A wireless energy-carrying-based unmanned ship-assisted marine communication system resource scheduling method and system

Abstract

The application discloses a kind of based on wireless energy-carrying unmanned ship auxiliary ocean communication system resource scheduling method and system, solve the technical problem that the existing unmanned ship auxiliary ocean communication research is difficult to effectively reduce system end-to-end total transmission delay, cause the overall transmission efficiency of open sea communication network to be low.Solution includes obtaining ocean communication scene basic configuration parameter, constructs ocean layered communication system physical model;Based on two-hop channel matrix and configuration parameter, the transmission rate and energy calculation relationship is deduced, and the rate energy calculation model set is formed;Combining unmanned ship sailing constraint constructs end-to-end delay minimization original optimization problem, and is decomposed to obtain initial optimization variable set and multiple sub-problems;Each sub-problem is alternately solved to obtain this round full-amount optimization variable set, and the optimal resource scheduling scheme is output after convergence determination.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a resource scheduling method and system for an unmanned surface vessel-assisted marine communication system based on wireless power. Background Technology

[0002] With the rapid development of applications such as marine Internet of Things (IoT), marine environmental monitoring, maritime transport management, and marine resource development, marine wireless communication networks have become a crucial infrastructure supporting the construction of smart oceans. However, compared to terrestrial communication networks, the marine environment has a wide coverage area, high infrastructure deployment costs, and is difficult to maintain, making it challenging to achieve continuous and stable communication coverage through the construction of numerous fixed base stations. Therefore, how to build an efficient, reliable, and low-cost marine communication system has become an important research direction.

[0003] In recent years, unmanned surface vehicles (USVs) have been widely used in marine communication scenarios due to their advantages such as maneuverability, strong autonomous navigation capabilities, and low deployment costs. By dynamically adjusting their position and communication resources, USVs can effectively extend the coverage of marine communication networks and improve data transmission capabilities in remote waters. Meanwhile, ocean buoys, as low-cost and easily deployed communication nodes, are also widely used for marine information collection and data forwarding. However, buoys typically rely on limited battery power, and their communication capabilities and continuous operating time are constrained by energy limitations.

[0004] To address the energy constraints of marine communication nodes, Simultaneous Wireless Information and Power Transfer (SWIPT) technology has garnered significant attention. This technology utilizes radio frequency signals to simultaneously transmit information and power, allowing receiving nodes to collect wireless energy while receiving information. This extends equipment lifespan and reduces manual maintenance costs. Introducing SWIPT technology into marine communication networks enables unmanned surface vessels (USVs) to not only transmit data to buoys but also provide them with wireless power replenishment, thereby enhancing the continuous operational capability of marine networks.

[0005] Existing research on unmanned surface vessels (USVs) assisted marine communication largely focuses on improving communication performance, adjusting parameters and designing schemes around trajectory design, resource allocation, and beamforming optimization to improve the transmission quality of marine communication links. These studies generally assume that marine buoys have a sufficient and stable energy supply during modeling and analysis, without considering the constraints of node energy limitations on the communication cooperation process. However, in actual offshore communication scenarios, buoys mostly rely on their own batteries for power, and limited energy reserves directly restrict the transmission power and continuous operating time of buoy cooperative relaying. Existing research, because it does not incorporate buoy energy replenishment into the system design and does not coordinate the resource allocation relationship between information decoding and energy harvesting, results in optimization schemes that cannot match the rate requirements of two-hop transmission through energy scheduling, and cannot continuously maintain the high-performance relaying state of multi-buoy cooperative beamforming. This fails to fully utilize the performance gains of cooperative communication, ultimately making it difficult to effectively reduce the total end-to-end transmission latency of the system, leading to low overall transmission efficiency of offshore communication networks. Summary of the Invention

[0006] This invention provides a resource scheduling method and system for an unmanned surface vessel (USV) assisted marine communication system based on wireless power, which solves the technical problem that existing research on USV assisted marine communication is unable to effectively reduce the total end-to-end transmission delay of the system, resulting in low overall transmission efficiency of the long-range communication network.

[0007] The first aspect of this invention provides a resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, comprising:

[0008] Obtain the basic configuration parameters of the marine communication scenario, and construct a physical model of the marine hierarchical communication system based on the basic configuration parameters of the marine communication scenario;

[0009] For the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine hierarchical communication system, the rate and energy of two-hop transmission are derived and modeled, and a set of rate and energy calculation models is output.

[0010] Based on the set of rate energy calculation models, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, an optimization problem is modeled, and the original optimization problem of minimizing end-to-end latency is output.

[0011] The original optimization problem of minimizing end-to-end delay is decomposed into subproblems, and the initial set of optimization variables and multiple subproblems are output.

[0012] Based on the initial set of optimization variables, each of the sub-problems is solved alternately, and the full set of optimization variables for this round is output.

[0013] The rate-energy calculation model set is used to determine convergence based on the full set of optimization variables in this round, and the optimal resource scheduling scheme is output.

[0014] Optionally, the step of constructing a physical model of a marine hierarchical communication system based on the basic configuration parameters of the marine communication scenario includes:

[0015] A two-dimensional Cartesian coordinate system is established, and the initial position of the unmanned surface vessel, the fixed position of the buoy, the position of the marine user, the position and safety radius of the obstacle, the effective antenna height of the node and the number of antennas in the basic configuration parameters of the marine communication scenario are mapped one by one to the two-dimensional Cartesian coordinate system to complete the assignment, so as to obtain a set of spatial parameters of nodes and obstacles with coordinate labels.

[0016] The total runtime in the basic configuration parameters of the marine communication scenario is evenly divided using the total number of time slots in the basic configuration parameters of the marine communication scenario to obtain the time slot division result.

[0017] The node spacing is calculated based on the node coordinates in the set of spatial parameters of the nodes and obstacles with coordinate labels.

[0018] The antenna array response matrix is ​​obtained by matrix derivation based on the node spacing, the effective antenna height of the nodes in the coordinate-annotated set of spatial parameters of nodes and obstacles, and the carrier wavelength in the basic configuration parameters of the marine communication scenario.

[0019] The first hop channel matrix and the second hop channel matrix are calculated based on the node spacing, the effective antenna height of the nodes in the coordinate-annotated node and obstacle spatial parameter set, the carrier wavelength in the basic configuration parameters of the marine communication scenario, and the antenna array response matrix.

[0020] Based on the time slot division results and the ocean current speed and maximum propulsion speed, obstacle position and safe radius, and maximum turning angle in the basic configuration parameters of the marine communication scenario, navigation constraints for unmanned surface vessels are constructed.

[0021] A physical model of a marine hierarchical communication system is constructed based on the set of coordinate-labeled node and obstacle spatial parameters, the time slot division results, the first hop channel matrix, the second hop channel matrix, and the unmanned surface vessel navigation constraints.

[0022] Optionally, the rate-energy calculation model set includes a first-hop reachable rate calculation formula, a buoy time-slot energy harvesting calculation formula, and a second-hop reachable rate calculation formula; the process of performing two-hop transmission rate and energy derivation modeling on the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine hierarchical communication system, and outputting the rate-energy calculation model set, includes:

[0023] Introducing variables to be optimized, including the unmanned surface vessel's transmit beamforming matrix, the buoy power split ratio, and the buoy cooperative beamforming matrix;

[0024] Based on the first hop channel matrix, the unmanned surface vessel transmit beamforming matrix, the buoy power division ratio, and the information decoding circuit noise power and additive white Gaussian noise in the basic configuration parameters of the marine communication scenario, the first hop information transmission rate is derived, and the formula for calculating the first hop achievable rate is output.

[0025] Based on the first hop channel matrix, the unmanned surface vessel transmit beamforming matrix, the buoy power division ratio, and combined with the energy conversion efficiency, additive white Gaussian noise, and time slot division results in the basic configuration parameters of the marine communication scenario and the physical model of the marine hierarchical communication system, the energy harvesting amount is derived, and the formula for calculating the energy harvesting amount of the buoy per time slot is output.

[0026] Based on the second-hop channel matrix, the buoy cooperative beamforming matrix, and the additive white Gaussian noise power in the basic configuration parameters of the marine communication scenario, the second-hop cooperative transmission rate is derived, and the formula for calculating the second-hop achievable rate is output.

[0027] Optionally, the optimization problem modeling based on the rate-energy calculation model set, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, outputting the original optimization problem of minimizing end-to-end latency, includes:

[0028] Based on the first hop reachability calculation formula and the second hop reachability calculation formula, combined with the file size to be transmitted in the basic configuration parameters of the marine communication scenario, the end-to-end total latency is derived, and the optimization objective function is output.

[0029] Based on the formula for calculating the energy harvested per time slot of the buoy, and combined with the calculation relationship of the total transmission energy consumption of the second hop buoy derived from the formula for calculating the second hop reachability rate and the buoy cooperative beamforming matrix, the energy causal constraint is derived, and the energy causal constraint expression is output.

[0030] The unmanned surface vessel (USV) navigation constraints are extracted from the physical model of the marine hierarchical communication system. Combined with the USV transmission power constraints, buoy transmission power constraints derived from the maximum power threshold in the basic configuration parameters of the marine communication scenario, and the energy causal constraint expression, all constraints are integrated to output an optimized constraint set.

[0031] Based on the objective function and the set of constraints, a primal optimization problem for minimizing end-to-end latency is constructed.

[0032] Optionally, the multiple sub-problems include a first sub-problem, a second sub-problem, and a third sub-problem; the step of alternately solving each sub-problem according to the initial set of optimization variables to output the full set of optimization variables for this round includes:

[0033] The buoy power split ratio and buoy cooperative beamforming matrix are extracted as fixed parameters from the initial set of optimization variables. The unmanned surface vessel trajectory is extracted as a local iteration point from the initial set of optimization variables. The trajectory-related weighted minimum mean square error method and the continuous convex approximation method are used to transform and solve the first subproblem, and the updated unmanned surface vessel trajectory and the updated unmanned surface vessel transmission beamforming matrix are output.

[0034] Using the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmission beamforming matrix, and the buoy cooperative beamforming matrix in the initial set of optimization variables as fixed parameters, the buoy power split ratio in the initial set of optimization variables is extracted as a local iteration point. The second subproblem is transformed into a convexity and solved using a concave-convex process and a first-order Taylor expansion method, and the updated buoy power split ratio is output.

[0035] Using the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmit beamforming matrix, and the updated buoy power division ratio as fixed parameters, the weighted minimum mean square error method is used to transform and solve the third subproblem, and the updated buoy cooperative beamforming matrix is ​​output.

[0036] By integrating the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmit beamforming matrix, the updated buoy power split ratio, and the updated buoy cooperative beamforming matrix, a full set of optimization variables for this round is generated.

[0037] Optionally, the step of using the rate-energy calculation model set to determine convergence based on the full set of optimization variables in this round and outputting the optimal resource scheduling scheme includes:

[0038] Substitute the full optimization variables of this round into the set of rate energy calculation models to calculate the end-to-end total transmission delay corresponding to the current iteration;

[0039] By comparing the total end-to-end transmission latency of the current iteration with that of the previous iteration, and combining this with the convergence threshold, a convergence determination is made. If convergence is achieved, the set of all optimized variables for this round is determined as the optimal resource scheduling scheme.

[0040] The second aspect of this invention provides a resource scheduling system for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, comprising:

[0041] The acquisition module is used to acquire basic configuration parameters of the marine communication scenario and construct a physical model of the marine hierarchical communication system based on the basic configuration parameters of the marine communication scenario.

[0042] The first modeling module is used to perform rate and energy derivation modeling for the first hop channel matrix, the second hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine layered communication system, and output a set of rate and energy calculation models.

[0043] The second modeling module is used to model the optimization problem based on the set of rate energy calculation models, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, and output the original optimization problem of minimizing end-to-end latency.

[0044] The decomposition module is used to decompose the original optimization problem of minimizing end-to-end delay into sub-problems and output an initial set of optimization variables and multiple sub-problems.

[0045] The solution module is used to solve each of the sub-problems alternately based on the initial set of optimization variables, and output the full set of optimization variables for this round.

[0046] The determination module is used to perform convergence determination based on the set of rate-energy calculation models and the set of full-scale optimization variables in this round, and output the optimal resource scheduling scheme.

[0047] A third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, as described above.

[0048] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying as described above.

[0049] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, as described above.

[0050] As can be seen from the above technical solutions, the present invention has the following advantages:

[0051] The above-mentioned technical solution of the present invention provides a resource scheduling method for an unmanned surface vessel (USV)-assisted marine communication system based on wireless power carrying. This method acquires basic configuration parameters of the marine communication scenario and constructs a physical model of a layered marine communication system based on these parameters. It then performs rate and energy derivation modeling for two-hop transmission on the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario within the physical model, outputting a set of rate and energy calculation models. Finally, based on the set of rate and energy calculation models, the USV navigation constraints in the physical model of the layered marine communication system, and the basic configuration parameters of the marine communication scenario, it models an optimization problem and outputs an end-to-end delay minimization principle. The initial optimization problem is decomposed into subproblems to minimize end-to-end latency, outputting an initial set of optimization variables and multiple subproblems. Each subproblem is solved alternately based on the initial set of optimization variables, outputting the full set of optimization variables for this round. A rate-energy calculation model set is used to determine convergence based on the full set of optimization variables for this round, outputting the optimal resource scheduling scheme. Based on the above scheme, this invention, by acquiring basic configuration parameters of the marine communication scenario and constructing a physical model of the marine hierarchical communication system, can complete the unified modeling of node spatial attributes, time-domain time slot division, double-hop channel characteristics, and unmanned surface vessel navigation constraints, providing a complete physical constraint and computational foundation for subsequent joint optimization. Based on this, the invention addresses the physical constraints and computational foundation for subsequent joint optimization. The model derives and models the rate and energy of two-hop transmission using the first-hop and second-hop channel matrices in the theoretical model, forming a set of rate and energy calculation models that includes calculations of the two-hop achievable rate and buoy time-slot energy harvesting. This allows the buoy energy supply process and two-hop transmission performance to be incorporated into a unified calculation framework. Subsequently, combining the set of rate and energy calculation models with the unmanned surface vessel's navigation constraints and basic configuration parameters, an optimization problem is modeled, constructing a primal optimization problem with the goal of minimizing end-to-end latency. This incorporates energy causal constraints, transmit power constraints, and navigation constraints into the optimization constraint system, anchoring the core objective of reducing transmission latency at the problem definition level. Finally, the initial optimization problem is obtained by decomposing the primal optimization problem into subproblems. The system employs a set of variables and multiple sub-problems, and alternately solves each sub-problem based on the initial set of optimized variables to obtain the full set of optimized variables for this round. This phased solution approach overcomes the challenges of solving multi-variable coupling problems, efficiently achieving coordinated optimization of unmanned surface vessel trajectory, transmission beamforming, buoy power segmentation, and cooperative beamforming. Finally, relying on the rate-energy calculation model set combined with the full set of optimized variables for this round, it completes convergence determination and outputs the optimal resource scheduling scheme. Under the premise of satisfying buoy energy constraints and navigation constraints, it can accurately match the rate requirements of two-hop transmission, fully release the performance gains of multi-buoy cooperative forwarding, effectively reduce the total end-to-end transmission latency of the system, and thus improve the overall transmission efficiency of the offshore communication network. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating the steps of a resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, as provided in Embodiment 1 of the present invention.

[0054] Figure 2 This is a system model diagram of a resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, provided in Embodiment 1 of the present invention.

[0055] Figure 3 The convergence graph of Simulation 1 provided in Embodiment 1 of the present invention;

[0056] Figure 4 The trajectory diagram of simulation 2 provided in Embodiment 1 of the present invention;

[0057] Figure 5 The total latency of simulation 3 provided in Embodiment 1 of the present invention varies with the maximum transmit power of the unmanned surface vessel. A schematic diagram illustrating the changes;

[0058] Figure 6 This is a schematic diagram illustrating the change in total latency of simulation 4 as a function of file size L, provided in Embodiment 1 of the present invention.

[0059] Figure 7 This is a structural block diagram of a resource scheduling system for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, provided in Embodiment 2 of the present invention. Detailed Implementation

[0060] This invention provides a resource scheduling method and system for an unmanned surface vessel (USV) assisted marine communication system based on wireless power, which solves the technical problem that existing research on USV assisted marine communication is unable to effectively reduce the total end-to-end transmission delay of the system, resulting in low overall transmission efficiency of the long-range communication network.

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of relevant departments, and in compliance with relevant laws, regulations, and standards. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0062] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, as provided in Embodiment 1 of the present invention.

[0063] This invention provides a resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, comprising:

[0064] Step 101: Obtain the basic configuration parameters of the marine communication scenario, and construct a physical model of the marine layered communication system based on the basic configuration parameters of the marine communication scenario.

[0065] It should be noted that the basic configuration parameters of the marine communication scenario are obtained by combining on-site surveys of the marine communication scenario, parameter calibration of node communication equipment, marine hydrological environment monitoring and collection, and setting of communication task requirements. The basic configuration parameters of the marine communication scenario include the initial position coordinates of the unmanned surface vessel, the number and fixed position coordinates of buoys, the number and position coordinates of marine users, the number of antennas configured at each node, the effective antenna height of each node, the carrier wavelength, the position coordinates and safety radius of obstacles, the total runtime, the total number of time slots, the maximum propulsion speed of the unmanned surface vessel, the ocean current speed, the maximum transmission power of the unmanned surface vessel, the maximum transmission power of the buoys, the additive white Gaussian noise power, the noise power of the information decoding circuit, the energy conversion efficiency, the size of the file to be transmitted, the maximum turning angle of the unmanned surface vessel, and the additive white Gaussian noise.The initial position coordinates of the unmanned surface vessel (USV) were obtained by collecting positioning data from the satellite positioning module onboard the USV at the start of the mission. The number of buoys and their fixed position coordinates were obtained by counting the total number of buoys in the sea area buoy deployment log and combining the anchoring position coordinates transmitted back by the buoys' built-in positioning terminals. The number of marine users and their location coordinates were obtained by counting the user scale from the network registration information of the operating terminals in the sea area and combining the real-time satellite positioning data of each user terminal. The number of antennas configured at each node was obtained by consulting the hardware equipment manuals of the three types of communication nodes—USVs, buoys, and marine users—and counting the total number of array elements of the corresponding antenna arrays. The effective antenna height was obtained by measuring the vertical height of the antenna mounting base at each node from the sea level through on-site surveying and calibration using local sea level elevation data. The carrier wavelength was calculated based on the communication system's preset operating carrier frequency and the propagation speed of electromagnetic waves in free space. The obstacle location coordinates and safety radius were obtained by extracting the location coordinates of islands, reefs, and underwater structures from the target sea area's electronic nautical chart and underwater topographic survey data, and setting corresponding safety radii based on the obstacle's physical dimensions and navigation safety avoidance regulations. The total runtime was directly set based on the continuous operation cycle of this marine communication mission. The total number of time slots was determined. The required system scheduling accuracy and channel time-varying rate are determined through communication protocol parameter configuration. The maximum propulsion speed of the unmanned surface vessel (USV) is obtained by referring to the factory performance calibration parameters of the USV propulsion system. The ocean current speed is obtained by real-time ocean current velocity and direction data collected by marine hydrological monitoring stations deployed in the target sea area. The maximum transmit power of the USV is obtained by referring to the factory rated RF power parameters of the USV communication transmitter. The maximum transmit power of the buoy is obtained by referring to the rated power parameter manual of the buoy communication forwarding module. The additive white Gaussian noise power is determined by the channel background noise power data obtained by field testing in the target sea area using a channel tester. The noise power of the information decoding circuit is obtained by simulation calculation or laboratory noise test calibration of the hardware circuit parameters of the buoy information decoding branch. The energy conversion efficiency is obtained by referring to the nominal parameters of the RF-DC conversion chip of the buoy energy harvesting module. The size of the file to be transmitted is set according to the total data transmission volume requirements of this communication mission. The maximum turning angle of the USV is obtained by referring to the factory maneuverability parameters of the USV control system. The additive white Gaussian noise is a standard noise model selected to match the statistical characteristics of channel noise in the target sea area, and its amplitude distribution and power spectrum characteristics are determined by combining the measured noise power. ;

[0066] Next, a two-dimensional Cartesian coordinate system was established, and the initial position of the unmanned surface vessel (USV), the fixed position of the buoy, the position of the marine user, the position of the obstacle, the safety radius, and the node antenna attributes were mapped one by one into the coordinate system to complete the assignment, resulting in a set of spatial parameters of nodes and obstacles with coordinate labels. Then, the total runtime was evenly divided according to the total number of time slots to obtain the time slot division result. The node spacing was calculated based on the node coordinates, and the antenna array response matrix of the two-hop link was derived by combining the antenna height and carrier wavelength. The first-hop channel matrix from the USV to the buoy and the second-hop channel matrix from the buoy to the marine user were calculated for each time slot. At the same time, the navigation constraints of the USV were constructed by combining the ocean current speed, maximum propulsion speed, obstacle parameters, and maximum turning angle. Finally, the above spatial, temporal, channel, and constraint elements were integrated to complete the construction of the physical model of the marine hierarchical communication system.

[0067] Further, step 101 may include the following sub-steps:

[0068] S11. Establish a two-dimensional Cartesian coordinate system, and map the initial position of the unmanned surface vessel, the fixed position of the buoy, the position of the marine user, the position and safety radius of the obstacle, the effective antenna height of the node and the number of antennas in the basic configuration parameters of the marine communication scenario to the two-dimensional Cartesian coordinate system to complete the assignment, so as to obtain a set of spatial parameters of nodes and obstacles with coordinate labels.

[0069] S12. The total runtime in the basic configuration parameters of the marine communication scenario is evenly divided using the total number of time slots in the basic configuration parameters of the marine communication scenario to obtain the time slot division result.

[0070] S13. The node spacing is calculated based on the node coordinates in the set of spatial parameters of nodes and obstacles with coordinate annotations.

[0071] S14. Based on the node spacing, the effective antenna height of the nodes in the set of coordinate-annotated node and obstacle spatial parameters, and the carrier wavelength in the basic configuration parameters of the marine communication scenario, matrix derivation is performed to obtain the antenna array response matrix.

[0072] S15. Calculate the first hop channel matrix and the second hop channel matrix based on the node spacing, the effective antenna height of the nodes in the coordinate-annotated spatial parameter set of nodes and obstacles, the carrier wavelength and antenna array response matrix in the basic configuration parameters of the marine communication scenario.

[0073] S16. Based on the time slot division results and the ocean current speed and maximum propulsion speed, obstacle position and safe radius, and maximum turning angle in the basic configuration parameters of the marine communication scenario, construct the navigation constraints of the unmanned surface vessel.

[0074] S17. Based on the set of spatial parameters of nodes and obstacles with coordinate labels, the time slot division results, the first hop channel matrix, the second hop channel matrix, and the unmanned surface vessel navigation constraints, construct a physical model of the marine hierarchical communication system.

[0075] It should be noted that, as Figure 2 As shown, this invention relates to a resource scheduling method and system for a wirelessly powered unmanned surface vessel (USV)-assisted marine communication system, comprising a multi-antenna USV, multiple buoys, and multiple marine users (MUs). The USV acts as a mobile communication platform and wireless power supply node, responsible for transmitting information and providing wireless power to the buoys. The buoys, acting as cooperative relay nodes, collect energy while receiving information and collaboratively provide power to the marine users using the collected energy.

[0076] Communication services. Unmanned surface vessels are equipped with Each buoy and marine user is equipped with an antenna. root antenna and Root antenna. The buoy set and user set represent respectively... For and .

[0077] This invention uses a two-dimensional Cartesian coordinate system to describe marine communication scenarios. Total runtime Divided evenly into There are 1 time slot, each time slot having a length of 1. .make Indicates that the unmanned surface vessel is in a time slot The location, where due to the time slot length Small enough to assume the unmanned surface vessel's position remains constant within each time slot.

[0078] The initial position of the unmanned surface vessel is given as follows: Mobile users The position is represented as The buoy is fixed in Accordingly, time slots Unmanned surface vessels and buoys Between and buoys With mobile users The distances between them are expressed as follows:

[0079] ;

[0080] in, The node spacing between the unmanned surface vessel (USV) and buoy b within time slot t represents the spatial transmission distance of the link from the USV to the buoy in the first hop. This is the core input parameter for deriving the antenna array response matrix and the first hop channel matrix. The node spacing between buoy b and ocean user u within time slot t, i.e., the spatial transmission distance of the link from the second-hop buoy to the user, is the core input parameter for deriving the antenna array response matrix and the second-hop channel matrix. Let be the position coordinate vector of the unmanned surface vessel (USV) in time slot t, corresponding to its planar position in a two-dimensional Cartesian coordinate system within that time slot. This position remains constant within a single time slot. Let be the fixed position coordinate vector of buoy b, corresponding to the buoy's anchoring plane position in a two-dimensional Cartesian coordinate system, which does not change with time. Let be the position coordinate vector of the marine user u in time slot t, corresponding to the user's planar position in a two-dimensional Cartesian coordinate system within that time slot. , and These represent the various nodes, namely unmanned surface vessels and buoys. and mobile users The effective antenna height.

[0081] Maritime radio channels typically exhibit sparse scattering and strong sea surface reflection, leading to a dominant two-ray propagation structure. Therefore, time slots... Internal unmanned surface vessel and buoy Between and buoys With mobile users The channel matrices between them are modeled as follows:

[0082] ;

[0083] in For wavelength, and These are the antenna array response matrices for the unmanned surface vessel-to-buoy link and the buoy-to-mobile user link, respectively. The number of antenna elements equipped for a single buoy is both the total number of receiving antennas in the first hop UAV-to-buoy link, corresponding to the row dimension of the first hop antenna array response matrix, and the total number of transmitting antennas in the second hop buoy-to-ocean user link, corresponding to the column dimension of the second hop antenna array response matrix. It is the antenna scale parameter connecting the two hop links. The number of antenna elements equipped for the unmanned surface vessel (USV) is the total number of transmitting antennas in the first hop link from the USV to the buoy. This corresponds to the number of columns in the first hop antenna array response matrix, which determines the spatial control capability of the transmitting antenna array. The number of antenna elements equipped for a single marine user is the total number of receiving antennas in the link from the second hop buoy to the marine user, corresponding to the row dimension of the second hop antenna array response matrix, which determines the spatial reception capability of the receiving antenna array.

[0084] The trajectory of the unmanned surface vessel follows a dynamic model that takes into account ocean current speed. and maximum propulsion speed The given information is as follows:

[0085] ;

[0086] To ensure safe navigation, unmanned surface vessels (USVs) must maintain a minimum distance from every obstacle. and They represent obstacles. The location and safety radius. The constraint is then given as follows:

[0087] ;

[0088] Furthermore, the turning angle between adjacent time slots is limited to [value missing]. The displacement vector is defined as follows: Steering angle constraint It can be equivalently rewritten as:

[0089] ;

[0090] in, The displacement vector of the unmanned surface vessel (USV) within time slot t is defined by the difference in the position coordinates of the USV between time slot t and time slot t−1. It is used to characterize the displacement direction and magnitude of the USV between adjacent time slots and is an intermediate variable in the derivation of the steering angle constraint. The heading angle of the unmanned surface vessel within time slot t is used to characterize the direction of travel of the unmanned surface vessel. The difference in heading angle between adjacent time slots is constrained by the maximum turning angle of the unmanned surface vessel.

[0091] In this embodiment, a two-dimensional Cartesian coordinate system is established, and the initial position of the unmanned surface vessel, the fixed position of the buoy, the position of the marine user, the position and safety radius of obstacles, the effective antenna height of the node, and the number of antennas in the basic configuration parameters of the marine communication scenario are mapped one by one into the two-dimensional Cartesian coordinate system to complete the assignment, thus obtaining a set of spatial parameters of nodes and obstacles with coordinate labels; wherein the position of the unmanned surface vessel in time slot t is represented by a two-dimensional column vector. This indicates that the initial position at the start of the mission is given as... Furthermore, since the duration of a single time slot is short enough, the position of the unmanned surface vessel is set to remain constant within a single time slot, and buoy b is positioned as a fixed two-dimensional column vector. The anchorage position of the marine user u in time slot t is represented by a two-dimensional column vector. The total runtime N is evenly divided using the total number of time slots T in the basic configuration parameters of the marine communication scenario, resulting in a time slot division result containing T independent time slots, with each time slot having a duration of [duration missing]. The node spacing is calculated based on the node coordinates in the set of spatial parameters of nodes and obstacles with coordinate annotations. Specifically, the three-dimensional spatial transmission distance of the link is calculated by superimposing the square of the Euclidean distance of the planar coordinates with the square of the antenna height difference and then taking the square root. The first-hop link distance between the unmanned surface vessel and buoy b within time slot t is... The second-hop link distance between buoy b and ocean user u is Based on the node spacing, the effective antenna height of nodes within the coordinate-annotated set of spatial parameters for nodes and obstacles, and the carrier wavelength λ in the basic configuration parameters of the marine communication scenario, matrix derivation is performed to obtain the antenna array response matrix of the unmanned surface vessel to buoy link. Antenna array response matrix of buoy-to-user link For the dual-ray propagation structure formed by sparse scattering and strong sea surface reflection in the maritime wireless channel, the first-hop channel matrix from the unmanned surface vessel to buoy b within time slot t is calculated by combining the node spacing, effective antenna height of the nodes, carrier wavelength, and corresponding antenna array response matrix. And the second-hop channel matrix from buoy b to ocean user u The fractional and sinusoidal terms together characterize the large-scale channel fading characteristics caused by dual-ray interference, while the antenna array response matrix characterizes the spatial amplitude and phase response characteristics of the multi-antenna system to signals from different directions. Based on the time slot division results and the ocean current velocity, maximum propulsion speed, obstacle position and safe radius, and maximum turning angle in the basic configuration parameters of the marine communication scenario, unmanned surface vessel (USV) navigation constraints are constructed: First, dynamic constraints considering ocean current coupling are constructed, with... Represents the ocean current velocity vector within time slot t. Represents the maximum propulsion speed of the unmanned surface vessel, and the constraint expression is: This is used to limit the maximum displacement of the unmanned surface vessel relative to the seawater within a single time slot, ensuring that the trajectory meets the propulsion performance limits; secondly, obstacle avoidance constraints are constructed to... Represents the position coordinates of obstacle i, This represents the safe radius of the corresponding obstacle, and the constraint expression is: This is used to ensure that the unmanned surface vessel (USV) maintains a distance of at least a safe radius from all obstacles in any time slot; finally, a steering angle constraint is constructed, defining the displacement vector of the USV within time slot t as... ,by This represents the maximum turning angle of the unmanned surface vessel, and the turning angle constraint is equivalently transformed into... This is used to limit the maximum deflection of the unmanned surface vessel's (USV) navigation direction between adjacent time slots, ensuring that the trajectory meets the maneuverability limits. Finally, by integrating the coordinate-annotated set of spatial parameters of nodes and obstacles, the time slot division results, the first-hop channel matrix, the second-hop channel matrix, and the USV navigation constraints, a complete physical model of the marine hierarchical communication system is constructed.

[0092] Step 102: For the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine layered communication system, perform rate and energy derivation modeling for two-hop transmission, and output a set of rate and energy calculation models.

[0093] The rate-energy calculation model set includes the formula for calculating the first-hop reachable rate, the formula for calculating the energy harvested per time slot of the buoy, and the formula for calculating the second-hop reachable rate.

[0094] It should be noted that, firstly, three types of variables to be optimized are introduced: the unmanned surface vessel (USV) transmit beamforming matrix, the buoy power split ratio, and the buoy cooperative beamforming matrix. Combining the first-hop channel matrix and the noise power of the information decoding circuit in the basic configuration parameters, the signal-to-interference-plus-noise ratio (SIR) of the first-hop information decoding branch is derived, and the formula for calculating the first-hop achievable rate is obtained. Based on the first-hop channel matrix and the buoy power split ratio, combined with the energy conversion efficiency in the basic configuration parameters and the single time slot duration in the time slot division results, the effective received energy of the buoy energy harvesting branch is derived, and the formula for calculating the buoy's time-slot-wise energy harvesting amount is obtained. Combining the second-hop channel matrix and the additive white Gaussian noise power in the basic configuration parameters, the SIR of the second-hop cooperative transmission link is derived, and the formula for calculating the second-hop achievable rate is obtained. Finally, the three types of calculation formulas are integrated to form a rate-energy calculation model set.

[0095] Furthermore, step 102 may include the following sub-steps:

[0096] S21. Introduce variables to be optimized, including the unmanned surface vessel's transmit beamforming matrix, the buoy power split ratio, and the buoy cooperative beamforming matrix.

[0097] S22. Based on the first hop channel matrix, the unmanned surface vessel transmit beamforming matrix, and the buoy power division ratio, combined with the information decoding circuit noise power and additive white Gaussian noise in the basic configuration parameters of the marine communication scenario, the first hop information transmission rate is derived, and the formula for calculating the first hop achievable rate is output.

[0098] S23. Based on the first hop channel matrix, the unmanned surface vessel transmit beamforming matrix, the buoy power division ratio, and combined with the energy conversion efficiency in the basic configuration parameters of the marine communication scenario, the additive white Gaussian noise, and the time slot division results in the physical model of the marine hierarchical communication system, the energy collection amount is derived, and the formula for calculating the energy collection amount of the buoy per time slot is output.

[0099] S24. Based on the second-hop channel matrix and the buoy cooperative beamforming matrix, and combined with the additive white Gaussian noise power in the basic configuration parameters of the marine communication scenario, the second-hop cooperative transmission rate is derived, and the formula for calculating the second-hop achievable rate is output.

[0100] It should be noted that, to support relay-assisted content transmission at sea, a layered transmission protocol is employed within each operational cycle. Specifically, the content transmission process is completed through two sequential transmission procedures. At the beginning of each operational cycle, each mobile user requests a file from the content library. Since the buoy lacks local caching capabilities, all requested files are first transmitted via the UAV-to-buoy forward link.

[0101] In the first hop, the unmanned surface vessel (USV) transmits the request file to the buoy cluster. Simultaneously, the buoys harvest energy from the received radio frequency signals via SWIPT. After the request content is successfully acquired, the second hop begins, in which the buoys collaboratively transmit the request file to the mobile user using the energy harvested in the first hop.

[0102] First hop: Unmanned surface vessel to buoy transfer. Consider a content library where all files have the same size. .

[0103] Assuming the unmanned surface vessel can support If there are multiple parallel data streams, then the buoy... Received signal at the location Represented as:

[0104] ;

[0105] in This indicates that the unmanned surface vessel is targeted at mobile users. The transmitted beamforming matrix, For a normalized data signal vector, satisfying , and when hour . Let be an additive white Gaussian noise (AWGN) vector, satisfying d represents the number of parallel data streams supported by the unmanned surface vessel, which determines the column dimension of the transmit beamforming matrix and characterizes the scale of independent data paths that can be transmitted simultaneously in the first hop link. It is a d-order identity matrix used to characterize the power constraints of the normalized data signal. Let be the additive white Gaussian noise vector at the buoy b receiver within time slot t, composed of the background noise from the first hop receiving antenna. Let be the additive white Gaussian noise power at the receiver of buoy b, representing the intensity of the background noise at the first hop receiving antenna. For M b The identity matrix, corresponding to the number of antennas at the buoy receiver, is used to characterize the power distribution characteristics of the noise vector.

[0106] The received signal is divided into two parts by a power divider, with a distribution ratio of 1:1. ,in Used for information decoding, the remaining part It was allocated for energy harvesting.

[0107] Therefore, the buoy receives mobile users from the unmanned surface vessel. The achievable speed of files Given:

[0108] ;

[0109] in, Let be the power division ratio of buoy b within time slot t, corresponding to the buoy power division ratio, with a value range of [0,1]. This indicates the noise power of the information decoding circuit. To add noise to the interference, write it as

[0110] ;

[0111] in, The set of all users other than the target user u is used to calculate the total power of interference signals from other users in the same channel. (Float) In the time slot Energy collected internally Given:

[0112] ;

[0113] in This indicates energy conversion efficiency.

[0114] Second hop: Buoy to mobile user transmission. After the request content is successfully transmitted in the first hop, the buoy uses the collected energy in the second hop to cooperatively send the request file to the corresponding mobile user.

[0115] Assuming a buoy Able to support One data stream, then mobile users Received signal at the location for

[0116] ;

[0117] in Represents the transmitted beamforming matrix. For mobile users The AWGN vector at that location satisfies , is the normalized data signal vector corresponding to user j within time slot t, and is the original data vector forwarded by the second buoy.

[0118] Mobile users achievable rate Represented as:

[0119] ;

[0120] in To add noise to the interference, the given value is:

[0121] ;

[0122] in, Let be the additive white Gaussian noise power at the mobile user u receiver, characterizing the intensity of the background noise at the second-hop user receiver. For M u The identity matrix, corresponding to the dimension of the number of antennas at the user receiver, is used to characterize the power distribution characteristics of the user-end noise vector. For time delay and energy constraints, let... and The number of time slots required for the first hop and the second hop are respectively represented as:

[0123] ;

[0124] in,, For mobile users The bottleneck transmission rate among all buoys; S is a variable representing the number of time slots, used to iterate and solve for the minimum number of time slots that meet the data transmission requirements; Let be the bottleneck transmission rate of mobile user u in the first hop within time slot t. Take the minimum achievable rate of the user at all buoys, which corresponds to the actual effective transmission rate of the user's first hop.

[0125] In this embodiment, variables to be optimized are introduced, including the unmanned surface vessel's transmit beamforming matrix, the buoy power split ratio, and the buoy cooperative beamforming matrix. The unmanned surface vessel is configured with a corresponding transmit beamforming matrix for each mobile user. d represents the number of parallel data streams supported by the unmanned surface vessel, and a single buoy is configured with a corresponding cooperative beamforming matrix for each mobile user. s represents the number of second-hop parallel data streams supported by a single buoy, and each buoy is configured with a time-slot-adjustable power split ratio. This is used to divide the power ratio between the information decoding branch and the energy harvesting branch for receiving radio frequency signals. Based on the first-hop channel matrix, the unmanned surface vessel's transmit beamforming matrix, and the buoy power division ratio, combined with the noise power of the information decoding circuit and additive white Gaussian noise in the basic configuration parameters of the marine communication scenario, the first-hop information transmission rate is derived, and the formula for calculating the first-hop achievable rate is output. Specifically, a first-hop buoy receiving signal model is first constructed. The received signal of buoy b within time slot t is the sum of the superposition components of all users' transmitted data signals after transmission through the first-hop channel and the additive white Gaussian noise at the buoy receiving end, expressed as: After the received signal enters the power divider at the buoy end, the ratio is... The signal components enter the information decoding branch. First, the co-channel interference power brought by the data streams of users other than the target user u is calculated. Then, the background noise power of the buoy receiver is added to obtain the interference plus noise matrix. Then, the circuit noise power introduced by the information decoding branch hardware is added on top. Finally, based on the determinant calculation form of MIMO (Multiple Input Multiple Output) channel capacity, the first-hop reachable rate of user u corresponding to buoy b within time slot t is derived. The minimum achievable rate for a single user across all buoys is taken as the user's first-hop bottleneck transmission rate, used to characterize the user's actual effective transmission capacity for the first hop. Based on the first-hop channel matrix, the unmanned surface vessel's transmit beamforming matrix, and the buoy power division ratio, combined with the energy conversion efficiency in the basic configuration parameters of the marine communication scenario, additive white Gaussian noise, and the time slot division results in the physical model of the marine hierarchical communication system, the energy harvesting amount is derived, and the formula for calculating the buoy's time slot-by-time energy harvesting amount is output; the specific ratio is... The received signal component enters the energy harvesting branch. First, the total received power of all user-transmitted signals at the buoy end is calculated using the Frobenius norm square operation. This is then superimposed with the total noise power introduced by all receiving antennas on the buoy. Next, the RF energy is multiplied by the energy conversion efficiency to convert it into usable DC energy. Finally, this is multiplied by the single time slot duration to obtain the total harvested energy within the time slot. Based on the second-hop channel matrix and the buoy cooperative beamforming matrix, combined with the additive white Gaussian noise power in the basic configuration parameters of the marine communication scenario, the second-hop cooperative transmission rate is derived, and the formula for calculating the second-hop achievable rate is output. Specifically, a second-hop user received signal model is first constructed. The received signal of mobile user u within time slot t is the sum of the superposition components of all user data signals forwarded by all buoys after transmission through the second-hop channel and the additive white Gaussian noise at the user end, expressed as follows: Then, the co-channel interference power caused by the data streams forwarded by users other than the target user u is calculated, and the background noise power at the user's receiver is added to obtain the interference plus noise matrix. Finally, based on the multi-node cooperative MIMO channel capacity formula, the second-hop achievable rate of mobile user u within time slot t is derived. The above formulas for calculating the first hop reachable rate, the buoy time-slot energy collection, and the second hop reachable rate are integrated to form a set of rate-energy calculation models.

[0126] Step 103: Based on the rate energy calculation model set, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, perform optimization problem modeling and output the original optimization problem of minimizing end-to-end latency.

[0127] It should be noted that, firstly, based on the formulas for calculating the first and second hop reachable rates, and combined with the file size to be transmitted in the basic configuration parameters, the time slot scale required to complete the full file transmission in both hops is calculated, deriving an optimization objective function with minimizing the total end-to-end transmission time as its core. Then, based on the formula for calculating the energy collected per time slot of the buoy, the total collected energy of each buoy is accumulated. Combined with the formula for calculating the second hop reachable rate and the buoy cooperative beamforming matrix, the total transmission energy consumption of the second hop buoy is derived, constructing an energy causal constraint expression that limits the buoy's transmission energy consumption to not exceeding its accumulated collected energy. Subsequently, the unmanned surface vessel (USV) navigation constraints are extracted, and combined with the maximum power threshold in the basic configuration parameters, the USV transmission power constraints and buoy transmission power constraints are derived, integrating all constraints to form an optimization constraint set. Finally, based on the optimization objective function and the optimization constraint set, a complete end-to-end latency minimization primal optimization problem is constructed.

[0128] Furthermore, step 103 may include the following sub-steps:

[0129] S31. Based on the formulas for calculating the first hop reachable rate and the second hop reachable rate, and combined with the file size to be transmitted in the basic configuration parameters of the marine communication scenario, derive the end-to-end total delay and output the optimization objective function.

[0130] S32. Based on the formula for calculating the energy harvested per time slot of the buoy, and combined with the calculation relationship of the total transmission energy consumption of the second-hop buoy derived from the formula for calculating the achievable rate of the second hop and the buoy cooperative beamforming matrix, the energy causal constraint is derived, and the energy causal constraint expression is output.

[0131] S33. Extract the unmanned surface vessel (USV) navigation constraints from the physical model of the marine hierarchical communication system. Combine the USV transmission power constraints, buoy transmission power constraints, and energy causal constraint expressions derived from the maximum power threshold in the basic configuration parameters of the marine communication scenario, perform full constraint integration, and output the optimized constraint condition set.

[0132] S34. Based on the objective function and the set of optimization constraints, construct the original optimization problem of minimizing end-to-end delay.

[0133] It should be noted that, since the buoys are powered only by the energy collected in the first hop, each buoy must satisfy the following energy causality constraint:

[0134] ;

[0135] The total time for two hops is defined as:

[0136] ;

[0137] For problem modeling, the goal of this invention is to jointly optimize the trajectory of unmanned surface vessels. Unmanned surface vessel beamforming Power distribution ratio and buoy beamforming To minimize end-to-end content transmission latency, which includes the forward transmission hop from the unmanned surface vessel to the buoy and the cooperative access hop from the buoy to the user, the model is as follows:

[0138] ;

[0139] Problem (P0) is highly nonconvex due to its coupled optimization variables, nonconvex reachability expression, and trajectory-dependent constraints. Therefore, obtaining the global optimum is often difficult, prompting us to propose an efficient iterative optimization algorithm in the next section.

[0140] Therefore, the present invention aims to minimize the end-to-end transmission delay of the system by jointly optimizing the unmanned surface vessel trajectory, unmanned surface vessel transmission beamforming, buoy power allocation coefficient, and buoy cooperative beamforming, while satisfying the constraints of transmission power, energy causality, navigation speed, navigation obstacle avoidance, and turning angle.

[0141] In this embodiment, based on the calculation formulas for the first and second hop reachable rates, and combined with the file size to be transmitted in the basic configuration parameters of the marine communication scenario, the end-to-end total latency is derived, and the optimization objective function is output. Specifically, the minimum first-hop reachable rate of each mobile user at all buoys is taken as the user's first-hop bottleneck transmission rate. Starting from the first time slot, the bottleneck transmission data volume of all users is accumulated time slot by time slot. When the accumulated transmission data volume is not less than the file size to be transmitted for the first time, the corresponding time slot number is the minimum number of time slots T1 required to complete the first hop full file transmission. Similarly, starting from the first time slot of the second hop, the second hop transmission data volume of all users is accumulated time slot by time slot to obtain the minimum number of time slots T2 required to complete the second hop full file forwarding. Then, the single time slot duration in the time slot division result is combined with the time slot allocation result. The total end-to-end delay is obtained by adding the product of the number of transmission time slots in the first hop and the duration of a single time slot, and the product of the number of transmission time slots in the second hop and the duration of a single time slot. With minimizing the total end-to-end delay as the core optimization objective, the optimization variables are identified as including four categories: the time-slot-by-time unmanned surface vessel (USV) position vector, the USV's transmit beamforming matrix for each user, the time-slot-by-time power split ratio of each buoy, and the cooperative beamforming matrix of each buoy for each user. This forms the optimization objective function. Based on the formula for calculating the time-slot-by-time energy harvested by the buoy, and combined with the calculation relationship for the total transmit energy consumption of the second-hop buoy derived from the second-hop reachability formula and the buoy cooperative beamforming matrix, energy causal constraints are derived, outputting an energy causal constraint expression. Specifically, the total transmit power of all users forwarding data streams for a single buoy within a single time slot is calculated using the Frobenius norm square operation. The transmit power of a single time slot within all time slots of the second hop is multiplied by the single time slot duration τ and summed to obtain the total transmit energy consumption of a single buoy in the entire second-hop forwarding phase. Then, the time-slot-by-time energy harvested by the buoy within all time slots of the first hop is summed to obtain the total effective DC energy harvested by the buoy in the first-hop receiving phase, thus constructing the constraint expression. The requirement is that the total transmission energy consumption of each buoy's second hop should not exceed the energy accumulated during its first hop, and this holds true for all buoys, forming a complete energy causal constraint expression. Unmanned surface vessel (USV) navigation constraints are extracted from the physical model of the marine hierarchical communication system. These constraints, along with the USV transmission power constraints, buoy transmission power constraints derived from the maximum power threshold in the basic configuration parameters of the marine communication scenario, and the energy causal constraint expression, are then fully integrated to output an optimized constraint set. The USV navigation constraints include dynamic velocity constraints considering ocean current velocity coupling, obstacle avoidance constraints with safe distances, steering angle constraints due to course deflection in adjacent time slots, and initial position fixation constraints for the USV. The USV transmission power constraint is that the sum of the squares of the Frobenius norms of the transmission beamforming matrices corresponding to all users of the USV within a single time slot should not exceed the maximum transmission power of the USV. ,Right now The buoy transmit power constraint is that the sum of the squares of the Frobenius norms of the cooperative beamforming matrices corresponding to all users of a single buoy within a single time slot does not exceed the buoy's maximum transmit power. ,Right now This holds true for all buoys, and also supplements the range constraint of the buoy power split ratio. All the above constraints are categorized and integrated to form a complete set of optimization constraints. Based on the optimization objective function and the set of optimization constraints, a primitive optimization problem for minimizing end-to-end delay is constructed and denoted as problem (P0). This problem exhibits highly non-convex properties due to the coupling of multiple optimization variables, the non-convexity of the reachable rate expression, and the inclusion of trajectory-related non-convex constraints. The global optimal solution cannot be directly obtained through conventional convex optimization methods, thus providing a standard problem prototype for subsequent subproblem decomposition and iterative solving.

[0142] Step 104: Decompose the original optimization problem of minimizing end-to-end delay into subproblems, and output the initial set of optimization variables and multiple subproblems.

[0143] The multiple subproblems include the first subproblem, the second subproblem, and the third subproblem.

[0144] It should be noted that, given the characteristics of the original optimization problem—multi-variable coupling, high non-convexity, and difficulty in direct solution—it is decoupled and decomposed according to the categories of optimization variables. First, based on the basic configuration parameters of the marine communication scenario, initial feasible values ​​satisfying all constraints are assigned to various optimization variables. These include the initial trajectory sequence formed by the initial position of the unmanned surface vessel (USV) in each time slot, the initial transmit beamforming matrix of the USV for each user, the initial power split ratio of each buoy in each time slot, and the initial cooperative beamforming matrix of each buoy for each user. These are integrated to form the initial set of optimization variables. Then, the original optimization problem is decomposed into four independent sub-problems: USV trajectory optimization, USV transmit beamforming optimization, buoy power split ratio optimization, and buoy cooperative beamforming optimization. Each sub-problem optimizes the target variable independently while keeping the other optimization variables fixed at their current values. Each sub-problem inherits the constraints corresponding to the original problem, ensuring that the feasible solution of the sub-problem is always within the feasible region of the original problem.

[0145] Specifically, this invention designs an AO (Alternating Optimization) algorithm to solve problem (P0). Since trajectory variables, beamforming matrices, and power splitting coefficients are highly coupled in the objective function and constraints, directly solving problem (P0) is extremely difficult. To address this challenge, this invention decomposes the original problem into three sub-problems, each corresponding to a USV trajectory. With beamforming Optimization, power split ratio Optimization and buoy beamforming The optimization was carried out. Each sub-problem was addressed using a combination of WMMSE and SCA techniques.

[0146] For sub-problem one: optimizing the USV trajectory and beamforming :

[0147] In a given buoy beamforming and power split ratio In this case, problem (P0) can be simplified to:

[0148] ;

[0149] when and When fixed, minimize Equivalent to maximizing the minimum rate .

[0150] However, about and Both are non-convex. To overcome this difficulty, the trajectory-dependent weighted minimum mean square error (TD-WMMSE) algorithm is adopted, which is the trajectory-dependent weighted minimum mean square error algorithm.

[0151] Because the communication distance at sea is usually much greater than the antenna height, the data rate... It can be approximated as:

[0152] ;

[0153] in ; ,and .

[0154] The above reconstruction explicitly separates the distance term related to the trajectory. This facilitates subsequent optimization of the USV trajectory.

[0155] In the above formula, The approximate first-hop reachable rate of user u corresponding to buoy b within time slot t is an approximate form obtained by reconstructing the original rate expression based on the scenario characteristics of the maritime communication distance being much greater than the antenna height. The link distance term related to the unmanned surface vessel trajectory is explicitly separated to meet the solution requirements of subsequent unmanned surface vessel trajectory optimization. The equivalent useful signal matrix of mobile user u at the receiver of buoy b within time slot t is derived by combining the power division ratio, antenna height correlation coefficient, antenna array response matrix and unmanned surface vessel transmit beamforming matrix. It is the useful signal component matrix in the rate approximation expression. The equivalent interference plus noise matrix when buoy b receives user u signal within time slot t is composed of the equivalent interference matrix of other co-channel users and the equivalent total noise component after link distance scaling. It is the interference and noise term matrix in the rate approximation expression. The antenna height-related constant coefficient is obtained by multiplying the square of the effective antenna height of the unmanned surface vessel by the square of the effective antenna height of the buoy. It is a fixed coefficient term extracted during the approximation and simplification of the dual-ray propagation channel model. The equivalent total noise power figure is composed of the additive white Gaussian noise power of the buoy receiver scaled by the power division ratio and the noise power of the information decoding circuit. It is the equivalent noise power parameter in the rate approximation expression. The antenna array response matrix represents the link from the unmanned surface vessel (USV) to buoy b within time slot t, corresponding to the spatial response characteristics of the antenna array in the first hop link, with dimension M. b ×M v The amplitude weighting and phase shift characteristics of the transmit and receive antenna array of the link for signals transmitted in different directions are used to characterize the characteristics of the transmit and receive antenna array. It is a core component of the first hop channel matrix and together with the dual-ray interferometric fading coefficient, the complete first hop channel matrix is ​​calculated.

[0156] To establish the TD-WMMSE equivalence relation, this invention introduces an equivalent scaling of the received signal. And on each buoy Linear beamforming is used at this location. Accordingly, the buoy... For MU The estimate of the corresponding data symbol vector can be expressed as:

[0157] ;

[0158] in, The estimated output of the data symbol vector corresponding to the mobile user u by buoy b within time slot t is the result obtained after the equivalent scaled received signal is processed by the linear receiving beamforming matrix, and is used to construct the equivalent rate relationship of TD-WMMSE. Indicates buoy Used to decode MU The receiving beamforming matrix corresponding to the data stream, Indicates the noise of the ID circuit, and satisfies .

[0159] Therefore, buoy For MU The mean squared error (MSE) matrix can be written as:

[0160] ;

[0161] in, ; The equivalent channel-beamforming composite matrix of mobile user u at buoy b within time slot t is derived from the received beamforming matrix, the square root of the power division ratio, the antenna height coefficient, the antenna array response matrix, and the transmitted beamforming matrix. It is the intermediate calculation matrix for constructing the TD-WMMSE equivalence relation. The equivalent interference channel matrix of mobile user j at buoy b within time slot t is derived by combining the square root of the power division ratio, the antenna height coefficient, the antenna array response matrix and the corresponding transmit beamforming matrix, and is used to quantify the equivalent interference intensity brought by other users in the same channel.

[0162] Based on the obtained MSE matrix, the approximate rate is... This can be equivalently transformed into the following MSE-based form:

[0163] ;

[0164] in, Represents the MSE weight matrix; The equivalent first-hop reachable rate based on the mean square error form is the rate expression obtained by equivalently transforming the approximate first-hop reachable rate through weighted minimum mean square error. It is the core equivalent form of the TD-WMMSE algorithm, which can transform the non-convex rate optimization problem into an iteratively solvable mean square error optimization problem. The MSE weight matrix, i.e. the weight matrix in the weighted minimum mean square error framework, is used to weight and adjust the mean square error matrix. It is one of the core variables for the iterative update of the WMMSE algorithm. By iteratively optimizing the weight matrix, the equivalent rate and the original achievable rate can be mapped. The mean square error matrix (MSEM) represents the error covariance matrix between the estimated signal after beamforming processing at the receiver and the original transmitted data signal. It is a core indicator for measuring the accuracy of signal estimation at the receiver and a core computational parameter of the WMMSE algorithm. The trace operation is performed on a matrix, and its value is the sum of the values ​​of all elements on the main diagonal of the matrix. In this scenario, it is used to convert the weighted mean square error in matrix form into a scalar value, which is then used in the calculation of the equivalent rate. It is a d-order identity matrix, corresponding to the identity matrix of the parallel data stream dimension, used to characterize the benchmark form of signal estimation results under ideal error-free transmission conditions.

[0165] For any given transmit beamforming matrix , satisfy:

[0166] ;

[0167] Among them, when and The above equation holds true when updated as follows.

[0168] First, for fixed Through calculation Optimal receiving beamforming can be obtained. ,Right now

[0169] ;

[0170] in, The optimal receiving beamforming matrix is ​​derived by taking the derivative of the equivalent rate expression with respect to the receiving beamforming matrix and setting it to zero under the condition of a fixed unmanned surface vessel trajectory and transmitting beamforming matrix. This optimal linear receiving matrix minimizes the mean square error at the receiving end.

[0171] Secondly, for fixed Through calculation The optimal MSE weight matrix can be obtained, that is:

[0172] ;

[0173] in, The optimal MSE weight matrix is ​​derived under the conditions of fixed unmanned surface vessel trajectory, transmit beamforming matrix and receive beamforming matrix. The value is the inverse of the mean square error matrix. After substituting it, the equivalent rate based on MSE is exactly the same as the original approximate rate.

[0174] The above and Substitution Subsequently, the rate based on MSE is equivalent to the approximate rate. ,Right now:

[0175] ;

[0176] in And steps Depend on get, The equivalent channel matrix is ​​derived from the square root of the power split ratio, the antenna height correlation coefficient, the antenna array response matrix, and the transmit beamforming matrix. It is an intermediate equivalent channel parameter in the rate equivalence derivation process. The identity transformation property of matrix determinants, namely Sylvester's determinant theorem, is used to perform equivalent transformations on the determinants of matrices of different dimensions, simplifying the derivation of the rate expression.

[0177] Therefore, in fixed and hour, about and It is a concave shape.

[0178] Next, we will deal with the non-convex energy causality constraint.

[0179] because In this subproblem, the total energy consumption of the buoy in the second hop can be considered a constant, denoted as:

[0180] ;

[0181] in Let be the total transmit energy consumption of buoy b during the second hop forwarding phase. This value is constant when the buoy cooperative beamforming matrix is ​​fixed, and represents the energy consumption threshold that the buoy needs to satisfy in the energy causality constraint. Therefore, the energy causality constraint can be rewritten as:

[0182] ;

[0183] and The approximation used in the middle is similar. It can be approximated as:

[0184] ;

[0185] in For the approximate energy harvested by buoy b within time slot t, consistent with the processing logic of the approximate rate, it is an approximate form obtained by explicitly separating the distance term from the original energy harvesting expression, adapting to the solution requirements of trajectory optimization; to simplify notation, it is defined as follows:

[0186] ;

[0187] in To simplify the notation definition, the equivalent beam matrix is ​​obtained by multiplying the first-hop antenna array response matrix with the unmanned surface vessel's transmit beamforming matrix, which simplifies the writing of non-convex terms in the energy expression. Let be the square of the three-dimensional distance between the unmanned surface vessel and buoy b within time slot t, which is the sum of the square of the Euclidean distance in planar coordinates and the square of the antenna height difference. This is the denominator parameter of the non-convex fractional term for constructing the energy. Therefore... The non-convex term in can be written as:

[0188] ;

[0189] For functions ,about It is a joint convex shape. Therefore, it has local points. The first-order Taylor expansion at a given point gives the global affine lower bound:

[0190] ;

[0191] in, It is a general form of fractional convex function, expressed as the ratio of the square of the matrix F norm to the positive scalar. It has the property of joint convexity and is the basic function form for constructing lower bounds through successive convex approximations. , The value of the local reference point corresponding to the l-th iteration is the result of the optimization variables obtained in the previous iteration, which is the reference point for constructing the convex approximation using the first-order Taylor expansion; is ; is ;

[0192] Will and Substituting, we get:

[0193] ;

[0194] in ; The value of the non-convex term at the local point of the l-th iteration is the number of fractional terms calculated from the variables of the previous iteration. The equivalent beam matrix at the local point in the l-th iteration is obtained from the transmit beamforming matrix of the previous round; The value of the squared link distance at the local point in the l-th iteration is calculated from the position coordinates of the unmanned surface vessel in the previous round;

[0195] Therefore, a concave lower bound for energy collection can be expressed as:

[0196] ;

[0197] in, The concave lower bound for the energy collected by buoy b in the l-th iteration is approximated by constructing a lower bound for the non-convex energy collection through successive convex approximations, which is used to transform the non-convex energy causal constraint into a convex constraint; since The energy causal constraint can be approximated as:

[0198] ;

[0199] Obstacle avoidance constraints are also non-convex. To handle this non-convexity, we can... At local points Performing a first-order Taylor expansion at this point, we obtain:

[0200] ;

[0201] in, Let be the unmanned surface vessel position vector at a local point in the l-th iteration, and be the reference point for the first-order Taylor expansion of the obstacle avoidance constraint;

[0202] Steering angle constraint due to and The coupling between them results in non-convexity.

[0203] To address this problem, it is broken down into: ,in , and respectively form the lower concave boundary of the two parts.

[0204] for Around local points By constructing its concave lower boundary, we can obtain:

[0205] ;

[0206] in, yes A concave lower bound for the trajectory variable The first component function obtained from the decomposition of the steering angle constraint corresponds to the inner product term of the displacement vectors in adjacent time slots. It is a non-convex function and requires a separate concave lower bound to be constructed.

[0207] Assumption Then there is .for Using Young's inequality Decoupling the norm product yields:

[0208] ;

[0209] in To ensure the scaling factor is tight at local points, The second component function obtained from the decomposition of the steering angle constraint corresponds to the product of the displacement vector norm and the cosine of the maximum steering angle. It is a non-convex function and requires a separate concave lower bound construction. Because... and Since all trajectory variables are concave functions, the steering angle constraint can be replaced with the following convex approximation constraint:

[0210] ;

[0211] in, for The corresponding concave lower bound function, constructed at a local reference point, is a component of the steering angle constrained convex approximation. for The corresponding concave lower bound function, constructed by decoupling the norm product using Young's inequality, is a component of the steering angle-constrained convex approximation.

[0212] Therefore, the atomic problem can be restated, that is, the first subproblem is:

[0213] ;

[0214] in, The maximum transmit power threshold for unmanned surface vessels (USVs) is the upper limit of the transmit power constraint for USVs, which limits the total transmit power of all data streams of the USV to not exceed this hardware limit.

[0215] This problem is a convex problem and can be solved directly using the CVX Convex Optimization Toolbox.

[0216] Furthermore, regarding sub-problem two: optimizing the power split ratio In fixed , and In this case, problem (P0) can be simplified to:

[0217] ;

[0218] The main difficulty lies in the rate expression. about It is non-concave. To handle this difficulty, it is expressed as the difference of the following concave functions:

[0219] ;

[0220] in ,Notice, and about All are concave functions. The useful signal covariance matrix corresponding to user u at buoy b within time slot t is derived by combining the first hop channel matrix and the corresponding transmit beamforming matrix, and characterizes the spatial power distribution characteristics of the target user's useful signal.

[0221] Therefore, for a given local point , The first-order Taylor expansion gives the following global upper bound:

[0222] ;

[0223] in ;

[0224] Accordingly, One concave lower boundary is:

[0225] ;

[0226] Its satisfaction ; The first concave function component, obtained by decomposing the first hop reachable rate using the difference of concave functions, is formed by superimposing the noise matrix of the information decoding circuit with the total covariance matrix of the useful signal and interference scaled by the power division ratio, and then taking the logarithm of the determinant. With respect to the power division ratio being a concave function, it is the minuend in the form of the difference of concave functions. The second concave function component, obtained by decomposing the first hop reachable rate into the difference of concave functions, is formed by superimposing the noise matrix of the information decoding circuit into the interference noise covariance matrix scaled by the power division ratio and then taking the logarithm of the determinant. With respect to the power division ratio being a concave function, it is a subtraction term in the form of the difference of concave functions. The local reference power division ratio corresponding to the l-th iteration is the power division ratio of buoy b obtained from the previous iteration. Construct a reference point for the global upper bound by performing a first-order Taylor expansion; is the first-order gradient value of the h-function at the baseline power segmentation ratio in the l-th iteration, used to construct the first-order Taylor linear approximation upper bound of the h-function; The concave lower bound of the first hop reachable rate in the l-th iteration is derived by combining the difference decomposition of concave functions with the Taylor upper bound. Its value is no greater than the original reachable rate. Substituting it into the lower bound can transform the non-convex power split ratio optimization problem into a convex optimization problem.

[0227] Substituting the obtained lower bound of the rate into atomic problem two, the second subproblem can be reformulated as follows:

[0228] ;

[0229] This problem is a convex problem.

[0230] Furthermore, regarding sub-problem three: optimizing buoy beamforming In a given , and In this case, the energy collected during the first jump becomes a known amount. Define the buoy. Total available energy for:

[0231] ;

[0232] Under this setting, problem (P0) can be simplified to:

[0233] ;

[0234] Note that minimizing Equivalent to maximizing each MU in the second jump The minimum rate. However, the rate about It is non-concave. To overcome this difficulty, the WMMSE algorithm is adopted.

[0235] Each beamforming matrix and channel Stacked as a full network matrix, it is represented as:

[0236] ;

[0237] in, To construct the stacked beamforming matrix for mobile user u, the cooperative beamforming matrices of all buoys corresponding to that user are stacked row-blocks to form the full network beamforming matrix, with dimension BM. b ×s is used to transform the multi-buoy distributed beamforming problem into a centralized optimization problem of a single stacked matrix; thus, MU The received signal at that location can be rewritten as:

[0238] ;

[0239] in, To obtain the stacked channel matrix for mobile user u, the second-hop channel matrices of all buoys to that user are concatenated to form the equivalent channel matrix for the entire network, adapting to the centralized solution form of the stacked beamforming matrix; correspondingly, the rate It can be equivalently represented as:

[0240] ;

[0241] in ;

[0242] Furthermore, for stacked beamforming matrices ,make Indicates its first Each row block corresponds to a float. Beamforming, namely:

[0243] ;

[0244] in, Let b be the b-th row block of the stacked beamforming matrix, corresponding to the cooperative beamforming matrix of a single buoy b for user u. This matrix is ​​used to map the global constraints of the stacked structure back to the local power and energy constraints of a single buoy. Thus, the buoy power constraints and buoy energy constraints can be rewritten as follows:

[0245] ;

[0246] MU (Marine Users) The estimated signal vector at point (i.e., the receiver of the u-th marine user) is:

[0247] ;

[0248] MU The MSE matrix is:

[0249] ;

[0250] in, MU The receiving beamforming matrix, The mean square error matrix of the second-hop mobile user u represents the error covariance matrix between the estimated signal after beamforming processing at the user end and the original transmitted data signal. It is the core calculation parameter for the equivalent derivation of the second-hop WMMSE. The beamforming matrix for the second-hop mobile user u acts on the received signal at the user end. It extracts the useful data signal of the target user through spatial filtering and is one of the core variables for the iterative update of the second-hop WMMSE algorithm.

[0251] The rate based on MSE is:

[0252] ;

[0253] in, is an s-order identity matrix, corresponding to the identity matrix of the second-hop parallel data stream dimension, used to characterize the baseline form of the signal estimation result under ideal error-free transmission conditions. MU The MSE weight matrix, The equivalent reachability rate for the second hop based on mean square error is an expression obtained by equivalently transforming the original reachability rate of the second hop through weighted minimum mean square error. This can transform the non-convex beamforming optimization problem into a convex problem that can be solved iteratively.

[0254] For any given transmit beamforming matrix ,rate satisfy:

[0255] ;

[0256] in, Let be the MSE weight matrix for the second-hop mobile user u. This matrix is ​​the weight matrix in the second-hop weighted minimum mean square error framework. Iterative optimization of this matrix can achieve an equivalent mapping between the MSE-based equivalent rate and the original second-hop reachable rate. and The equality holds when updated as follows.

[0257] First, for fixed By order The optimal receiving beamforming matrix can be obtained. Therefore, we can conclude that:

[0258] ;

[0259] in, The optimal receive beamforming matrix for the second hop is derived by taking the derivative of the equivalent rate expression with respect to the receive beamforming and setting it to zero, under the condition of a fixed stacked transmit beamforming matrix. This optimal linear receive matrix minimizes the mean square error at the user end. Furthermore, for a fixed... and By order The optimal MSE weight matrix can be obtained. ,Right now:

[0260] ;

[0261] in, The optimal MSE weight matrix for the second hop is derived under the condition of fixed transmit and receive beamforming matrices. Its value is the inverse of the second-hop mean square error matrix. Substituting it into the matrix, the equivalent rate based on MSE is exactly equal to the original second-hop rate. and Substitution back, and equivalence.

[0262] For a given , about Since it is a concave function, subproblem three can be reconstructed into the following standard convex problem (i.e., the third subproblem):

[0263] ;

[0264] Step 105: Solve each subproblem alternately based on the initial set of optimization variables, and output the full set of optimization variables for this round.

[0265] It should be noted that, starting with the initial set of optimization variables as the initial values ​​for iteration, alternating optimization is performed sequentially according to the order of subproblem division. First, the buoy power split ratio and the buoy cooperative beamforming matrix are fixed, and the joint optimization subproblem of unmanned surface vessel trajectory and transmission beamforming is solved to obtain the updated time-slot position of the unmanned surface vessel and the transmission beamforming matrix of each user. Then, the updated trajectory and transmission beamforming parameters are substituted, and the power split ratio optimization subproblem is solved with the buoy cooperative beamforming matrix fixed to obtain the updated time-slot power split ratio of each buoy. Finally, the aforementioned two types of updated parameters are substituted to solve the buoy cooperative beamforming optimization subproblem to obtain the updated cooperative beamforming matrix of each buoy. All updated optimization parameters are integrated to form the full set of optimization variables for this round, which serves as the input benchmark for the next round of iteration.

[0266] Furthermore, step 105 may include the following sub-steps:

[0267] S51. Extract the buoy power division ratio and buoy cooperative beamforming matrix from the initial set of optimization variables as fixed parameters. Extract the unmanned surface vessel trajectory from the initial set of optimization variables as local iteration points. Use the trajectory-related weighted minimum mean square error method and the continuous convex approximation method to transform and solve the first subproblem. Output the updated unmanned surface vessel trajectory and the updated unmanned surface vessel transmission beamforming matrix.

[0268] S52. Using the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmission beamforming matrix, and the buoy cooperative beamforming matrix in the initial set of optimization variables as fixed parameters, the buoy power split ratio in the initial set of optimization variables is extracted as a local iteration point. The second subproblem is transformed into a convexity and solved using the concave-convex process and the first-order Taylor expansion method, and the updated buoy power split ratio is output.

[0269] S53. Using the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmit beamforming matrix, and the updated buoy power division ratio as fixed parameters, the weighted minimum mean square error method is used to transform and solve the third subproblem, and the updated buoy cooperative beamforming matrix is ​​output.

[0270] S54. Integrate the updated unmanned surface vessel trajectory, the updated unmanned surface vessel launch beamforming matrix, the updated buoy power split ratio, and the updated buoy cooperative beamforming matrix to generate the full set of optimization variables for this round.

[0271] It should be noted that during the single-round alternating optimization, the solution proceeds sequentially according to the three pre-set sub-problems. First, the per-slot power division ratio of each buoy and the cooperative beamforming matrix of each buoy for all mobile users are extracted from the initial set of optimization variables and kept at fixed values. At the same time, the initial per-slot position sequence of the unmanned surface vessel is extracted as the local iteration reference point for the continuous convex approximation. For the first sub-problem, the trajectory-related weighted minimum mean square error method is first used to introduce the linear receiving beamforming matrix and MSE weight matrix at the buoy end, and the non-convex first-hop reachable rate is equivalently transformed into a weighted mean square error expression. Then, the range-related non-convex fractional terms in the energy harvesting expression, the range constraint terms in the unmanned surface vessel obstacle avoidance constraints, and the steering angle are addressed. The adjacent displacement coupling terms in the beam are subjected to a first-order Taylor expansion at the current local iteration point. A corresponding convex approximation lower or upper bound is constructed to replace the original non-convex terms, transforming the original non-convex unmanned surface vessel (USV) trajectory and transmit beamforming joint optimization subproblem into a standard convex optimization problem. A convex optimization solution tool is called to complete the solution, outputting the updated USV time-slot trajectory sequence and the USV transmit beamforming matrix corresponding to each mobile user. After solving the first subproblem, the updated USV trajectory, USV transmit beamforming matrix, and buoy cooperative beamforming matrix in the initial optimization variable set are all treated as fixed parameters. The buoy power split ratio in the initial optimization variable set is extracted as the local iteration point of the concave-convex process. For the ... The second subproblem first decomposes the first-hop reachability rate into the form of two concave functions of the power split ratio minus one. Then, a first-order Taylor expansion is performed on the concave function used as the subtraction term at the local iteration point to obtain the global linear upper bound, thereby constructing the concave lower bound of the first-hop reachability rate. This transforms the original non-convex power split ratio optimization problem into a convex optimization problem and solves it, outputting the updated time-slot power split ratio of each buoy. After solving the second subproblem, the updated UAV trajectory, UAV beamforming matrix, and buoy power split ratio are all treated as fixed parameters. At this point, the total usable energy collected by each buoy during the first-hop transmission phase is a constant. For the third subproblem, a weighted minimum mean square error method is used to first... The second-hop channel matrix and cooperative beamforming matrix of the buoys are stacked to form an equivalent matrix for the entire network. Introducing the receiving beamforming matrix and MSE weight matrix of the marine user terminal, the non-convex second-hop reachable rate is equivalently transformed into a weighted mean square error expression. This transforms the original non-convex multi-buoy cooperative beamforming optimization problem into a convex optimization problem, which is then solved, outputting the updated cooperative beamforming matrices for each buoy for all mobile users. Finally, the updated UAV trajectory, updated UAV transmitting beamforming matrix, updated buoy power splitting ratio, and updated buoy cooperative beamforming matrix are integrated to form a complete set of full-scale optimization variables for this round, serving as input parameters for the next round of alternating optimization iterations. This phased alternating solution and corresponding convexification process effectively solves the problem of highly coupled multiple variables and strong non-convexity, making direct solution difficult in the original optimization problem.

[0272] Step 106: Using the rate-energy calculation model set, convergence determination is performed based on the full set of optimization variables in this round, and the optimal resource scheduling scheme is output.

[0273] It should be noted that the full set of optimization variables for this round is substituted into the rate-energy calculation model set to calculate the bottleneck transmission rate of each user in the first hop, the transmission rate of each user in the second hop, the energy collection amount per time slot of each buoy, and the corresponding end-to-end total delay. The obtained end-to-end total delay is compared with the total delay result of the previous iteration. If the relative difference is less than the preset convergence threshold, or the number of iterations reaches the preset maximum number of iterations, the iteration is determined to be converged. The UAV trajectory, UAV beamforming parameters, buoy power splitting ratio parameters, and buoy cooperative beamforming parameters corresponding to the current full set of optimization variables are integrated into the optimal resource scheduling scheme. If the convergence condition is not met, the full set of optimization variables for this round is used as the input benchmark for the next iteration, and the alternating optimization solution process continues until the convergence judgment condition is met.

[0274] Furthermore, step 106 may include the following sub-steps:

[0275] S61. Substitute all the optimization variables of this round into the rate energy calculation model set to calculate the end-to-end total transmission delay corresponding to the current iteration.

[0276] S62. Compare the total end-to-end transmission delay of the current iteration with the total end-to-end transmission delay of the previous iteration, and combine the convergence threshold to complete the convergence determination. If convergence is achieved, the set of all optimized variables in this round is determined as the optimal resource scheduling scheme.

[0277] It should be noted that when performing convergence determination, the performance index calculation for the current iteration is completed first. The time-slot-by-time trajectory of the unmanned surface vessel, the beamforming matrix of the unmanned surface vessel, and the power division ratio of the buoy in the time-slot-by-time buoy are substituted into the first-hop reachable rate calculation formula and the time-slot-by-time energy collection formula of the rate-energy calculation model set. The first-hop reachable rate of each user corresponding to each buoy is calculated in time slot. The minimum rate of a single user at all buoys is taken as the first-hop bottleneck transmission rate of that user. Starting from the first time slot, the bottleneck transmission data volume of all users is accumulated in time slot. When the accumulated data volume is not less than the size of the file to be transmitted for the first time, the number of first-hop transmission time slots T1 under the current iteration is obtained. Then, the buoy cooperative beamforming matrix in the full-scale optimization variables of this round is substituted into the second-hop reachable rate calculation formula. The second-hop transmission rate of each marine user is calculated in time slot. Similarly, the second-hop transmission time slots T2 under the current iteration are obtained by accumulating and solving. Combined with the single time slot duration τ, the end-to-end total transmission delay corresponding to the current iteration is calculated. After completing the current latency calculation, the end-to-end total transmission latency corresponding to the previous iteration is extracted from the historical iteration storage record as a comparison benchmark. The absolute difference between the two latency rounds is calculated and divided by the latency value of the previous round to obtain the relative rate of change of the objective function. Subsequently, a dual-condition convergence check is performed. On the one hand, the relative rate of change is compared with the preset convergence threshold. On the other hand, the number of iteration rounds completed is counted and compared with the preset maximum number of iterations. If the relative rate of change is less than or equal to the convergence threshold, or the number of iteration rounds reaches the maximum number of iterations, the iteration process is determined to have converged. At this time, the unmanned surface vessel trajectory configuration, unmanned surface vessel launch beamforming configuration, buoy power splitting ratio configuration, and buoy cooperative beamforming configuration included in the full set of optimization variables of this round are structured and integrated to determine the final optimal resource scheduling scheme. If the convergence condition is not met, the full set of optimization variables of this round and the corresponding latency results are stored in the historical iteration storage record as the input benchmark and local iteration point for the next round of alternating optimization. The sub-problem alternating solution and convergence judgment process continues to be executed in a loop.

[0278] It is worth mentioning that the present invention uses numerical simulation to evaluate the performance, and some parameter settings are as follows: , , , , , , , , , , , The buoy positions are respectively , and MU The positions are respectively , and .

[0279] To evaluate the performance of the present invention, the present invention considers the following benchmark schemes for comparison:

[0280] Straight trajectory reference scheme: The USV travels along a preset straight path at maximum speed Navigation. Under this fixed trajectory, the transceiver parameters are optimized only by solving the beamforming components of subproblems (P2), (P3), and (P1).

[0281] Static USV baseline scheme: The USV remains in its initial position throughout the entire mission cycle. Under this fixed position constraint, the remaining transceiver design parameters and power division parameters are optimized.

[0282] Fixed power division baseline scheme: The power division ratio of all buoys is fixed at a constant. Under this constraint, USV trajectory, USV beamforming, and buoy cooperative beamforming are jointly optimized.

[0283] Single-antenna reference schemes: USV, buoy, and MU All are equipped with a single antenna, that is Therefore, the beamforming matrix is ​​degenerated into a scalar variable and optimized using the proposed algorithm.

[0284] like Figure 3 As shown in Simulation 1, different numbers of buoy antennas are displayed. The convergence performance of the proposed scheme is shown below. It can be observed that the latency decreases rapidly in the initial iteration phase and gradually stabilizes. Furthermore, increasing the number of buoy antennas... Lower convergence latency is typically achievable because the additional spatial degrees of freedom simultaneously enhance both the reception capability in the USV-to-buoy transmission phase and the cooperative beamforming gain in the buoy-to-user transmission phase. However, larger... This also expands the optimization variable space, making beamforming design more complex and requiring more iterations to reach convergence. Nevertheless, all considered scenarios converge within a reasonable number of iterations, indicating that the proposed scheme has good stability and effectiveness.

[0285] like Figure 4As shown in Simulation 2, the optimized trajectory of the USV is depicted. It can be seen that the USV gradually navigates towards the buoy while satisfying mobility, obstacle avoidance, and steering angle constraints. This trajectory is the result of jointly considering radio information transmission and energy transfer in the first hop. By approaching the buoy, the USV significantly improves the channel quality of the USV-buoy link, thereby simultaneously increasing the achievable transmission rate and energy harvesting. The additional energy harvested further enhances the cooperative forwarding performance in the second hop. Therefore, the optimized trajectory effectively balances communication efficiency and energy sustainability, ultimately contributing to a reduction in total transmission latency.

[0286] like Figure 5 As shown in Simulation 3, the maximum transmit power of the USV is given. Impact on total transmission delay. As expected, it increases... This will reduce transmission latency across all scenarios. This performance improvement stems primarily from two aspects. First, the higher transmit power enhances the information transmission capability of the USV-to-buoy link. Second, it increases the energy that the buoy can collect, enabling a more aggressive cooperative beamforming strategy in the buoy-to-user transmission phase. Among all the considered scenarios, it offers the best overall performance. The system consistently achieves the lowest latency within its range. The significant performance difference compared to static USV and straight-track reference schemes underscores the importance of trajectory optimization in marine environments. Furthermore, the performance gain compared to fixed power splitting and single-antenna reference schemes validates the advantages of adaptive SWIPT resource allocation and multi-antenna cooperative transmission.

[0287] like Figure 6 As shown in Simulation 4, the total transmission latency varies with file size. The changing relationship. With... As the data increases, more transmission resources are needed to complete the information transmission process; therefore, the latency of all schemes shows a monotonically increasing trend. Nevertheless, its overall latency... The proposed solution consistently outperforms all benchmark solutions within its range. These results demonstrate that the proposed solution remains highly effective under varying service requirements and transmission loads.

[0288] The results above demonstrate that, compared with several benchmark schemes, the present invention can significantly reduce transmission latency and effectively leverage the combined advantages of mobility control, wireless power transfer, and cooperative beamforming.

[0289] For comparison of technological effectiveness, existing technologies can be referenced. Most current research on marine communication networks employs traditional fixed infrastructure or single-hop transmission architectures. Due to the vast coverage area of ​​the marine environment, the difficulty in infrastructure deployment, and the high maintenance costs, these technologies struggle to meet the continuous and stable data transmission needs of distant sea areas. In recent years, unmanned surface vessels (USVs) have gradually gained attention for their role in marine communication. The mobility of USVs can extend network coverage and improve communication quality. However, existing research largely focuses on USV trajectory optimization, resource allocation, or beamforming design, typically assuming that ocean buoys have sufficient energy supply, neglecting the energy constraints faced by buoy nodes during long-term operation.

[0290] To address the energy shortage problem of marine communication nodes, some studies have introduced Wireless Powered Communication (SWIPT) technology into wireless networks, enabling receiving nodes to collect wireless energy while receiving information. However, existing SWIPT research mainly focuses on terrestrial networks or integrated air-space-ground networks, with relatively few studies targeting the marine environment. Furthermore, most existing work only considers a single-layer communication architecture, failing to fully utilize the collaborative relationships between unmanned surface vessels and multiple buoys, making it difficult to simultaneously meet the demands for long-range coverage, energy replenishment, and efficient data transmission.

[0291] Furthermore, in marine collaborative communication scenarios, there are complex coupling relationships between the movement trajectory of unmanned surface vessels (USVs), beamforming, buoy energy harvesting, and collaborative transmission processes. Existing research typically employs a discrete design approach, optimizing only a subset of variables and neglecting the correlation between overall system performance and end-to-end transmission latency. This results in low communication resource utilization efficiency, high transmission latency, and an inability to fully leverage the advantages of USV maneuverability and wireless power transfer technology.

[0292] As mentioned above, existing research on unmanned surface vessel (USV)-assisted marine communication largely focuses on communication performance optimization, such as trajectory design, resource allocation, and beamforming. It typically assumes that buoys have sufficient energy supply and rarely considers communication cooperation mechanisms under energy-constrained conditions. Furthermore, current SWIPT-related research primarily focuses on terrestrial networks, with relatively limited research on the collaborative work of USVs and buoys in marine environments. Especially in hierarchical communication networks composed of USVs, multiple buoys, and marine users, how to fully utilize wireless power transmission, cooperative beamforming, and the maneuverability of USVs to achieve low-latency, high-energy-efficiency data transmission remains a crucial problem to be solved.

[0293] Therefore, this invention provides a resource scheduling method for an unmanned surface vessel (USV)-assisted marine communication system based on wireless energy transmission. By jointly optimizing the USV's motion trajectory, wireless energy transmission, and cooperative communication strategies, it reduces end-to-end data transmission latency and improves the coverage, energy utilization efficiency, and service quality of the marine communication network. This method has significant theoretical and engineering application value.

[0294] This invention relates to the field of communication technology, specifically to a marine hierarchical communication method based on Wireless Powered Communication (SWIPT). This method targets marine communication networks composed of unmanned surface vessels (USVs), ocean buoys, and marine users. By jointly optimizing key parameters such as USV trajectory, USV transmit beamforming, buoy power allocation coefficients, and buoy cooperative beamforming, an end-to-end transmission delay minimization model is constructed. The model is solved using weighted minimum mean square error (WMMSE), continuous convex approximation (SCA), and alternating optimization algorithms. Under the conditions of satisfying energy causality constraints, transmit power constraints, and navigation safety constraints, this method effectively reduces transmission delay and significantly improves communication performance in marine communication networks.

[0295] Specifically, this invention constructs a hierarchical communication network architecture consisting of a multi-antenna unmanned surface vessel (USV), ocean buoys, and ocean users. In the first hop, the USV simultaneously transmits information and wireless energy to multiple buoys; in the second hop, the buoys collaboratively transmit data to the ocean users using the collected energy. Based on this network architecture, an end-to-end transmission delay minimization model is established, and the functional relationships between system parameters such as USV trajectory, USV transmit beamforming, buoy power allocation coefficient, and buoy cooperative beamforming and transmission delay are derived.

[0296] Furthermore, this invention employs a weighted minimum mean square error method, a continuous convex approximation method, and an alternating optimization algorithm to jointly optimize the unmanned surface vessel's navigation trajectory, transmit beamforming, power allocation coefficient, and buoy cooperative beamforming. Under the conditions of satisfying transmit power constraints, energy causality constraints, and navigation safety constraints, it improves wireless energy utilization efficiency and cooperative transmission capabilities, effectively reduces end-to-end data transmission latency, and thus enhances the coverage performance, transmission efficiency, and service quality of the marine communication network.

[0297] In summary, the key point of this invention is that, in a marine communication network, a hierarchical cooperative communication structure is constructed using unmanned surface vessels (USVs), marine buoys, and marine users. The USVs simultaneously transmit information and wireless energy to multiple marine buoys. The buoys collect energy while receiving information and use the collected energy to cooperatively forward data, thereby achieving low-latency data transmission in energy-constrained marine communication networks.

[0298] Furthermore, this invention models the transmission delay minimization problem by establishing the relationship between system parameters such as the unmanned surface vessel (USV) trajectory, USV transmit beamforming, buoy power segmentation factor, and buoy cooperative beamforming and end-to-end transmission delay. By jointly optimizing the USV trajectory, USV transmit beamforming, buoy power segmentation factor, and buoy cooperative beamforming, the relationship between information transmission, wireless energy harvesting, and buoy cooperative forwarding is coordinated. Under the conditions of satisfying power constraints, energy causality constraints, and USV dynamics constraints, the overall system transmission delay is effectively reduced, improving the transmission efficiency and sustainable service capability of the offshore communication network.

[0299] Compared to existing technologies, this invention utilizes unmanned surface vessels (USVs) to construct a hierarchical marine communication network supporting simultaneous wireless information and energy transmission. By transmitting information and wireless energy simultaneously to buoys at sea via USVs, energy-constrained buoys can collaboratively serve maritime users using the collected energy. Furthermore, this invention coordinates the relationship between information transmission, energy harvesting, and collaborative forwarding by jointly optimizing USV navigation trajectories, USV transmit beamforming, buoy power segmentation factors, and buoy collaborative beamforming. This effectively reduces end-to-end transmission latency and improves the transmission efficiency and sustainable service capabilities of the offshore communication network.

[0300] In this embodiment of the invention, a resource scheduling method for an unmanned surface vessel (USV)-assisted marine communication system based on wireless power carrying is provided. The method acquires basic configuration parameters of the marine communication scenario and constructs a physical model of a layered marine communication system based on these parameters. It then performs rate and energy derivation modeling for two-hop transmission using the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario, outputting a set of rate and energy calculation models. Finally, based on the set of rate and energy calculation models, the USV navigation constraints in the physical model of the layered marine communication system, and the basic configuration parameters of the marine communication scenario, an optimization problem is modeled, outputting the minimized end-to-end latency. The original optimization problem is decomposed into subproblems to minimize end-to-end latency, outputting an initial set of optimization variables and multiple subproblems. Each subproblem is solved alternately based on the initial set of optimization variables, outputting the full set of optimization variables for this round. A rate-energy calculation model set is used to determine convergence based on the full set of optimization variables for this round, outputting the optimal resource scheduling scheme. Based on the above scheme, this invention, by acquiring basic configuration parameters of the marine communication scenario and constructing a physical model of the marine hierarchical communication system, can complete the unified modeling of node spatial attributes, time-domain time slot division, double-hop channel characteristics, and unmanned surface vessel navigation constraints, providing a complete physical constraint and computational foundation for subsequent joint optimization. Based on this, [the following is discussed]. The physical model uses the first and second hop channel matrices to derive and model the rate and energy of two-hop transmission, forming a set of rate and energy calculation models that include calculations of the two-hop achievable rate and buoy time-slot energy harvesting. This allows the buoy energy supply process and two-hop transmission performance to be incorporated into a unified calculation framework. Subsequently, combining the set of rate and energy calculation models, unmanned surface vessel (USV) navigation constraints, and basic configuration parameters, an optimization problem is modeled to construct a primary optimization problem with the goal of minimizing end-to-end latency. This approach incorporates energy causal constraints, transmit power constraints, and navigation constraints into the optimization constraint system, anchoring the core objective of reducing transmission latency at the problem definition level. The initial optimization problem is then obtained by decomposing the primary optimization problem into subproblems. The system employs a set of variables and multiple sub-problems, and alternately solves each sub-problem based on the initial set of optimized variables to obtain the full set of optimized variables for this round. This phased solution approach overcomes the challenges of solving multi-variable coupling problems, efficiently achieving coordinated optimization of unmanned surface vessel trajectory, transmission beamforming, buoy power segmentation, and cooperative beamforming. Finally, relying on the rate-energy calculation model set combined with the full set of optimized variables for this round, it completes convergence determination and outputs the optimal resource scheduling scheme. Under the premise of satisfying buoy energy constraints and navigation constraints, it can accurately match the rate requirements of two-hop transmission, fully release the performance gains of multi-buoy cooperative forwarding, effectively reduce the total end-to-end transmission latency of the system, and thus improve the overall transmission efficiency of the offshore communication network.

[0301] Please see Figure 7 , Figure 7This is a structural block diagram of a resource scheduling system for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, provided in Embodiment 2 of the present invention.

[0302] This invention provides a resource scheduling system for an unmanned surface vessel (USV)-assisted marine communication system based on wireless power carrying, comprising:

[0303] The acquisition module 701 is used to acquire basic configuration parameters of the marine communication scenario and construct a physical model of the marine layered communication system based on the basic configuration parameters of the marine communication scenario.

[0304] The first modeling module 702 is used to perform rate and energy derivation modeling for the first hop channel matrix, the second hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine layered communication system, and output a set of rate and energy calculation models.

[0305] The second modeling module 703 is used to model the optimization problem based on the set of rate energy calculation models, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, and output the original optimization problem of minimizing end-to-end latency.

[0306] Decomposition module 704 is used to decompose the original optimization problem of minimizing end-to-end delay into subproblems and output the initial set of optimization variables and multiple subproblems.

[0307] Solver module 705 is used to solve each subproblem alternately based on the initial set of optimization variables and output the full set of optimization variables for this round;

[0308] The decision module 706 is used to determine the convergence of the rate-energy calculation model set based on the full set of optimization variables in this round, and output the optimal resource scheduling scheme.

[0309] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0310] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, as described in the above embodiments.

[0311] This invention also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps of the resource scheduling method for the wireless-powered unmanned surface vessel-assisted marine communication system as described in the above embodiments.

[0312] This invention also provides a computer program product, including a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the resource scheduling method for a wireless-powered unmanned surface vessel-assisted marine communication system as described in the above embodiments.

[0313] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0314] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0315] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0316] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0317] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying, characterized in that, include: Obtain the basic configuration parameters of the marine communication scenario, and construct a physical model of the marine hierarchical communication system based on the basic configuration parameters of the marine communication scenario; For the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine hierarchical communication system, the rate and energy of two-hop transmission are derived and modeled, and a set of rate and energy calculation models is output. Based on the set of rate energy calculation models, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, an optimization problem is modeled, and the original optimization problem of minimizing end-to-end latency is output. The original optimization problem of minimizing end-to-end delay is decomposed into subproblems, and the initial set of optimization variables and multiple subproblems are output. Based on the initial set of optimization variables, each of the sub-problems is solved alternately, and the full set of optimization variables for this round is output. The rate-energy calculation model set is used to determine convergence based on the full set of optimization variables in this round, and the optimal resource scheduling scheme is output.

2. The resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying according to claim 1, characterized in that, The step of constructing a physical model of a marine hierarchical communication system based on the basic configuration parameters of the marine communication scenario includes: A two-dimensional Cartesian coordinate system is established, and the initial position of the unmanned surface vessel, the fixed position of the buoy, the position of the marine user, the position and safety radius of the obstacle, the effective antenna height of the node and the number of antennas in the basic configuration parameters of the marine communication scenario are mapped one by one to the two-dimensional Cartesian coordinate system to complete the assignment, so as to obtain a set of spatial parameters of nodes and obstacles with coordinate labels. The total runtime in the basic configuration parameters of the marine communication scenario is evenly divided using the total number of time slots in the basic configuration parameters of the marine communication scenario to obtain the time slot division result. The node spacing is calculated based on the node coordinates in the set of spatial parameters of the nodes and obstacles with coordinate labels. The antenna array response matrix is ​​obtained by matrix derivation based on the node spacing, the effective antenna height of the nodes in the coordinate-annotated set of spatial parameters of nodes and obstacles, and the carrier wavelength in the basic configuration parameters of the marine communication scenario. The first hop channel matrix and the second hop channel matrix are calculated based on the node spacing, the effective antenna height of the nodes in the coordinate-annotated node and obstacle spatial parameter set, the carrier wavelength in the basic configuration parameters of the marine communication scenario, and the antenna array response matrix. Based on the time slot division results and the ocean current speed and maximum propulsion speed, obstacle position and safe radius, and maximum turning angle in the basic configuration parameters of the marine communication scenario, navigation constraints for unmanned surface vessels are constructed. A physical model of a marine hierarchical communication system is constructed based on the set of coordinate-labeled node and obstacle spatial parameters, the time slot division results, the first hop channel matrix, the second hop channel matrix, and the unmanned surface vessel navigation constraints.

3. The resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying according to claim 2, characterized in that, The rate-energy calculation model set includes the first-hop reachable rate calculation formula, the buoy time-slot energy harvesting calculation formula, and the second-hop reachable rate calculation formula; the rate and energy derivation modeling of two-hop transmission is performed on the first-hop channel matrix, the second-hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine layered communication system, and the output rate-energy calculation model set includes: Introducing variables to be optimized, including the unmanned surface vessel's transmit beamforming matrix, the buoy power split ratio, and the buoy cooperative beamforming matrix; Based on the first hop channel matrix, the unmanned surface vessel transmit beamforming matrix, the buoy power division ratio, and the information decoding circuit noise power and additive white Gaussian noise in the basic configuration parameters of the marine communication scenario, the first hop information transmission rate is derived, and the formula for calculating the first hop achievable rate is output. Based on the first hop channel matrix, the unmanned surface vessel transmit beamforming matrix, the buoy power division ratio, and combined with the energy conversion efficiency, additive white Gaussian noise, and time slot division results in the basic configuration parameters of the marine communication scenario and the physical model of the marine hierarchical communication system, the energy harvesting amount is derived, and the formula for calculating the energy harvesting amount of the buoy per time slot is output. Based on the second-hop channel matrix, the buoy cooperative beamforming matrix, and the additive white Gaussian noise power in the basic configuration parameters of the marine communication scenario, the second-hop cooperative transmission rate is derived, and the formula for calculating the second-hop achievable rate is output.

4. The resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying according to claim 3, characterized in that, The optimization problem is modeled based on the set of rate-energy calculation models, the unmanned surface vessel navigation constraints in the physical model of the marine layered communication system, and the basic configuration parameters of the marine communication scenario. The output is the original optimization problem of minimizing end-to-end latency, including: Based on the first hop reachability calculation formula and the second hop reachability calculation formula, combined with the file size to be transmitted in the basic configuration parameters of the marine communication scenario, the end-to-end total latency is derived, and the optimization objective function is output. Based on the formula for calculating the energy harvested per time slot of the buoy, and combined with the calculation relationship of the total transmission energy consumption of the second hop buoy derived from the formula for calculating the second hop reachability rate and the buoy cooperative beamforming matrix, the energy causal constraint is derived, and the energy causal constraint expression is output. The unmanned surface vessel (USV) navigation constraints are extracted from the physical model of the marine hierarchical communication system. Combined with the USV transmission power constraints, buoy transmission power constraints derived from the maximum power threshold in the basic configuration parameters of the marine communication scenario, and the energy causal constraint expression, all constraints are integrated to output an optimized constraint set. Based on the objective function and the set of constraints, a primal optimization problem for minimizing end-to-end latency is constructed.

5. The resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying according to claim 1, characterized in that, The multiple sub-problems include a first sub-problem, a second sub-problem, and a third sub-problem; the process of alternately solving each sub-problem based on the initial set of optimization variables to output the full set of optimization variables for this round includes: The buoy power split ratio and buoy cooperative beamforming matrix are extracted as fixed parameters from the initial set of optimization variables. The unmanned surface vessel trajectory is extracted as a local iteration point from the initial set of optimization variables. The trajectory-related weighted minimum mean square error method and the continuous convex approximation method are used to transform and solve the first subproblem, and the updated unmanned surface vessel trajectory and the updated unmanned surface vessel transmission beamforming matrix are output. Using the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmission beamforming matrix, and the buoy cooperative beamforming matrix in the initial set of optimization variables as fixed parameters, the buoy power split ratio in the initial set of optimization variables is extracted as a local iteration point. The second subproblem is transformed into a convexity and solved using a concave-convex process and a first-order Taylor expansion method, and the updated buoy power split ratio is output. Using the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmit beamforming matrix, and the updated buoy power division ratio as fixed parameters, the weighted minimum mean square error method is used to transform and solve the third subproblem, and the updated buoy cooperative beamforming matrix is ​​output. By integrating the updated unmanned surface vessel trajectory, the updated unmanned surface vessel transmit beamforming matrix, the updated buoy power split ratio, and the updated buoy cooperative beamforming matrix, a full set of optimization variables for this round is generated.

6. The resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying according to claim 1, characterized in that, The process of using the rate-energy calculation model set to determine convergence based on the full set of optimization variables in this round, and outputting the optimal resource scheduling scheme, includes: Substitute the full optimization variables of this round into the set of rate energy calculation models to calculate the end-to-end total transmission delay corresponding to the current iteration; By comparing the total end-to-end transmission latency of the current iteration with that of the previous iteration, and combining this with the convergence threshold, a convergence determination is made. If convergence is achieved, the set of all optimized variables for this round is determined as the optimal resource scheduling scheme.

7. A resource scheduling system for an unmanned surface vessel (USV)-assisted marine communication system based on wireless power carrying, characterized in that, include: The acquisition module is used to acquire basic configuration parameters of the marine communication scenario and construct a physical model of the marine hierarchical communication system based on the basic configuration parameters of the marine communication scenario. The first modeling module is used to perform rate and energy derivation modeling for the first hop channel matrix, the second hop channel matrix, and the basic configuration parameters of the marine communication scenario in the physical model of the marine layered communication system, and output a set of rate and energy calculation models. The second modeling module is used to model the optimization problem based on the set of rate energy calculation models, the unmanned surface vessel navigation constraints in the physical model of the marine hierarchical communication system, and the basic configuration parameters of the marine communication scenario, and output the original optimization problem of minimizing end-to-end latency. The decomposition module is used to decompose the original optimization problem of minimizing end-to-end latency into subproblems and output an initial set of optimization variables and multiple subproblems. The solution module is used to solve each of the sub-problems alternately based on the initial set of optimization variables, and output the full set of optimization variables for this round. The determination module is used to perform convergence determination based on the set of rate-energy calculation models and the set of full-scale optimization variables in this round, and output the optimal resource scheduling scheme.

8. An electronic device, characterized in that, The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the resource scheduling method for an unmanned surface vessel-assisted marine communication system based on wireless power carrying as described in any one of claims 1-6.

Images