike traditional centralized beamforming, distributed beamforming can be flexibly adjusted between different nodes to adapt to environmental changes and user needs, thereby improving the robustness and flexibility of the system. In complex communication environments, especially in the presence of interference and multipath propagation, distributed beamforming can effectively enhance the reliability of signals and reduce the impact of interference, thereby achieving more efficient data transmission.

[0005] In order to solve the problem that wireless ad hoc network communications in dense transmission line scenarios are faced with multiple interferences that affect the communication quality, some solutions have been proposed in the prior art, such as using a beamforming adjustment model to enhance the signal coverage of the wireless ad hoc network, or using beamforming technology to reduce the impact of single-source interference on the communication of each node in the wireless ad hoc network.

[0006] However, existing communication enhancement methods perform poorly in real scenarios, and the communication quality of wireless ad hoc networks is difficult to guarantee. Summary of the invention

[0007] The present invention provides a method, device, equipment and storage medium for enhancing communication of a power transmission line ad hoc network, so as to solve the defects in the prior art that the communication enhancement method performs poorly in real scenarios and the communication quality of the wireless ad hoc network is difficult to guarantee.

[0008] The present invention provides a method for enhancing communication of a power transmission line ad hoc network, comprising: constructing a mixed noise model; constructing a wireless ad hoc network channel model; and based on the mixed noise model and the wireless ad hoc network channel model, adopting a distributed beamforming method to enhance communication of the wireless ad hoc network.

[0009] According to a method for enhancing communication of a transmission line self-organizing network provided by the present invention, a mixed noise model is a mathematical model constructed based on background noise, pulse noise and modulated Gaussian pulse noise, the background noise obeys a zero-mean Gaussian distribution, the pulse noise obeys a Gaussian distribution with a non-zero mean, and the modulated Gaussian pulse noise obeys a Poisson distribution; the expression of the mixed noise model is: ; ; in, is the occurrence probability of background noise; is the occurrence probability of impulse noise; is the occurrence probability of modulated Gaussian pulse noise; is the mean value of the impulse noise; is the mean value of the modulated Gaussian pulse noise; is the variance of background noise; is the variance of the impulse noise; is the noise intensity; The noise intensity is probability.

[0010] According to a method for enhancing communication of a transmission line ad hoc network provided by the present invention, the wireless ad hoc network channel model is a mathematical model; the expression of the wireless ad hoc network channel model is: ; Where r is the actual distance between the sending node and the receiving node in the wireless ad hoc network; is the reference distance between the sending node and the receiving node in the wireless ad hoc network; is the received signal field strength of the receiving node at a distance r from the sending node in the wireless ad hoc network; For receiving nodes in wireless ad hoc networks, the distance between sending nodes The received signal strength at is the path loss slope; Calibrate the reference frequency; is the path loss coefficient; is the measured frequency; is the diffraction loss component; is the model correction factor.

[0011] According to a method for enhancing communication of a transmission line ad hoc network provided by the present invention, the wireless ad hoc network includes multiple nodes; based on a mixed noise model and a wireless ad hoc network channel model, a distributed beamforming method is used to enhance communication of the wireless ad hoc network, including: constructing a wireless ad hoc network beamforming optimization problem based on the mixed noise model and the wireless ad hoc network channel model; solving the wireless ad hoc network beamforming optimization problem based on the communication enhancement model to obtain the transmission power of each node and the phase of each node, and based on the transmission power of each node and the phase of each node, enhancing communication of the wireless ad hoc network; wherein the communication enhancement model is a model constructed based on the MAPPO algorithm.

[0012] According to a method for enhancing communication of a transmission line ad hoc network provided by the present invention, the expression of the beamforming optimization problem of the wireless ad hoc network is: ; ; Among them, max is the maximum value function; is the beamformer of node i in the wireless ad hoc network, and node i is the sending node; is the number of nodes in the wireless ad hoc network; Nodes in wireless ad hoc networks The achievable rate of nodes is the receiving node; is the maximum transmit power of each cluster head node; It is the collection of all nodes in the wireless ad hoc network.

[0013] According to a transmission line self-organizing network communication enhancement method provided by the present invention, based on a communication enhancement model, a wireless self-organizing network beamforming optimization problem is solved to obtain the transmission power of each node and the phase of each node, and based on the transmission power of each node and the phase of each node, the wireless self-organizing network is enhanced for communication, including: obtaining an observation signal of each node in the wireless self-organizing network; inputting the observation signal of each node into the communication enhancement model to obtain the transmission power of each node and the phase of each node output by the communication enhancement model; the communication enhancement model solves the wireless self-organizing network beamforming optimization problem with the goal of maximizing the total channel capacity of the transceiver nodes when there is co-frequency interference between multiple nodes; and adjusting each node in the wireless self-organizing network based on the transmission power of each node and the phase of each node.

[0014] According to a method for enhancing communication in a transmission line ad hoc network provided by the present invention, the communication enhancement model includes a Critic network and multiple Actor networks; wherein the Critic network is deployed at a central node, and each Actor network is deployed at a non-central node, and the central node is a node in the wireless ad hoc network that can communicate and interconnect with all nodes.

[0015] The present invention also provides a transmission line self-organizing network communication enhancement device, comprising: a first construction module for constructing a mixed noise model; a second construction module for constructing a wireless self-organizing network channel model; a communication enhancement module for enhancing the communication of the wireless self-organizing network based on the mixed noise model and the wireless self-organizing network channel model by adopting a distributed beamforming method.

[0016] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the above-mentioned methods for enhancing communication in a self-organizing network of power transmission lines is implemented.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for enhancing communication in a self-organizing network of power transmission lines.

[0018] The method, device, equipment and storage medium for enhancing communication of a transmission line ad hoc network provided by the present invention perform noise modeling and wireless ad hoc network channel modeling for the ad hoc network in the transmission line scenario, construct a mixed noise model and a wireless ad hoc network channel model, and based on the mixed noise model and the wireless ad hoc network channel model, adopt a distributed beamforming method to enhance communication of the wireless ad hoc network. In the process of communication enhancement, the influence of complex noise on communication in the transmission line scenario and the characteristics of wireless ad hoc network channel transmission are fully considered, which can ensure that the ad hoc network after communication enhancement is more adaptable to the real transmission line scenario, optimize the performance of the ad hoc network in the real transmission line scenario, improve the communication quality of the ad hoc network, and enhance the reliability of the ad hoc network communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is one of the flow charts of the method for enhancing communication of a self-organizing network of power transmission lines provided by the present invention.

[0021] Figure 2It is a schematic diagram of the self-organizing network communication architecture in the dense transmission line scenario provided by the present invention.

[0022] Figure 3 This is the second flow chart of the method for enhancing communication of a self-organizing network of power transmission lines provided by the present invention.

[0023] Figure 4 It is a structural schematic diagram of the power transmission line self-organizing network communication enhancement device provided by the present invention.

[0024] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 , Figure 1 This is one of the flow charts of the method for enhancing communication of a transmission line self-organizing network provided by the present invention. In this embodiment, the method for enhancing communication of a transmission line self-organizing network is applied to a self-organizing network in a dense transmission line scenario, and the method for enhancing communication of a transmission line self-organizing network includes steps S110 to S130, and each step is specifically as follows: S110: Constructing a mixed noise model.

[0027] See also Figure 2 , Figure 2 It is a schematic diagram of the self-organizing network communication architecture in the dense transmission line scenario provided by the present invention.

[0028] like Figure 2 As shown, in this embodiment, a wireless ad hoc network can be deployed in a dense power transmission line scenario as a signal connection in areas without public networks to improve communication coverage. During the communication process, the wireless ad hoc network is inevitably subject to electromagnetic interference from the power transmission line and co-frequency interference from other devices (i.e., nodes) in the wireless ad hoc network.

[0029] Among them, co-channel interference refers to the situation where multiple nodes in a wireless ad hoc network share the same spectrum resources. In complex environments or densely populated nodes, signal transmission is subject to interference from other nodes. This interference not only causes signal fading, but may also cause packet loss and delay, seriously affecting the reliability and efficiency of communications.

[0030] Electromagnetic interference refers to the phenomenon that the energy in electromagnetic waves is coupled into the radio receiving equipment or system, resulting in a decrease in the ability of the equipment to receive signals, which will cause negative effects such as deterioration of wireless communication signal quality and increased information errors. There are widespread power frequency electric fields and power frequency magnetic fields around transmission lines. Especially with the promotion of ultra-high voltage alternating current technology, the voltage of charged bodies will increase significantly, which directly leads to further complexity of the electromagnetic environment. There are two main sources of electromagnetic interference in transmission lines, namely active interference and passive interference. Active interference is mainly caused by corona discharge on the surface of the conductor, spark discharge generated by the high potential gradient part of the insulator, and spark discharge caused by poor contact. The high-frequency electromagnetic pulse signal intensity generated by it has reached the level that can interfere with normal radio broadcast communications; passive interference is mainly caused by the reflection and shielding of useful signals by large metal structures such as transmission lines and dry towers.

[0031] See also Figure 3 , Figure 3 This is the second flow chart of the method for enhancing communication of a self-organizing network of power transmission lines provided by the present invention.

[0032] like Figure 3 As shown, in response to the above-mentioned co-frequency interference and electromagnetic interference from transmission lines, this embodiment proposes a communication enhancement method based on distributed beamforming: first, in order to accurately evaluate the propagation characteristics of wireless signals in wireless ad hoc networks in complex interference environments, it is necessary to model the environmental noise and wireless ad hoc network channels in dense transmission line scenarios, such as establishing a mixed noise model to describe the intensity variation of background noise in the time domain, and using a Lee model-based method to construct a wireless ad hoc network channel model to analyze the intensity fading of signals during propagation caused by factors such as path loss, shadow fading and electromagnetic interference; then, combined with the above modeling results, the distributed beamforming technology is applied to dynamically adjust the transmission power and phase of each node in the wireless ad hoc network, and the beamforming problem is solved based on the MAPPO algorithm to optimize the overall network capacity.

[0033] Specifically, noise modeling is first performed on the wireless ad hoc network in a dense transmission line scenario to obtain a mixed noise model.

[0034] S120: Construct a wireless ad hoc network channel model.

[0035] Furthermore, wireless ad hoc network channel modeling is performed for the wireless ad hoc network in the dense transmission line scenario to obtain a wireless ad hoc network channel model.

[0036] S130: Based on the mixed noise model and the wireless ad hoc network channel model, a distributed beamforming method is used to enhance the communication of the wireless ad hoc network.

[0037] The method for enhancing communication of a transmission line ad hoc network provided in this embodiment performs noise modeling and wireless ad hoc network channel modeling for the ad hoc network in the transmission line scenario, constructs a mixed noise model and a wireless ad hoc network channel model, and based on the mixed noise model and the wireless ad hoc network channel model, adopts a distributed beamforming method to enhance communication of the wireless ad hoc network. In the process of communication enhancement, the influence of complex noise on communication in the transmission line scenario and the characteristics of wireless ad hoc network channel transmission are fully considered, which can ensure that the ad hoc network after communication enhancement can better adapt to the real transmission line scenario, optimize the performance of the ad hoc network in the real transmission line scenario, improve the communication quality of the ad hoc network, and enhance the reliability of the ad hoc network communication.

[0038] In some embodiments, the mixed noise model is a mathematical model constructed based on background noise, pulse noise and modulated Gaussian pulse noise, the background noise obeys a zero-mean Gaussian distribution, the pulse noise obeys a Gaussian distribution with a non-zero mean, and the modulated Gaussian pulse noise obeys a Poisson distribution; the expression of the mixed noise model is: ; ; in, is the occurrence probability of background noise; is the occurrence probability of impulse noise; is the occurrence probability of modulated Gaussian pulse noise; is the mean value of the impulse noise; is the mean value of the modulated Gaussian pulse noise; is the variance of background noise; is the variance of the impulse noise; is the noise intensity; The noise intensity is probability.

[0039] It should be noted that the wireless signal transmission environment in the dense transmission line scenario is different from the transmission environment of other traditional communication channels. Due to the complex and changeable topology of the power line network and the various loads connected to the power lines, these loads are irregularly connected to or disconnected from the power grid. This makes the noise in the power line channel not simply Gaussian white noise, but a combination of noises with many different characteristics, and the noise exhibits characteristics such as randomness, periodicity and continuity.

[0040] Based on this, this embodiment classifies large-amplitude and long-duration noise as modulated Gaussian pulse noise, small-amplitude and short-duration noise as pulse noise, and the remaining noise as background white Gaussian noise (i.e., background noise). Then, a mixed noise model constructed based on background noise, pulse noise and modulated Gaussian pulse noise is proposed. The mixed noise model takes into account background noise that obeys a zero-mean Gaussian distribution, pulse noise with a non-zero mean, and modulated Gaussian pulse noise that obeys a Poisson distribution. The three types of noise are constructed into three systems, and the state transfer matrix of the Markov chain is calculated according to their probability of occurrence.

[0041] Among them, the expression of the mixed noise model is: ; ; in, is the occurrence probability of background noise; is the occurrence probability of impulse noise; is the occurrence probability of modulated Gaussian pulse noise; is the mean value of the impulse noise; is the mean value of the modulated Gaussian pulse noise; is the variance of background noise; is the variance of the impulse noise; is the noise intensity; The noise intensity is probability.

[0042] The state transition matrix of the three states is as follows: The matching pursuit algorithm is a greedy algorithm in sparse decomposition. It selects the atom that best matches the original signal from the atom library in each generation process, thus forming a gradual approximation process and finally completing the sparse representation of the signal.

[0043] Optionally, in the noise modeling process, a matching pursuit algorithm is used to statistically analyze the pulse width and pulse amplitude of the modulated Gaussian pulse generated by the sampled noise samples to determine various parameters of the mixed noise model.

[0044] In some embodiments, the wireless ad hoc network channel model is a mathematical model; the expression of the wireless ad hoc network channel model is: ; Where r is the actual distance between the sending node and the receiving node in the wireless ad hoc network; is the reference distance between the sending node and the receiving node in the wireless ad hoc network; is the received signal field strength of the receiving node at a distance r from the sending node in the wireless ad hoc network; For receiving nodes in wireless ad hoc networks, the distance between sending nodes The received signal strength at is the path loss slope; Calibrate the reference frequency; is the path loss coefficient; is the measured frequency; is the diffraction loss component; is the model correction factor.

[0045] To select a beamforming solution, you first need to understand the propagation characteristics of wireless ad hoc network signals, so you need to model the wireless channels in dense power transmission line scenarios. Since signal transmission in wireless ad hoc networks is affected by multiple factors such as scattering, diffraction, and electromagnetic interference caused by terrain and power transmission facilities, traditional wireless communication channel models are difficult to ensure stability and reliability in complex environments. Based on this, this embodiment comprehensively considers the effects of path loss, shadow fading, and electromagnetic loss to construct a wireless ad hoc network channel model based on the Lee model: ; Where r is the actual distance between the sending node and the receiving node in the wireless ad hoc network; is the reference distance between the sending node and the receiving node in the wireless ad hoc network; is the received signal field strength of the receiving node at a distance r from the sending node in the wireless ad hoc network; For receiving nodes in wireless ad hoc networks, the distance between sending nodes The received signal strength at is the path loss slope; Calibrate the reference frequency; is the path loss coefficient; is the measured frequency; is the diffraction loss component; in order to make the model adapt to the complex and changeable actual scenes, the model correction factor is introduced Fine-tune the model to improve its prediction accuracy.

[0046] The wireless signal transmission environment in the dense transmission line scenario has multiple obstacles such as obstructing terrain and transmission towers. Therefore, the radio wave propagation process can be modeled as non-line-of-sight propagation affected by multiple edge peak obstacles. The calculation method is: ; ; ; in, is the propagation clearance between the link and the obstacle; is the distance between the obstacle and the sending node (i.e., the transmitting station); is the distance between the obstacle and the receiving node (i.e., receiving station); is the diffraction factor; n is the total number of obstacles between the sending node and the receiving node; is the kth obstacle between the sending node and the receiving node; is the diffraction loss of the kth obstacle between the sending node and the receiving node; is the total amount of diffraction loss caused by obstacles between the sending node and the receiving node; is the effective total diffraction loss.

[0047] In some embodiments, the wireless ad hoc network includes multiple nodes; based on a mixed noise model and a wireless ad hoc network channel model, a distributed beamforming method is used to enhance communication of the wireless ad hoc network, including: based on the mixed noise model and the wireless ad hoc network channel model, a wireless ad hoc network beamforming optimization problem is constructed; based on the communication enhancement model, the wireless ad hoc network beamforming optimization problem is solved to obtain the transmission power of each node and the phase of each node, and based on the transmission power of each node and the phase of each node, the wireless ad hoc network is enhanced for communication; wherein the communication enhancement model is a model constructed based on the MAPPO algorithm.

[0048] Firstly, based on the mixed noise model and the wireless ad hoc network channel model, the wireless ad hoc network beamforming optimization problem is constructed.

[0049] Specifically, this embodiment divides the communication process of the wireless ad hoc network into multiple time slots, assuming that the channel state and other conditions of the wireless ad hoc network communication parties remain unchanged in each time slot. Assume that in the tth time slot, node i transmits a signal to node k , while node k is interfered by the same frequency of other nodes, the signal received by node k is: ; in, ; i is the sending node, i.e. the expected signal source; j is the non-sending node, i.e. the undesired signal source; is the beamformer of node i, including the transmit power of node i modulated by the beamformer and the transmit phase ; Is After Hermite transformation, we get is the direct downlink channel between node i and node k in the tth time slot; Is After Hermite transformation, we get is the interference channel between node j and node k; is the environmental noise, It can be calculated using a mixed noise model.

[0050] In the wireless ad hoc network communication scenario of this embodiment, not all nodes will have strong co-channel interference. When the co-channel interference link channel conditions are poor, the received signal strength (RSS) is weak when the interference signal is transmitted to the interfered receiving node, and this type of interference can be ignored. By determining two dynamic control thresholds and , respectively limiting the number of interfering nodes and interfered nodes. For a receiving node k and its sending node i, all its interfering nodes and the disturbed node Can be defined as follows: ; ; Then, in the tth time slot, the signal-to-noise ratio of receiving node k and the achievable rate of receiving node k are: ; ; in, is the transmit power of node i after modulation by the beamformer ; is the transmit power of node j after modulation by the beamformer; is the environmental noise power, which can be obtained from the above environmental noise modeling .

[0051] At this time, in the wireless ad hoc network beamforming problem, the solution goal can be set to maximize the total channel capacity of the transmitting and receiving nodes when there is co-frequency interference between multiple nodes. Then, in the tth time slot, the expression of the wireless ad hoc network beamforming optimization problem is: ; ; Among them, max is the maximum value function; is the beamformer of node i in the wireless ad hoc network, and node i is the sending node; is the number of nodes in the wireless ad hoc network; Nodes in wireless ad hoc networks The achievable rate of nodes is the receiving node; is the maximum transmit power of each cluster head node; It is the collection of all nodes in the wireless ad hoc network.

[0052] Furthermore, based on the communication enhancement model, the wireless ad hoc network beamforming optimization problem is solved to obtain the transmission power of each node and the phase of each node, and based on the transmission power of each node and the phase of each node, the wireless ad hoc network is enhanced in communication.

[0053] In some embodiments, the expression of the wireless ad hoc network beamforming optimization problem is: ; ; Among them, max is the maximum value function; is the beamformer of node i in the wireless ad hoc network, and node i is the sending node; is the number of nodes in the wireless ad hoc network; Nodes in wireless ad hoc networks The achievable rate of nodes is the receiving node; is the maximum transmit power of each cluster head node; It is the collection of all nodes in the wireless ad hoc network.

[0054] In some embodiments, based on a communication enhancement model, a wireless ad hoc network beamforming optimization problem is solved to obtain the transmit power of each node and the phase of each node, and based on the transmit power of each node and the phase of each node, communication enhancement is performed on the wireless ad hoc network, including: obtaining an observation signal of each node in the wireless ad hoc network; inputting the observation signal of each node into the communication enhancement model to obtain the transmit power of each node and the phase of each node output by the communication enhancement model; the communication enhancement model solves the wireless ad hoc network beamforming optimization problem with the goal of maximizing the total channel capacity of the transceiver nodes when there is co-frequency interference between multiple nodes; and adjusting each node in the wireless ad hoc network based on the transmit power of each node and the phase of each node.

[0055] In view of the above-mentioned wireless ad hoc network beamforming optimization problem, this embodiment adopts the MAPPO algorithm for solution. The communication enhancement model is constructed based on the MAPPO algorithm. It is a model using the Actor-Critic structure. In the wireless ad hoc network of dense transmission line scenarios, the communication enhancement model includes a Critic network and multiple Actor networks, which can be deployed using centralized training and distributed execution methods: a central node that can be interconnected with all nodes is defined in the wireless ad hoc network, and the Critic network is deployed on the central node to obtain information from all nodes; the remaining nodes are defined as non-central nodes (i.e., ordinary nodes), and each non-central node deploys an Actor network. Each non-central node can be considered as an intelligent agent. After the model is centrally trained, the training results can be fed back to each node. When the observation information of the remaining nodes is limited, the local distributed PPO network can be used for distributed execution to make relevant decisions on the beamforming parameters.

[0056] definition represents the joint action process of the above scenario, i represents any agent, that is, Each agent has a local PPO network, and the corresponding strategy function of the network is .

[0057] Represents the set of all agent reward functions. In this embodiment, the network aims to maximize the objective function while minimizing its own co-frequency interference to other agents. Therefore, the reward function of each agent is Designed for: ; in, Represents the gain term of agent i; is the penalty term of agent i, which is the penalty of agent i itself on other agents The sum of the achievable rates that produce co-channel interference is expressed as: ; in, is the expected signal source of node j; is the undesired signal source of node j.

[0058] Represents the state space, that is, the restricted observation state information of the i-th node, including local information, node information that causes co-frequency interference to it, and node information that is interfered with by it: ; Among them, the local information at time slot t Including the channel between node i and node k at time slot t-1 , and the expected signal received by node k .

[0059] Includes the set of nodes that interfere with node k and the noise information of node k .

[0060] Includes the set of nodes affected by it , node i and node m at time slot t-1 ( ) and the loss caused by interference .

[0061] Represents the set of all agent action spaces. Agent i must allocate power for itself in the continuous action space. , but also output its own phase ,Right now , the PPO algorithm defines a parameterized Gaussian distribution to express the probability of selecting an action. and The corresponding multiples are obtained by respectively completing the corresponding unit Gaussian distribution sampling values: ; ; in, and They are all sampled values ​​obtained by sampling the Gaussian distribution function using the values ​​obtained by the Tanh activation function.

[0062] represents the state transfer function; is set as a discount factor, which is used to make an appropriate trade-off between the predicted reward value and the current reward value. The working goal of each agent i is to optimize its network parameters and determine its optimal strategy function , to achieve the best long-term return for the entire network system: ; In the formula, ,Depend on and Joint decision.

[0063] The action value function corresponding to the i-th agent is: ; The state value function of agent i can be described as: ; Define the agent’s strategy function as ,in ; represents the parameters related to the strategy function of agent i, and in the distributed PPO network of this embodiment, the agent cannot obtain the global state, so is the restricted observation value of agent i, then the policy gradient corresponding to agent i is: ; The optimization goal of agent i is: ; The significance of the Clip function is to limit the change ratio of this network strategy to a limited range; It is defined as a hyperparameter to limit the change ratio of network strategy. Network advantage function The estimation method is Generalized Advantage Estimation (GAE), which is expressed as follows: ; ; in, represents the advantage of performing a specific action at step t, and introduces Balance the weight of each action's advantages.

[0064] At the same time, the objective function of the Critic network The meaning of is the square of the difference between the estimated state value and the current state value: ; in, is the current estimated state value; is the current target state value calculated based on the reward value.

[0065] To sum up, the training process of the model in each time slot is: (1) Transmitting node i selects a beamforming scheme based on its state and current strategy function and sends a signal.

[0066] (2) The receiving node k receives the signal and feeds back the expected signal strength to the sending node i. , noise intensity and channel state information .

[0067] (3) The receiving node k feeds back the interference node to the central node The resulting loss and channel status information .

[0068] (4) The sending node i obtains the interfered node from the central node The degradation caused by the channel state information And calculate the penalty .

[0069] (5) The sending node i updates the local policy function, and the central node updates the evaluation function at the end of each training batch.

[0070] Specifically, after completing the training of the communication enhancement model, the wireless ad hoc network beamforming optimization problem can be solved based on the communication enhancement model: first, the observation signal of each node in the wireless ad hoc network is obtained, and the observation signal of each node is input into the communication enhancement model. The communication enhancement model can solve the wireless ad hoc network beamforming optimization problem, predict and output the solution result that meets the solution target of the wireless ad hoc network beamforming optimization problem, that is, the transmission power of each node and the phase of each node.

[0071] Furthermore, each node in the wireless ad hoc network is adjusted based on the transmission power of each node and the phase of each node.

[0072] The transmission line ad hoc network communication enhancement method provided in this embodiment adopts an optimization scheme based on the MAPPO algorithm, which can realize efficient scheduling and interference management of network resources, thereby ensuring efficient communication of each node of the wireless ad hoc network in a complex environment. This method not only improves the anti-interference ability of the power information system in harsh environments, ensures efficient communication of wireless ad hoc networks in complex environments, but also improves the overall capacity and stability of the network, providing important technical support for the communication security of future smart grids.

[0073] In some embodiments, the communication enhancement model includes a Critic network and multiple Actor networks; wherein the Critic network is deployed at a central node, and each Actor network is deployed at a non-central node, and the central node is a node in the wireless ad hoc network that can communicate and interconnect with all nodes.

[0074] This embodiment proposes a method for enhancing communication of wireless ad hoc networks based on beamforming in a dense transmission line environment. On the basis of noise modeling and wireless ad hoc network channel modeling, the MAPPO algorithm is used to solve the distributed beamforming problem, and the transmission power and phase of each communication node in the wireless ad hoc network are dynamically adjusted to maximize the network capacity. The electromagnetic interference and co-frequency interference problems of wireless ad hoc networks in a dense transmission line environment are effectively solved, the communication quality of wireless ad hoc networks in a dense transmission line environment is improved, and the reliability of communication is enhanced.

[0075] The present invention also provides a transmission line self-organizing network communication enhancement device. Figure 4, Figure 4 4 is a schematic diagram of the structure of the power transmission line self-organizing network communication enhancement device provided by the present invention. In this embodiment, the power transmission line self-organizing network communication enhancement device includes a first construction module 410, a second construction module 420 and a communication enhancement module 430.

[0076] The first building module 410 is used to build a mixed noise model.

[0077] The second building module 420 is used to build a wireless ad hoc network channel model.

[0078] The communication enhancement module 430 is used to enhance the communication of the wireless ad hoc network by using a distributed beamforming method based on a mixed noise model and a wireless ad hoc network channel model.

[0079] In some embodiments, the mixed noise model is a mathematical model constructed based on background noise, pulse noise and modulated Gaussian pulse noise, the background noise obeys a zero-mean Gaussian distribution, the pulse noise obeys a Gaussian distribution with a non-zero mean, and the modulated Gaussian pulse noise obeys a Poisson distribution; the expression of the mixed noise model is: ; ; in, is the occurrence probability of background noise; is the occurrence probability of impulse noise; is the occurrence probability of modulated Gaussian pulse noise; is the mean value of the impulse noise; is the mean value of the modulated Gaussian pulse noise; is the variance of background noise; is the variance of the impulse noise; is the noise intensity; The noise intensity is probability.

[0080] In some embodiments, the wireless ad hoc network channel model is a mathematical model; the expression of the wireless ad hoc network channel model is: ; Where r is the actual distance between the sending node and the receiving node in the wireless ad hoc network; is the reference distance between the sending node and the receiving node in the wireless ad hoc network; is the received signal field strength of the receiving node at a distance r from the sending node in the wireless ad hoc network; For receiving nodes in wireless ad hoc networks, the distance between sending nodes The received signal strength at is the path loss slope; Calibrate the reference frequency; is the path loss coefficient; is the measured frequency; is the diffraction loss component; is the model correction factor.

[0081] In some embodiments, the wireless ad hoc network includes a plurality of nodes.

[0082] The communication enhancement module 430 is used to construct a wireless ad hoc network beamforming optimization problem based on a mixed noise model and a wireless ad hoc network channel model; based on the communication enhancement model, the wireless ad hoc network beamforming optimization problem is solved to obtain the transmission power of each node and the phase of each node, and based on the transmission power of each node and the phase of each node, the wireless ad hoc network is enhanced in communication; wherein the communication enhancement model is a model constructed based on the MAPPO algorithm.

[0083] In some embodiments, the expression of the wireless ad hoc network beamforming optimization problem is: ; ; Among them, max is the maximum value function; is the beamformer of node i in the wireless ad hoc network, and node i is the sending node; is the number of nodes in the wireless ad hoc network; Nodes in wireless ad hoc networks The achievable rate of nodes is the receiving node; is the maximum transmit power of each cluster head node; It is the collection of all nodes in the wireless ad hoc network.

[0084] In some embodiments, the communication enhancement module 430 is used to obtain the observation signal of each node in the wireless ad hoc network; the observation signal of each node is input into the communication enhancement model to obtain the transmission power of each node and the phase of each node output by the communication enhancement model; the communication enhancement model solves the wireless ad hoc network beamforming optimization problem with the goal of maximizing the total channel capacity of the transceiver nodes when there is co-frequency interference between multiple nodes; and each node in the wireless ad hoc network is adjusted based on the transmission power of each node and the phase of each node.

[0085] In some embodiments, the communication enhancement model includes a Critic network and multiple Actor networks; wherein the Critic network is deployed at a central node, and each Actor network is deployed at a non-central node, and the central node is a node in the wireless ad hoc network that can communicate and interconnect with all nodes.

[0086] The invention also provides an electronic device. Figure 5 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the transmission line self-organizing network communication enhancement method.

[0087] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0088] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the power transmission line self-organizing network communication enhancement method provided by the above-mentioned methods.

[0089] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for enhancing communication of a power transmission line ad hoc network, characterized in that: include: Construct mixed noise model; Construct wireless ad hoc network channel model; Based on the mixed noise model and the wireless ad hoc network channel model, a distributed beamforming method is adopted to enhance the communication of the wireless ad hoc network.

2. The method for enhancing communication of a power transmission line ad hoc network according to claim 1, characterized in that: The mixed noise model is a mathematical model constructed based on background noise, pulse noise and modulated Gaussian pulse noise, the background noise obeys a zero-mean Gaussian distribution, the pulse noise obeys a Gaussian distribution with a non-zero mean, and the modulated Gaussian pulse noise obeys a Poisson distribution; The expression of the mixed noise model is: ； ； in, is the occurrence probability of the background noise; is the occurrence probability of the impulse noise; is the occurrence probability of the modulated Gaussian pulse noise; is the mean value of the impulse noise; is the mean value of the modulated Gaussian pulse noise; is the variance of the background noise; is the variance of the impulse noise; is the noise intensity; The noise intensity is probability.

3. The method for enhancing communication of a power transmission line ad hoc network according to claim 1, characterized in that: The wireless ad hoc network channel model is a mathematical model; The expression of the wireless ad hoc network channel model is: ； Wherein, r is the actual distance between the sending node and the receiving node in the wireless ad hoc network; is a reference distance between a sending node and a receiving node in the wireless ad hoc network; is the received signal field strength of the receiving node at a distance r from the sending node in the wireless ad hoc network; The receiving node in the wireless ad hoc network is at a distance from the sending node The received signal strength at is the path loss slope; Calibrate the reference frequency; is the path loss coefficient; is the measured frequency; is the diffraction loss component; is the model correction factor.

4. The method for enhancing communication of a power transmission line ad hoc network according to claim 1, characterized in that: The wireless ad hoc network includes a plurality of nodes; The method of enhancing communication of the wireless ad hoc network by using a distributed beamforming method based on the mixed noise model and the wireless ad hoc network channel model includes: Based on the mixed noise model and the wireless ad hoc network channel model, construct a wireless ad hoc network beamforming optimization problem; Based on the communication enhancement model, the wireless ad hoc network beamforming optimization problem is solved to obtain the transmission power of each node and the phase of each node, and based on the transmission power of each node and the phase of each node, the wireless ad hoc network is enhanced in communication; Wherein, the communication enhancement model is a model constructed based on the MAPPO algorithm.

5. The method for enhancing communication of a power transmission line ad hoc network according to claim 4, characterized in that: The expression of the wireless ad hoc network beamforming optimization problem is: ； ； Among them, max is the maximum value function; is a beamformer of node i in the wireless ad hoc network, where node i is a sending node; is the number of nodes in the wireless ad hoc network; A node in the wireless ad hoc network The achievable rate of nodes is the receiving node; is the maximum transmit power of each cluster head node; It is the set of all the nodes in the wireless ad hoc network.

6. The method for enhancing communication of a power transmission line ad hoc network according to claim 4, characterized in that: The method of solving the wireless ad hoc network beamforming optimization problem based on the communication enhancement model, obtaining the transmit power of each node and the phase of each node, and performing communication enhancement on the wireless ad hoc network based on the transmit power of each node and the phase of each node, includes: Acquiring an observation signal of each of the nodes in the wireless ad hoc network; The observation signal of each node is input into the communication enhancement model to obtain the transmission power of each node and the phase of each node output by the communication enhancement model; the communication enhancement model solves the beamforming optimization problem of the wireless ad hoc network with the goal of maximizing the total channel capacity of the transceiver node when there is co-frequency interference between multiple nodes; Each of the nodes in the wireless ad hoc network is adjusted based on the transmission power of each of the nodes and the phase of each of the nodes.

7. The method for enhancing communication of a power transmission line ad hoc network according to claim 4, characterized in that: The communication enhancement model includes a Critic network and multiple Actor networks; The Critic network is deployed at a central node, and each of the Actor networks is deployed at a non-central node. The central node is a node in the wireless ad hoc network that can communicate and interconnect with all the nodes.

8. A transmission line self-organizing network communication enhancement device, characterized in that: include: A first building module is used to build a mixed noise model; The second building module is used to build a wireless ad hoc network channel model; The communication enhancement module is used to enhance the communication of the wireless ad hoc network by adopting a distributed beamforming method based on the mixed noise model and the wireless ad hoc network channel model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for enhancing communication in a self-organizing network of power transmission lines as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for enhancing communication in a self-organizing network of power transmission lines as claimed in any one of claims 1 to 7 is implemented.

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