Power optimization method for power communication system based on hybrid duplex relay

Abstract

The application relates to the technical field of power communication, and discloses a power optimization method for a power communication system based on a hybrid duplex relay, which has the technical scheme as follows: a three-node system model of a source node and a relay node half duplex and a destination node full duplex is constructed, and an OFDM multi-carrier framework is combined to adapt to the characteristics of a power line channel; an optimization model is established by taking the minimization of total power consumption as a target and the link capacity as a constraint, an alternating optimization algorithm is adopted to decompose a non-convex problem, and the power distribution of each node is accurately solved through a Karush-Kuhn-Tucker condition, so that the total power consumption and the implementation complexity are reduced, the spectrum and power utilization efficiency are improved, and the QoS requirements of automatic meter reading and distributed energy monitoring and other services are met.

Description

Technical Field

[0001] This invention relates to the field of power communication technology, and more specifically, to a power optimization method for power communication systems based on hybrid duplex relay. Background Technology

[0002] Power line communication is a core transmission technology for smart grids. While it can significantly reduce the deployment cost of communication networks, it suffers from channel defects such as high attenuation, multipath interference, and strong noise, resulting in a high bit error rate. Furthermore, the time-division multiplexing of traditional half-duplex systems causes reverse link delays exceeding seconds, making it difficult to meet the real-time bidirectional communication requirements of services such as automatic meter reading and distributed energy monitoring. Even after introducing relay nodes, existing solutions still have shortcomings: direct communication between the source and destination nodes under poor channel conditions requires a significant increase in transmission power; half-duplex relays use four time slots to complete bidirectional transmission, reducing spectral efficiency by about half and failing to consider link coupling characteristics; while frequency-division duplexing can improve spectral efficiency, it is limited by the scarcity of low-frequency bandwidth in the power line band, and duplex filters have insufficient isolation and are difficult to suppress self-interference under wideband noise.

[0003] Meanwhile, traditional power allocation does not consider the cooperative mechanism of hybrid duplex nodes. The nonlinear coupling between QoS constraints and power variables makes the optimization problem non-convex and difficult to solve. Existing relay power optimization is mostly aimed at wireless channels and ignores the spatial correlation of power line channels. Direct application will fail. There is an urgent need for a power optimization method that is adapted to power line channels and takes into account both QoS and low power consumption.

[0004] Therefore, the present invention provides a power optimization method for power communication systems based on hybrid duplex relay, which improves the above-mentioned technical problems. Summary of the Invention

[0005] This disclosure aims to address the shortcomings of existing technologies by providing a power optimization method for power communication systems based on hybrid duplex relays. The invention constructs a three-node system model with half-duplex source and relay nodes and full-duplex destination nodes, adapting the OFDM multi-carrier framework to the characteristics of power line channels. An optimization model is then established with the goal of minimizing total power consumption and link capacity as a constraint. An alternating optimization algorithm is used to decompose the non-convex problem, and the power allocation of each node is accurately solved using the Karush-Kuhn-Tucker condition. Ultimately, while meeting the QoS requirements of services such as automatic meter reading and distributed energy monitoring, the overall system power consumption and implementation complexity are reduced, and spectrum and power utilization efficiency are improved.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: a power optimization method for power communication systems based on hybrid duplex relay, comprising the following steps:

[0007] S1: Construct a three-node bidirectional communication system model containing a source node, a relay node, and a destination node, wherein the source node and the relay node adopt half-duplex mode, and the destination node adopts full-duplex mode;

[0008] S2: Establish a power optimization mathematical model that includes quality of service constraints. The model aims to minimize the total power consumption of the system and uses forward link capacity and reverse link capacity as constraints.

[0009] S3: The non-convex optimization problem is decomposed into three sub-problems by using an alternating optimization algorithm, and the relay node power allocation, source node power allocation and destination node time-division power allocation are solved sequentially.

[0010] S4: Convex optimization solutions to each subproblem are achieved through the Karush-Kuhn-Tucker conditions, and the power parameters are iteratively updated until convergence.

[0011] As a preferred embodiment of the present invention, step S1 specifically includes:

[0012] S11: Construct an orthogonal frequency division multiplexing (OFDM) communication framework, uniformly divide the system bandwidth into multiple subcarriers, and ensure that each subcarrier channel exhibits flat fading characteristics;

[0013] S12: Define node roles and duplex modes. The source node and relay node adopt half-duplex mode and achieve transmit and receive isolation through time division multiplexing. The destination node adopts full-duplex mode and is equipped with an ideal circulator to achieve simultaneous transmit and receive on the same frequency band. The self-interference suppression capability meets the communication requirements.

[0014] S13: Design a two-stage hybrid duplex transmission protocol. In the first stage, the source node broadcasts signals to the relay node and the destination node, while the destination node sends a reverse link signal to the relay node. In the second stage, the relay node amplifies and forwards the received signal and broadcasts a gain signal to the source node and the destination node, while the destination node directly sends a reverse link signal to the source node.

[0015] S14: Establish a dynamic power allocation dimension, whereby the source node allocates the total transmit power; the destination node allocates power independently in stages: the first stage transmit power is used for reverse link transmission, and the second stage transmit power is used for direct link transmission; the relay node allocates the forwarding signal power.

[0016] S15: Define the characteristics of the power line channel. The channel frequency response includes the forward and reverse links. The inter-node channels are coupled through the same power line cable mesh and have spatial correlation.

[0017] As a preferred embodiment of the present invention, step S2 specifically includes:

[0018] S21: Define a minimization objective function with the goal of minimizing the total power consumption of the system. The total power consumption includes the sum of the transmit power of all subcarriers of the source node, the sum of the transmit power of the destination node in two stages, and the sum of the forwarding power of the relay node.

[0019] S22: Set service quality constraints. Forward link capacity constraint: The average subcarrier capacity from the source node to the destination node must meet the preset lower limit threshold. Reverse link capacity constraint: The average subcarrier capacity from the destination node to the source node must meet the preset lower limit threshold.

[0020] S23: Establish a link capacity calculation model. The forward link capacity is calculated based on the superposition of the signal-to-noise ratios received in the two stages at the destination node. The reverse link capacity is calculated based on the equivalent signal-to-noise ratio when the source node decodes the reverse signal.

[0021] S24: Declare the domain of the optimization variables and identify the non-convexity of the problem. The power allocation parameters of all nodes are non-negative and are limited by the maximum transmission power of the power line channel. The optimization problem is non-convex due to the non-linear coupling relationship between the quality of service constraints and the power variables.

[0022] As a preferred embodiment of the present invention, step S3 specifically includes:

[0023] S31: Decompose the non-convex optimization problem into three sequentially executed subproblems according to the node roles: relay node power allocation subproblem, source node power allocation subproblem, and destination node time-division power allocation subproblem; use the solution result of the previous subproblem as the input parameter of the subsequent subproblem through an iterative loop mechanism.

[0024] S32: Relay node power optimization: Fix the transmit power parameters of the source node on all subcarriers and the transmit power parameters of the destination node in both stages, and optimize only the signal forwarding power allocation of the relay node on each subcarrier to minimize the total system power consumption and meet the quality of service constraints; Source node power optimization: Based on the updated relay node power parameters and the fixed destination node power parameters, optimize the transmit power allocation of the source node on all subcarriers to maintain the goal of minimizing total power consumption and the quality of service constraints; Destination node time-division power optimization: Based on the updated source node and relay node power parameters, optimize the time slot ratio allocation of the reverse link transmit power of the destination node in the first transmission stage and the direct transmission link transmit power in the second transmission stage to ensure the reverse link capacity constraint in full-duplex mode;

[0025] S33: After solving the above three sub-problems, update the total system power consumption and determine whether the convergence condition is met: if the change in total power consumption between two adjacent iterations is lower than the preset threshold, or the maximum number of iterations is reached, then terminate the optimization process; otherwise, use the current power parameter as the initial value for the next iteration and re-execute the sub-problem solving sequence.

[0026] As a preferred embodiment of the present invention, step S4 specifically includes:

[0027] S41: Construct a convex optimization solution framework, construct an augmented Lagrangian function for each subproblem, and introduce non-negative multipliers to integrate the service quality constraints into the objective function;

[0028] S42: The relay power subproblem is solved iteratively by the binary search algorithm to find the optimal power; the source node power subproblem is solved by gradient analysis to obtain a closed analytical solution; and the destination node time-sharing power subproblem is solved by transforming the quasi-convex problem into a convex optimization problem using the slack variable method.

[0029] S43: Check whether the obtained power solution satisfies the physical constraints of the power line communication system;

[0030] S44: Pass the optimized solution of the current subproblem as the input parameter to the next subproblem to form a closed-loop iterative process.

[0031] As a preferred technical solution of the present invention, in step S15, the spatial correlation formed by the inter-node channels through the coupling of the same power cable grid is reflected in the fact that the channel gains between each node satisfy a preset correlation relationship.

[0032] As a preferred technical solution of the present invention, in step S21, the calculation range of the total power consumption of the system covers all transmission and forwarding power consumption of the source node, destination node and relay node in the communication process, without omitting any key power expenditure item of any node.

[0033] As a preferred technical solution of the present invention, in step S33, the range of the preset threshold is set according to the power consumption stability requirements of smart grid communication services, and the maximum number of iterations is set according to the balance between system computing resources and optimization efficiency.

[0034] As a preferred technical solution of the present invention, the physical constraints in step S43 include, but are not limited to, the transmission power of each node not exceeding the hardware power limit of the power line communication equipment, and the subcarrier power allocation meeting the channel transmission characteristics requirements.

[0035] In summary, the present invention has the following beneficial effects:

[0036] Firstly, by constructing a three-node hybrid duplex model with half-duplex source and relay nodes and full-duplex destination nodes, and combining a two-stage transmission protocol and an alternating optimization algorithm, the total power consumption of the system is significantly reduced while meeting the capacity and QoS constraints of the forward and reverse links. This solves the problems of traditional bidirectional direct transmission systems requiring a significant increase in transmission power and low spectrum efficiency of half-duplex relays under adverse channel conditions.

[0037] Secondly, based on the OFDM multi-carrier framework, subcarrier flat fading is achieved, and a link capacity calculation model is designed in combination with the spatial correlation of the power line channel. This avoids the problem that the existing relay power optimization schemes that focus on the wireless channel fail due to ignoring the characteristics of the power line channel, and provides high-reliability communication guarantee for services such as automatic meter reading and distributed energy monitoring.

[0038] Third, by accurately solving the power allocation of each node using the Karush-Kuhn-Tucker condition, it not only solves the problem of solving non-convex problems caused by the nonlinear coupling of QoS constraints and power variables in traditional power optimization, but also improves spectrum utilization efficiency through the full-duplex mode of the destination node and the dynamic power allocation of the relay. It overcomes the shortcomings of frequency division duplex in the low-frequency band of power lines, such as scarce bandwidth and difficulty in suppressing self-interference, and fully adapts to the power communication needs of smart grids. Attached Figure Description

[0039] Figure 1 A flowchart illustrating a power optimization method for a power communication system based on hybrid duplex relay, provided in an embodiment of the present invention;

[0040] Figure 2 This is a three-node bidirectional relay communication system model provided in an embodiment of the present invention. Detailed Implementation

[0041] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0044] Furthermore, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0045] This disclosure aims to address the problems in smart grid power communication systems, where traditional solutions suffer from high power consumption and insufficient reliability of real-time bidirectional communication due to channel defects, and the power optimization problem is non-convex, difficult to solve, and has poor adaptability. Therefore, this disclosure proposes a power optimization method for power communication systems based on hybrid duplex relays to achieve low-power, high-reliability operation of bidirectional communication services in smart grids. This method constructs a three-node system model with half-duplex source and relay nodes and full-duplex destination nodes, adapting to power line channel characteristics using an OFDM multi-carrier framework. An optimization model is then established with minimizing total power consumption as the objective and link capacity as the constraint. An alternating optimization algorithm is used to decompose the non-convex problem, and the power allocation of each node is accurately solved using the Karush-Kuhn-Tucker condition. Ultimately, while meeting the QoS requirements of services such as automatic meter reading and distributed energy monitoring, the method reduces total system power consumption and implementation complexity, and improves spectrum and power utilization efficiency.

[0046] Please refer to Figure 1 , Figure 1 A flowchart of a power optimization method for a power communication system based on hybrid duplex relay, according to an embodiment of this disclosure, is shown. The overall process mainly includes the following four steps:

[0047] S1: Construct a three-node bidirectional communication system model containing a source node, a relay node, and a destination node, wherein the source node and the relay node adopt half-duplex mode, and the destination node adopts full-duplex mode.

[0048] S11: Construct a communication system containing three nodes, such as Figure 2 As shown, the solid line represents the first stage, and the dashed line represents the second stage. Source node Relay node is responsible for sending forward link signals to the destination node, using half-duplex mode; Employing an amplified relay mode, it receives mixed signals and broadcasts them, using half-duplex mode; destination node It receives forward link signals and transmits reverse link signals, equipped with an ideal circulator to achieve full-duplex mode. Inter-node channels are coupled through the same power cable mesh, and channel gain exhibits spatial correlation.

[0049] S12: Adjust the total system bandwidth Evenly divided into Each subcarrier satisfies the subcarrier channel flat fading condition: .

[0050] Among them, the The frequency response of each subcarrier channel is expressed as:

[0051] , This is the transmission phase; among which, This is the normalized path gain. and It is a transmission node. .

[0052] S13: Develop a two-phase hybrid duplex transmission protocol. Broadcast signal to and Power is ; Simultaneously send reverse link signal to Power is relay node The received signal is:

[0053] ,

[0054] in, This is additive noise from power lines.

[0055] The received signal is amplified and broadcast, with a gain of . Relay power :

[0056] ;

[0057] in, It is noise power.

[0058] Simultaneous direct signal transmission to Transmission power ; The reverse link signal is decoded using self-interference cancellation technology.

[0059] S14: Establish dynamic power allocation dimensions: Source node:

[0060] ,

[0061] Target node:

[0062] ,

[0063] Relay node:

[0064] .

[0065] It is the source node The maximum permissible transmission power is limited by electromagnetic compatibility specifications and equipment safety requirements; The destination node The maximum allowable transmit power is the maximum sum of the power of the two stages when the target node transmits signals in full-duplex mode. It is a relay node The maximum allowed forwarding power is the maximum total power of the forwarded signal when the relay node amplifies and forwards the received signal.

[0066] S15: Defines the power line channel characteristics; the channel frequency response includes both forward and reverse links; all nodes are coupled through the same power line mesh. Phase From the node arrive Normalized path gain satisfy:

[0067] .

[0068] S2: Establish a power optimization mathematical model that includes quality of service constraints. The model aims to minimize the total power consumption of the system and uses forward link capacity and reverse link capacity as constraints.

[0069] S21: Define the objective function to minimize the total system power consumption. The total power of the source node, destination node, and relay node includes:

[0070] .

[0071] S22: Set QoS constraints, including average subcarrier capacity in the forward link capacity constraint. Meets the lower limit of smart grid business requirements:

[0072] .

[0073] Average subcarrier capacity in reverse link capacity constraints Satisfy the lower limit:

[0074] .

[0075] in, , and They are The signal-to-noise ratio at the location and The signal-to-noise ratio at the two phases, and These are the QoS thresholds for the forward and reverse links, respectively.

[0076] S23: Establish a link capacity calculation model, calculate the forward link signal-to-noise ratio, and the destination node. Combining the received signals from both stages:

[0077] .

[0078] Calculate the signal-to-noise ratio of the reverse link, source node Decoding after eliminating self-interference:

[0079] .

[0080] in, yes Send to Normalized signal-to-noise ratio, yes direct pass Normalized signal-to-noise ratio, yes Forward signal to Normalized signal-to-noise ratio, yes Forward signal to Normalized signal-to-noise ratio, yes Send to Normalized signal-to-noise ratio, yes direct pass The normalized signal-to-noise ratio is calculated as follows:

[0081] .

[0082] S24: Declare the domain and physical constraints of the optimization variables, as described in step 1, step 4. The optimization problem exhibits non-convexity due to the nonlinear coupling between QoS constraints and power variables. The Alternating Optimization (AO) algorithm is used to decompose the original problem into three sub-problems: fixed source / destination node power → optimized relay power, fixed relay / destination node power → optimized source node power, and fixed source / relay node power → optimized time-division power of the destination node.

[0083] S3: The non-convex optimization problem is decomposed into three sub-problems using an alternating optimization algorithm, and the relay node power allocation, source node power allocation and destination node time-division power allocation are solved sequentially.

[0084] S31: Initialize power parameters and convergence threshold; initialize source node power. Two-stage power at the target node , and relay node power This is typically set to uniform distribution. A convergence threshold is set. and maximum number of iterations .

[0085] S32: Solve the relay power subproblem by fixing the power of the source and destination nodes:

[0086] ,

[0087] in, It is the target average capacity of the forward link. It is the average capacity of the reverse link target. yes Link normalized total gain, It is the reverse link gain difference term. It is the reverse link diversity gain combination term. yes Link normalized gain, yes Normalized total gain during the broadcast phase, yes Normalized total gain during the broadcast phase, yes Link gain contribution, yes The link normalized gain and its parameters are calculated as follows:

[0088] ; ;

[0089] ; ;

[0090] ; ;

[0091] ; ;

[0092] Constructing the Lagrange function Solve using the following system of equations:

[0093] ;

[0094] S33: With fixed relay and destination node power, solve the source node power subproblem:

[0095] ;

[0096] Similarly,

[0097] ;

[0098] Derive the optimal solution based on the KKT conditions: ,in The total power constraint is satisfied by using the bisection method.

[0099] S34: With fixed source node and relay power, solve the time-division power subproblem at the destination node:

[0100] ;

[0101] Similarly,

[0102] ;

[0103] Introducing auxiliary variables Transform quasi-convex constraints into convex constraints:

[0104] ,

[0105] Solve using KKT conditional iteration.

[0106] S4: Convex optimization solutions to each subproblem are achieved through the Karush-Kuhn-Tucker conditions, and the power parameters are iteratively updated until convergence.

[0107] S41: Perform parameter updates, using the solution to the current subproblem as the input to the next subproblem: relay power solution → source node optimization input → destination node optimization input.

[0108] S42: Perform convergence determination and calculate the change in total system power consumption. ,like or number of iterations Output the final power scheme; otherwise, return to step two and continue iterating.

[0109] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A power optimization method for power communication systems based on hybrid duplex relays, characterized in that, The method includes the following steps: S1: Construct a three-node bidirectional communication system model containing a source node, a relay node, and a destination node, wherein the source node and the relay node adopt half-duplex mode, and the destination node adopts full-duplex mode; S2: Establish a power optimization mathematical model that includes quality of service constraints. The model aims to minimize the total power consumption of the system and uses forward link capacity and reverse link capacity as constraints. S3: The non-convex optimization problem is decomposed into three sub-problems by using an alternating optimization algorithm, and the relay node power allocation, source node power allocation and destination node time-division power allocation are solved sequentially. S4: Convex optimization solutions to each subproblem are achieved through the Karush-Kuhn-Tucker conditions, and the power parameters are iteratively updated until convergence. S2 specifically includes: S21: Define a minimization objective function with the goal of minimizing the total power consumption of the system. The total power consumption includes the sum of the transmit power of all subcarriers of the source node, the sum of the transmit power of the destination node in two stages, and the sum of the forwarding power of the relay node. S22: Set service quality constraints. Forward link capacity constraint: The average subcarrier capacity from the source node to the destination node must meet the preset lower limit threshold. Reverse link capacity constraint: The average subcarrier capacity from the destination node to the source node must meet the preset lower limit threshold. S23: Establish a link capacity calculation model. The forward link capacity is calculated based on the superposition of the signal-to-noise ratios received in the two stages at the destination node. The reverse link capacity is calculated based on the equivalent signal-to-noise ratio when the source node decodes the reverse signal. S24: Declare the domain of the optimization variables and identify the non-convexity of the problem. The power allocation parameters of all nodes are non-negative and are limited by the maximum transmission power of the power line channel. The optimization problem is non-convex due to the non-linear coupling relationship between the quality of service constraints and the power variables.

2. The power optimization method for power communication systems based on hybrid duplex relay according to claim 1, characterized in that, S1 specifically includes: S11: Construct an orthogonal frequency division multiplexing (OFDM) communication framework, uniformly divide the system bandwidth into multiple subcarriers, and ensure that each subcarrier channel exhibits flat fading characteristics; S12: Define node roles and duplex modes. The source node and relay node adopt half-duplex mode and achieve transmit and receive isolation through time division multiplexing. The destination node adopts full-duplex mode and is equipped with an ideal circulator to achieve simultaneous transmit and receive on the same frequency band. The self-interference suppression capability meets the communication requirements. S13: Design a two-stage hybrid duplex transmission protocol. In the first stage, the source node broadcasts signals to the relay node and the destination node, while the destination node sends a reverse link signal to the relay node. In the second stage, the relay node amplifies and forwards the received signal and broadcasts a gain signal to the source node and the destination node, while the destination node directly sends a reverse link signal to the source node. S14: Establish a dynamic power allocation dimension, whereby the source node allocates the total transmit power; the destination node allocates power independently in stages: the first stage transmit power is used for reverse link transmission, and the second stage transmit power is used for direct link transmission; the relay node allocates the forwarding signal power. S15: Define the characteristics of the power line channel. The channel frequency response includes the forward and reverse links. The inter-node channels are coupled through the same power line cable mesh and have spatial correlation.

3. The power optimization method for power communication systems based on hybrid duplex relay according to claim 1, characterized in that, S3 specifically includes: S31: Decompose the non-convex optimization problem into three sequentially executed subproblems according to the node roles: relay node power allocation subproblem, source node power allocation subproblem, and destination node time-division power allocation subproblem; use the solution result of the previous subproblem as the input parameter of the subsequent subproblem through an iterative loop mechanism. S32: Relay node power optimization: Fix the transmit power parameters of the source node on all subcarriers and the transmit power parameters of the destination node in both stages, and optimize only the signal forwarding power allocation of the relay node on each subcarrier to minimize the total system power consumption and meet the quality of service constraints; Source node power optimization: Based on the updated relay node power parameters and the fixed destination node power parameters, optimize the transmit power allocation of the source node on all subcarriers to maintain the goal of minimizing total power consumption and the quality of service constraints; Destination node time-division power optimization: Based on the updated source node and relay node power parameters, optimize the time slot ratio allocation of the reverse link transmit power of the destination node in the first transmission stage and the direct transmission link transmit power in the second transmission stage to ensure the reverse link capacity constraint in full-duplex mode; S33: After solving the above three sub-problems, update the total power consumption of the system and determine whether the convergence condition is met: if the total power consumption change between two adjacent iterations is lower than the preset threshold, or the maximum number of iterations is reached, then terminate the optimization process; otherwise, use the current power parameter as the initial value for the next iteration and re-execute the sub-problem solving sequence.

4. The power optimization method for power communication systems based on hybrid duplex relay according to claim 1, characterized in that, S4 specifically includes: S41: Construct a convex optimization solution framework, construct an augmented Lagrangian function for each subproblem, and introduce non-negative multipliers to integrate the service quality constraints into the objective function; S42: The relay power subproblem is solved iteratively by the binary search algorithm to find the optimal power; the source node power subproblem is solved by gradient analysis to obtain a closed analytical solution; and the destination node time-sharing power subproblem is solved by transforming the quasi-convex problem into a convex optimization problem using the slack variable method. S43: Check whether the obtained power solution satisfies the physical constraints of the power line communication system; S44: Pass the optimized solution of the current subproblem as the input parameter to the next subproblem to form a closed-loop iterative process.

5. The power optimization method for power communication systems based on hybrid duplex relay according to claim 2, characterized in that, In S15, the spatial correlation formed by the inter-node channels coupled through the same power cable grid is reflected in the fact that the channel gains between each node satisfy a preset correlation relationship.

6. The power optimization method for power communication systems based on hybrid duplex relay according to claim 1, characterized in that, In S21, the calculation of the total power consumption of the system covers all transmission and forwarding power consumption of the source node, destination node, and relay node during the communication process, without omitting any key power expenditure item of any node.

7. The power optimization method for power communication systems based on hybrid duplex relay according to claim 3, characterized in that, In S33, the range of the preset threshold is set according to the power consumption stability requirements of smart grid communication services, and the maximum number of iterations is set according to the balance between system computing resources and optimization efficiency.

8. The power optimization method for power communication systems based on hybrid duplex relay according to claim 4, characterized in that, The physical constraints in S43 include, but are not limited to, the transmit power of each node not exceeding the hardware power limit of the power line communication equipment, and the subcarrier power allocation conforming to the channel transmission characteristics requirements.

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