Unmanned aerial vehicle assisted power communication user association and spectrum allocation method and device

Abstract

The invention discloses an unmanned aerial vehicle assisted power communication user association and spectrum allocation method and device, and the method comprises the steps: taking maximization of the throughput of a system as an optimization target, and building a joint optimization problem of base station user association, sub-channel allocation and power allocation in the system; considering user requirements, base station load, intra-layer interference and inter-layer interference, and establishing corresponding constraints for user rate, base station capacity, power and signal interference intensity; and solving a joint optimization problem under corresponding constraints, specifically, solving a user association and sub-channel allocation optimization problem by using a matching algorithm, and then solving a power allocation problem by using a Lagrange duality method. According to the method, the user communication rate can be effectively increased, the system capacity of the heterogeneous network is improved, and the whole heterogeneous network is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and in particular relates to a method and device for unmanned aerial vehicle-assisted power communication user association and spectrum allocation. Background Art

[0002] In recent years, the emergence of smart grids has broken through the traditional power supply and consumption model. Smart grids use advanced information, control and communication technologies to provide technical support for the safe and efficient use of electricity, while reducing energy consumption and greenhouse gas emissions. The realization of these functions is inseparable from an effective, reliable and powerful communication system.

[0003] The smart power communication network covers power plants, substations, distributed power sources, electric meters and other facilities, supports information exchange between the distribution side and the upper control center, and supports services such as distribution operation monitoring and electricity consumption information collection. Therefore, the smart power communication network is directly oriented to power customers and society, and has the characteristics of wide coverage, large number of devices and complex business types.

[0004] In response to the complex and diverse communication needs, multiple communication networks complement each other to form heterogeneous networks to achieve high-speed and seamless coverage of smart power communication networks. In order to coordinate multi-user access and maximize system throughput, heterogeneous networks must provide flexible resource allocation, intelligent network management, and fine-grained traffic control. At the same time, the deployment of wireless terminals in smart power communication networks presents diversity and complexity in spatial locations. In order to meet the power needs of users as much as possible, some terminal devices will be deployed in areas with dense buildings and a large number of users, while others may be distributed in remote areas with complex terrain, scarce computing resources, and poor communication conditions. In addition, in the smart power communication scenario, with the continuous development of the Internet of Things and machine communication, there are a large number of users accessing at the same time in power communication. This puts strict requirements on the network planning and network access selection mechanism of the power communication network. For the above challenges, drones can provide auxiliary communication services for remote areas, high-density communication areas, and natural disaster areas because of their low cost, high mobility, and adaptability. Therefore, there is an urgent need for a drone-assisted power communication system.

[0005] In the UAV-assisted power communication network, low-power UAV base stations are deployed in the area covered by the macro base station to form a heterogeneous network. The macro base station and the UAV base station reuse the same spectrum resources, which can provide more spectrum resources for users to improve the system capacity. Users establish connections with the macro base station or the UAV base station through single connection or multi-connection technology to communicate. The traditional network access selection mechanism considers simply establishing a connection between users and the base station that can provide the maximum signal-to-noise ratio. In heterogeneous networks, since the differences between base stations are ignored, this algorithm is very likely to cause load imbalance and low resource utilization, and cannot meet the needs of smart power communication. Reference 1 (User-network association algorithm based on matching game in massive MIMO heterogeneous networks [J]) studies the user association algorithm based on one-to-many matching in heterogeneous networks, and obtains the user association strategy that maximizes the system energy efficiency through matching game while ensuring the user QoS (Quality of Service). However, the consideration of base station power control is ignored, and the adjustment of base station power after user association will affect the utility function in the matching game and further affect the optimal solution of user association. Reference 2 (Cooperative Optimization of User Association and Base Station Power in Heterogeneous Cellular Networks [J]) studies the collaborative optimization scheme of user association mechanism and base station power control in heterogeneous networks, and uses constraint relaxation and convex optimization iteration to obtain the optimal solution. However, slack variables may lead to loose upper bound solutions, resulting in the upper bound solution obtained in the end not being the optimal solution to the original problem. The patent application with application number CN202110526421.8 studies the user association joint power allocation strategy based on deep learning in heterogeneous networks, and obtains user association and power allocation strategies as data sets using the Hungarian algorithm and water injection algorithm, respectively, and then trains a deep neural network based on the data set to achieve online decision-making. However, the training of deep neural network models requires a large amount of data, and there is a problem of model generalization. Summary of the invention

[0006] In view of the above, the purpose of the present invention is to provide a drone-assisted power communication user association and spectrum allocation method and device, which can effectively increase the user communication rate, improve the capacity of the heterogeneous network system, and optimize the heterogeneous network as a whole.

[0007] To achieve the above-mentioned purpose of the invention, an embodiment provides a method for associating users and allocating spectrum for UAV-assisted power communication. The method is applied to a UAV-assisted power communication heterogeneous network system consisting of several UAV base stations, one macro base station, and several randomly distributed users, and includes the following steps:

[0008] Taking maximizing system throughput as the optimization goal, a joint optimization problem of base station user association, subchannel allocation and power allocation in the system is established;

[0009] Considering user demand, base station load, intra-layer interference and inter-layer interference, corresponding constraints are established on user rate, base station capacity, power and signal interference intensity;

[0010] The joint optimization problem is solved under the corresponding constraints, which includes using a matching algorithm to solve the user association and subchannel allocation optimization problems, and then using the Lagrangian dual method to solve the power allocation problem.

[0011] Preferably, taking maximizing system throughput as the optimization goal, a joint optimization problem of base station user association, subchannel allocation and power allocation in the system is established, including:

[0012] According to Shannon's formula, when user k establishes a connection with drone base station m on subchannel n, its communication rate R k,m,n It is expressed as:

[0013] R k,m,n =B n log2(1+γ k,m,n ),

[0014] Among them, B n represents the bandwidth of subchannel n, γ k,m,n It represents the signal-to-interference-to-noise ratio of the signal received by user k, and its calculation formula is as follows:

[0015]

[0016] Among them, p k,n represents the signal power of drone base station user k on subchannel n, p j,n represents the signal power of drone base station user j on subchannel n, p l,n represents the signal power of macro base station user l on subchannel n, h k,m,n 、h j,m,n 、h l,m,n denote the channel gains from drone base station user k, drone base station user j and macro base station user l to drone base station m, σ 2 represents the noise power, represents the set of drone base station users communicating on subchannel n;

[0017] Define user associated variable x k,m represents the connection relationship between drone base station m and user k. When user k establishes a connection with base station m, x k,m =1, otherwise, x k,m =0; define subchannel allocation variable y k,m,n represents the subchannel allocation of drone base station m. When subchannel n of drone base station m is occupied by user k, y k,m,n =1, otherwise, y k,m,n=0; use As a collection of user-associated variables, As a collection of subchannel allocation variables, As a collection of user power allocation variables;

[0018] Based on the above definition, considering factors such as base station user association, subchannel allocation and user signal transmission power, and taking maximizing system throughput as the optimization goal, the following joint optimization problem is established:

[0019]

[0020] in, They represent the set of UAV base stations, the set of users, and the set of orthogonal sub-channels respectively.

[0021] Preferably, considering user demand, base station load, intra-layer interference and inter-layer interference, corresponding constraints are established on user rate, base station capacity, power and signal interference intensity, including:

[0022] When the UAV-assisted power communication system is applied to a single connection scenario, that is, when a user establishes a connection and communicates with a macro base station or a UAV base station through a single connection technology, a total of 9 corresponding constraints are established, namely:

[0023]

[0024]

[0025] Among them, constraint (a1) ensures that each user k can obtain a communication rate that meets its minimum requirements Constraint (b1) indicates that the signal interference of drone base station m to macro base station on subchannel n should be controlled within the interference threshold Inside, h k,0,n represents the channel gain from drone base station user k to macro base station 0; constraint (c1) indicates that a user can only connect to one base station; constraint (d1) indicates that the number of users served by drone base station m cannot exceed the maximum number of users it can serve, q m To ensure load balancing; Constraints (e1) and (f1) indicate that for each drone base station, each subchannel can be assigned to at most one user, and each user can be assigned to at most one subchannel; Constraints (g1) and (h1) indicate that the variable x k,m and variable y k,m,n is a binary variable; constraint (i1) indicates that the signal transmission power of each user should be controlled within the maximum signal transmission power and minimum transmission power A certain range of Inside.

[0026] Preferably, considering user demand, base station load, intra-layer interference and inter-layer interference, corresponding constraints are established on user rate, base station capacity, power and signal interference intensity, including:

[0027] When the UAV-assisted power communication system is applied to a multi-connection scenario, that is, when the user establishes connections and communicates with multiple base stations through multi-connection technology, a total of 9 corresponding constraints are established, and only the third constraint (c1) is modified to:

[0028]

[0029] The remaining eight constraints are the same as those in the single-connection scenario (a1)-(b1), (d1)-(i1). Constraint (c2) indicates that each user k can have at most k Base station connection.

[0030] Preferably, solving the joint optimization problem under corresponding constraints specifically includes using a matching algorithm to solve the user association and subchannel allocation optimization problems, and then using a Lagrangian dual method to solve the power allocation problem, including:

[0031] Step 1: Initialize given conditions and constraint information, including the number and location of users, the number and location of base stations, the minimum user rate, interference threshold, base station capacity, power range, subchannel bandwidth and signal noise. At the same time, initialize the user association variable set X, the subchannel allocation variable set Y and the user power allocation variable set P;

[0032] Step 2: Under the constraints, according to the subchannel allocation variable set Y and the user power allocation variable set P, the user association problem is solved by the matching algorithm to obtain X;

[0033] Step 3: Under the constraints, according to X obtained in step 2 and the user power allocation variable set P, the sub-channel allocation problem is solved by a matching algorithm to obtain Y;

[0034] Step 4: Under the constraints, according to X and Y obtained in steps 2 and 3, the power allocation problem is solved by Lagrangian dual method to obtain P;

[0035] Step 5: Repeat steps 2, 3, and 4 until the maximum number of iterations is reached or convergence occurs, and the final base station user association, subchannel allocation, and power allocation results are obtained.

[0036] Preferably, step 2 comprises:

[0037] The user side uses the average signal-to-interference-noise ratio of all available subchannels of the drone base station m as the utility function for evaluating the base station m. Right now:

[0038]

[0039] The base station side uses the received signal strength of user k as the utility function to evaluate user k. Right now;

[0040]

[0041] Among them, γ k,m,n represents the signal-to-interference-noise ratio of the signal received by user k, p k,n represents the signal power of drone base station user k on subchannel n, h k,m,n represents the channel gain from drone base station user k to drone base station m, S m represents the set of available subchannels of base station m;

[0042] According to the user's utility function The user builds a preference list for drone base stations and sends a preference to the one with the largest utility function or b k The drone base station sends a connection request, and the drone base station responds according to the utility function on the base station side. and its own remaining capacity to decide whether to accept the user's connection request;

[0043] If the user is rejected, the drone base station is removed from the preference list and the user continues to try to find the one with the largest utility function in the preference list or b k The drone base station sends a connection request, and so on, until each user is matched with a drone base station that accepts it and establishes a connection to build the user association variable set X;

[0044] When the UAV-assisted power communication system is applied to a single-connection scenario, a connection request is sent to a UAV base station. When the UAV-assisted power communication system is applied to a multi-connection scenario, a connection request is sent to b k A drone base station sends a connection request.

[0045] Preferably, step 3 comprises:

[0046] For each drone base station, it only needs to allocate subchannels to users who have established connections with it. It does not need to consider the user connections and subchannel allocations of other drone base stations, and regards the subchannel allocation problem as an independent subproblem. Based on this, for drone base station m, the user side uses the communication rate R on subchannel n k,m,n As the utility function for evaluating this subchannel Right now:

[0047]

[0048] The subchannel side uses the weighted result of the communication rate of user k on subchannel n and the interference caused by the user to other base stations on subchannel n as the utility function for evaluating user k. Right now:

[0049]

[0050] in, represents the minimum required communication rate, p k,n represents the signal power of drone base station user k on subchannel n, h k,q,n represents the channel gain from drone base station user k to drone base station q, h k,0,n represents the channel gain from the drone base station user k to the macro base station 0, α and β represent the weighting coefficients respectively;

[0051] According to the user-side utility function The user builds a preference list for subchannels and sends a request to the base station for a subchannel with the largest utility function. The base station then selects the subchannel based on the utility function on the subchannel side. and channel occupancy to determine whether to allocate a sub-channel to the user;

[0052] If the user is rejected, the subchannel is deleted from the preference list, and the connection request is continued to be sent to the subchannel with the largest utility function in the preference list of the base station subchannel, and so on, until a subchannel that accepts itself is matched for each user and a connection is established to construct a subchannel allocation variable set Y.

[0053] Preferably, step 4 comprises:

[0054] For different orthogonal subcarriers, since different subcarriers will not interfere with each other, the power allocation problem of user-base station pairs using different subchannels is considered as an independent subproblem;

[0055] For the power allocation problem of user-base station pairs with different sub-channels, Lagrange multipliers are introduced to construct the Lagrange equation, and then the KKT condition is used to obtain the optimal solution of power allocation, and the user power allocation variable set P is obtained.

[0056] To achieve the above-mentioned purpose of the invention, an embodiment also provides a drone-assisted power communication user association and spectrum allocation device, including a memory and one or more processors, wherein the memory stores executable code, and the device is applied to a drone-assisted power communication heterogeneous network system composed of several drone base stations, a macro base station, and several randomly distributed users. When the one or more processors execute the executable code, they are used to implement the above-mentioned drone-assisted power communication user association and spectrum allocation method.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention comprehensively considers the requirements of maximizing system throughput, ensuring user communication quality, ensuring load balancing of base stations, and implementing signal interference control in the UAV-assisted power communication heterogeneous network system, jointly optimizes user association, subchannel allocation, and power allocation problems in heterogeneous networks, and solves the problems with low-complexity algorithms. The proposed UAV-assisted power communication heterogeneous network helps promote the power communication system to provide high-speed and seamless coverage for users in remote areas, high-density communications, and natural disaster areas. The designed UAV-assisted power communication user association and spectrum allocation method can coordinate various requirements in heterogeneous networks, optimize the resource allocation of heterogeneous networks as a whole, and improve system capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 is a flow chart of a method for UAV-assisted power communication user association and spectrum allocation provided in an embodiment;

[0061] Figure 2 is a schematic diagram of the structure of a UAV-assisted power communication heterogeneous network system provided in an embodiment;

[0062] Figure 3 It is a flow chart for solving the joint optimization problem provided by the embodiment. DETAILED DESCRIPTION

[0063] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation methods described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0064] The inventive concept of the present invention is: in a UAV-assisted power communication heterogeneous network system, while maximizing the system throughput, it is necessary to ensure the communication quality of users, and it is also necessary to ensure the load balancing of the base station, and it is necessary to control the signal interference in the system in real time. To achieve the above requirements, it is necessary to comprehensively consider the user association, subchannel allocation and power allocation problems in heterogeneous networks. The current research work mainly focuses on research in a certain aspect, without optimizing the heterogeneous network as a whole, and there are respective shortcomings. In response to this technical problem, an embodiment of the present invention provides a UAV-assisted power communication user association and spectrum allocation scheme, which is applied to a scenario in which there is a macro base station, several UAV base stations and several randomly distributed users in a UAV-assisted power communication heterogeneous network system. Among them, by arranging UAV base stations in coverage blind spots or hot spots, the business volume of the macro base station can be shared, and the communication needs of blind spots or areas with large traffic can be met, thereby increasing the system capacity and improving the network quality.

[0065] In the scene, such as Figure 2 As shown in , the macro base station coexists with M drone base stations, and their coverage areas overlap, thus providing high-speed and seamless communication services for K randomly distributed users. For ease of representation, we use Represents a collection of drone base stations, using Represents the set of users. In order to improve spectrum utilization, the system uses OFDMA (Orthogonal Frequency Division Multiple Access) communication. The macro base station and the drone base station share a set of orthogonal sub-channels for communication. This set of orthogonal sub-channels is represented by the set Indicates that the user establishes a connection and communicates with the macro base station or the drone base station through a single connection or multiple connection technology. When a single connection scenario with a single connection is adopted, the user establishes a connection and communicates with the macro base station or a drone base station through a single connection technology.

[0066] In heterogeneous networks, due to the low power and small coverage of small base stations, the connection between user equipment and drone base stations is prone to communication interruption. Through multi-connection technology, user equipment can establish connections with multiple base stations. The coverage of multiple base stations can ensure the reliability of network connections and ensure high-speed data transmission. In a multi-connection scenario, user equipment maintains connections with multiple access base stations at the same time, which can effectively improve the stability of communication, increase network reliability and reduce network latency.

[0067] Based on this, Figure 1 As shown, the drone-assisted power communication user association and spectrum allocation method provided in the embodiment includes the following steps:

[0068] S1, with maximizing system throughput as the optimization goal, establishes the joint optimization problem of base station user association, subchannel allocation and power allocation in the system.

[0069] In the embodiment, a joint optimization problem of base station user association, subchannel allocation and power allocation in the system is established, and the specific process is as follows:

[0070] According to Shannon's formula, when user k establishes a connection with drone base station m on subchannel n, its communication rate R k,m,n It is expressed as:

[0071] R k,m,n =B n log2(1+γ k,m,n ),

[0072] Among them, B n represents the bandwidth of subchannel n, γ k,m,n The SINR (Signal to Interference Plus Noise) of the signal received by user k is calculated as follows:

[0073]

[0074] Among them, p k,n represents the signal power of drone base station user k on subchannel n, p j,n represents the signal power of drone base station user j on subchannel n, p l,n represents the signal power of macro base station user l on subchannel n, h k,m,n 、h j,m,n 、h l,m,n denote the channel gains from drone base station user k, drone base station user j and macro base station user l to drone base station m, σ 2 represents the noise power, represents the set of drone base station users communicating on subchannel n.

[0075] Define user associated variable x k,m represents the connection relationship between drone base station m and user k. When user k establishes a connection with base station m, x k,m =1, otherwise, x k,m =0; define subchannel allocation variable y k,m,n represents the subchannel allocation of drone base station m. When subchannel n of drone base station m is occupied by user k, y k,m,n =1, otherwise, y k,m,n =0; use As a collection of user-associated variables, As a collection of subchannel allocation variables, As a collection of user power allocation variables;

[0076] Based on the above definition, considering factors such as base station user association, subchannel allocation and user signal transmission power, and taking maximizing system throughput as the optimization goal, the following joint optimization problem is established:

[0077]

[0078] S2, considering user demand, base station load, intra-layer interference and inter-layer interference, establishes corresponding constraints on user rate, base station capacity, power and signal interference intensity.

[0079] In the embodiment, in a single connection scenario, that is, when a user establishes a connection and communicates with a macro base station or a drone base station through a single connection technology, a total of 9 corresponding constraints are established, which are:

[0080]

[0081] Among them, constraint (a1) ensures that each user k can obtain a communication rate that meets its minimum requirements Constraint (b1) indicates that the signal interference of drone base station m to macro base station on subchannel n should be controlled within the interference threshold Inside, h k,0,n represents the channel gain from drone base station user k to macro base station 0; constraint (c1) indicates that a user can only connect to one base station; constraint (d1) indicates that the number of users served by drone base station m cannot exceed the maximum number of users it can serve, q m To ensure load balancing; Constraints (e1) and (f1) indicate that for each drone base station, each subchannel can be assigned to at most one user, and each user can be assigned to at most one subchannel; Constraints (g1) and (h1) indicate that the variable x k,m and variable y k,m,n is a binary variable; constraint (i1) indicates that the signal transmission power of each user should be controlled within the maximum signal transmission power and minimum transmission power A certain range of Inside.

[0082] In the multi-connection scenario, that is, when the user establishes connections and communicates with multiple base stations through multi-connection technology, a total of 9 corresponding constraints are established, and only the third constraint (c1) is modified to:

[0083]

[0084] The remaining eight constraints are the same as those in the single-connection scenario (a1)-(b1), (d1)-(i1). Constraint (c2) indicates that each user k can have at most k Base station connection.

[0085] S3, solving the joint optimization problem under the corresponding constraints, specifically including using the matching algorithm to solve the user association and subchannel allocation optimization problems, and then using the Lagrangian dual method to solve the power allocation problem.

[0086] The above optimization problem is a mixed integer nonlinear programming (MINLP) problem, which cannot be solved directly. In order to solve this problem, the present invention uses a block coordinate descent (BCD) algorithm to split the optimization problem into three sub-problems: user association problem, sub-channel allocation problem and power allocation problem, and then solves the three sub-problems in blocks and iteratively updates the variables to achieve overall optimization. Among them, the utility function is designed according to the constraints and optimization objectives, and the matching algorithm is used to solve the user association and sub-channel allocation optimization problems in heterogeneous networks, and the Lagrangian dual method is used to solve the power allocation problem.

[0087] like Figure 3 As shown in , the process of solving the joint optimization problem under the corresponding constraints is:

[0088] Step 1: Initialize the given conditions and constraint information, including the number and location of users, the number and location of base stations, the minimum user rate, interference threshold, base station capacity, power range, subchannel bandwidth and signal noise. At the same time, initialize the user association variable set X, the subchannel allocation variable set Y and the user power allocation variable set P.

[0089] Step 2: Under the constraints, according to the subchannel allocation variable set Y and the user power allocation variable set P, the user association problem is solved by a matching algorithm to obtain X.

[0090] In the embodiment, the user side uses the average SINR of all available sub-channels of the drone base station m as the utility function for evaluating the base station m. Right now:

[0091]

[0092] The base station side uses the received signal strength of user k as the utility function to evaluate user k. Right now;

[0093]

[0094] Among them, Sm represents the set of available subchannels of base station m;

[0095] According to the user's utility function The user builds a preference list for drone base stations and sends a preference to the one with the largest utility function or b k The drone base station sends a connection request, and the drone base station responds according to the utility function on the base station side. and its own remaining capacity to decide whether to accept the user's connection request;

[0096] If the user is rejected, the drone base station is removed from the preference list and the user continues to try to find the one with the largest utility function in the preference list or b k The drone base station sends a connection request, and so on, until each user is matched with a drone base station that accepts it and establishes a connection to build the user association variable set X;

[0097] It should be noted that when the UAV-assisted power communication system is applied to a single-connection scenario, a connection request is sent to a UAV base station. When the UAV-assisted power communication system is applied to a multi-connection scenario, a connection request is sent to b k A drone base station sends a connection request.

[0098] Step 3: Under the constraints, according to X obtained in step 2 and the user power allocation variable set P, the sub-channel allocation problem is solved by a matching algorithm to obtain Y.

[0099] In the embodiment, for each drone base station, it is only necessary to allocate subchannels to users connected to itself, without considering the user connections and subchannel allocations of other drone base stations, and the subchannel allocation problem is regarded as an independent sub-problem. Based on this, for drone base station m, the user side adopts the communication rate R on subchannel n k,m,n As the utility function for evaluating this subchannel Right now:

[0100]

[0101] The subchannel side uses the weighted result of the communication rate of user k on subchannel n and the interference caused by the user to other base stations on subchannel n as the utility function for evaluating user k. Right now:

[0102]

[0103] in, represents the minimum required communication rate, p k,n represents the signal power of drone base station user k on subchannel n, h k,q,nrepresents the channel gain from drone base station user k to drone base station q, h k,0,n represents the channel gain from drone base station user k to macro base station 0, α and β represent weighting coefficients, and their value range is [0,1].

[0104] According to the user-side utility function The user builds a preference list for subchannels and sends a request to the base station for a subchannel with the largest utility function. The base station then selects the subchannel based on the utility function on the subchannel side. and channel occupancy to determine whether to allocate a sub-channel to the user;

[0105] If the user is rejected, the subchannel is deleted from the preference list, and the connection request is continued to be sent to the subchannel with the largest utility function in the preference list of the base station subchannel, and so on, until a subchannel that accepts itself is matched for each user and a connection is established to construct a subchannel allocation variable set Y.

[0106] Step 4: Under the constraints, according to X and Y obtained in steps 2 and 3, the power allocation problem is solved by the Lagrangian dual method to obtain P.

[0107] In the embodiment, for different orthogonal subcarriers, since different subcarriers will not interfere with each other, the power allocation problem of user-base station pairs using different subchannels is regarded as an independent subproblem; therefore, for the power allocation problem of user-base station pairs using different subchannels, Lagrange multipliers are introduced to construct the Lagrange equation, and then the KKT (Karush-Kuhn-Tucker) condition is used to obtain the optimal solution for the power allocation, and the user power allocation variable set P is obtained.

[0108] Step 5: Repeat steps 2, 3, and 4 until the maximum number of iterations is reached or convergence occurs, and the final base station user association, subchannel allocation, and power allocation results are obtained.

[0109] In steps 1 to 5, by using a matching algorithm to solve the user association and sub-channel allocation problems and adjusting the transmission power in real time, the user communication rate can be effectively increased, the capacity of the heterogeneous network system can be improved, and the overall heterogeneous network can be optimized.

[0110] Based on the same inventive concept, an embodiment further provides a drone-assisted power communication user association and spectrum allocation device, including a memory and one or more processors, wherein an executable code is stored in the memory, and when the one or more processors execute the executable code, the method for drone-assisted power communication user association and spectrum allocation is implemented, specifically including the following steps:

[0111] S1, with maximizing system throughput as the optimization goal, establish the joint optimization problem of base station user association, subchannel allocation and power allocation in the system;

[0112] S2, considering user demand, base station load, intra-layer interference and inter-layer interference, establishes corresponding constraints on user rate, base station capacity, power and signal interference intensity;

[0113] S3, solving the joint optimization problem under the corresponding constraints, specifically including using the matching algorithm to solve the user association and subchannel allocation optimization problems, and then using the Lagrangian dual method to solve the power allocation problem.

[0114] The drone-assisted power communication user association and spectrum allocation device provided in the embodiment, at the hardware level, includes not only a processor and a memory, but also hardware required for other services such as an internal bus, a network interface, and a memory. The memory is a non-volatile memory, and the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the drone-assisted power communication user association and spectrum allocation method described in S1-S3 above. Of course, in addition to software implementation methods, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0115] The specific implementation methods described above have described in detail the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for unmanned aerial vehicle-assisted power communication user association and spectrum allocation, characterized in that: The method is applied to a UAV-assisted power communication heterogeneous network system consisting of several UAV base stations, one macro base station, and several randomly distributed users, and includes the following steps: Taking maximizing system throughput as the optimization goal, a joint optimization problem of base station user association, subchannel allocation and power allocation in the system is established; Considering user demand, base station load, intra-layer interference and inter-layer interference, corresponding constraints are established on user rate, base station capacity, power and signal interference intensity; The joint optimization problem is solved under the corresponding constraints, which includes using a matching algorithm to solve the user association and subchannel allocation optimization problems, and then using the Lagrangian dual method to solve the power allocation problem.

2. The method for unmanned aerial vehicle-assisted power communication user association and spectrum allocation according to claim 1, characterized in that: With the optimization goal of maximizing system throughput, a joint optimization problem of base station user association, subchannel allocation, and power allocation in the system is established, including: According to Shannon's formula, when user k establishes a connection with drone base station m on subchannel n, its communication rate R k,m,n It is expressed as: R k,m,n =B n log2(1+γ k,m,n ), Among them, B n represents the bandwidth of subchannel n, γ k,m,n It represents the signal-to-interference-to-noise ratio of the signal received by user k, and its calculation formula is as follows: Among them, p k,n represents the signal power of drone base station user k on subchannel n, p j,n represents the signal power of drone base station user j on subchannel n, p l,n represents the signal power of macro base station user l on subchannel n, h k,m,n 、h j,m,n 、h l,m,n denote the channel gains from drone base station user k, drone base station user j and macro base station user l to drone base station m, σ 2 represents the noise power, represents the set of drone base station users communicating on subchannel n; Define user associated variable x k,m represents the connection relationship between drone base station m and user k. When user k establishes a connection with base station m, x k,m =1, otherwise, x k,m =0; define subchannel allocation variable y k,m,n represents the subchannel allocation of drone base station m. When subchannel n of drone base station m is occupied by user k, y k,m,n =1, otherwise, y k,m,n =0; use As a collection of user-associated variables, As a collection of subchannel allocation variables, As a collection of user power allocation variables; Based on the above definition, considering factors such as base station user association, subchannel allocation and user signal transmission power, and taking maximizing system throughput as the optimization goal, the following joint optimization problem is established: in, They represent the set of UAV base stations, the set of users, and the set of orthogonal sub-channels respectively.

3. The method for unmanned aerial vehicle-assisted power communication user association and spectrum allocation according to claim 1, characterized in that: Considering user demand, base station load, intra-layer interference and inter-layer interference, corresponding constraints are established for user rate, base station capacity, power and signal interference strength, including: When the UAV-assisted power communication system is applied to a single connection scenario, that is, when a user establishes a connection and communicates with a macro base station or a UAV base station through a single connection technology, a total of 9 corresponding constraints are established, namely: Among them, constraint (a1) ensures that each user k can obtain a communication rate that meets its minimum requirements Constraint (b1) indicates that the signal interference of drone base station m to macro base station on subchannel n should be controlled within the interference threshold Inside, h k,0,n represents the channel gain from drone base station user k to macro base station 0; constraint (c1) indicates that a user can only connect to one base station; constraint (d1) indicates that the number of users served by drone base station m cannot exceed the maximum number of users it can serve, q m To ensure load balancing; Constraints (e1) and (f1) indicate that for each drone base station, each subchannel can be assigned to at most one user, and each user can be assigned to at most one subchannel; Constraints (g1) and (h1) indicate that the variable x k,m and variable y k,m,n is a binary variable; constraint (i1) indicates that the signal transmission power of each user should be controlled within the maximum signal transmission power and minimum transmission power A certain range of Inside.

4. The method for UAV-assisted power communication user association and spectrum allocation according to claim 3, characterized in that: Considering user demand, base station load, intra-layer interference and inter-layer interference, corresponding constraints are established for user rate, base station capacity, power and signal interference strength, including: When the UAV-assisted power communication system is applied to a multi-connection scenario, that is, when the user establishes connections and communicates with multiple base stations through multi-connection technology, a total of 9 corresponding constraints are established, and only the third constraint (c1) is modified to: The remaining eight constraints are the same as those in the single-connection scenario (a1)-(b1), (d1)-(i1). Constraint (c2) indicates that each user k can have at most k Base station connection.

5. The method for UAV-assisted power communication user association and spectrum allocation according to claim 1, characterized in that: Solve the joint optimization problem under the corresponding constraints, specifically using the matching algorithm to solve the user association and subchannel allocation optimization problems, and then use the Lagrangian dual method to solve the power allocation problem, including: Step 1: Initialize given conditions and constraint information, including the number and location of users, the number and location of base stations, the minimum user rate, interference threshold, base station capacity, power range, subchannel bandwidth and signal noise. At the same time, initialize the user association variable set X, the subchannel allocation variable set Y and the user power allocation variable set P; Step 2: Under the constraints, according to the subchannel allocation variable set Y and the user power allocation variable set P, the user association problem is solved by the matching algorithm to obtain X; Step 3: Under the constraints, according to X obtained in step 2 and the user power allocation variable set P, the sub-channel allocation problem is solved by a matching algorithm to obtain Y; Step 4: Under the constraints, according to X and Y obtained in steps 2 and 3, the power allocation problem is solved by Lagrangian dual method to obtain P; Step 5: Repeat steps 2, 3, and 4 until the maximum number of iterations is reached or convergence occurs, and the final base station user association, subchannel allocation, and power allocation results are obtained.

6. The method for unmanned aerial vehicle-assisted power communication user association and spectrum allocation according to claim 5, characterized in that: Step 2 includes: The user side uses the average signal-to-interference-noise ratio of all available subchannels of the drone base station m as the utility function for evaluating the base station m. Right now: The base station side uses the received signal strength of user k as the utility function to evaluate user k. Right now; Among them, γ k,m,n represents the signal-to-interference-to-noise ratio of the signal received by user k, p k,n represents the signal power of drone base station user k on subchannel n, h k,m,n represents the channel gain from drone base station user k to drone base station m, S m represents the set of available subchannels of base station m; According to the user's utility function The user builds a preference list for drone base stations and sends a preference to the one with the largest utility function or b k The drone base station sends a connection request, and the drone base station responds according to the utility function on the base station side. and its own remaining capacity to decide whether to accept the user's connection request; If the user is rejected, the drone base station is removed from the preference list and the user continues to try to find the one with the largest utility function in the preference list or b k The drone base station sends a connection request, and so on, until each user is matched with a drone base station that accepts it and establishes a connection to build the user association variable set X; When the UAV-assisted power communication system is applied to a single-connection scenario, a connection request is sent to a UAV base station. When the UAV-assisted power communication system is applied to a multi-connection scenario, a connection request is sent to b k A drone base station sends a connection request.

7. The method for unmanned aerial vehicle-assisted power communication user association and spectrum allocation according to claim 5, characterized in that: Step 3 includes: For each drone base station, it only needs to allocate subchannels to users who have established connections with it. It does not need to consider the user connections and subchannel allocations of other drone base stations, and regards the subchannel allocation problem as an independent subproblem. Based on this, for drone base station m, the user side uses the communication rate R on subchannel n k,m,n As the utility function for evaluating this subchannel Right now: The subchannel side uses the weighted result of the communication rate of user k on subchannel n and the interference caused by the user to other base stations on subchannel n as the utility function for evaluating user k. Right now: in, represents the minimum required communication rate, p k,n represents the signal power of drone base station user k on subchannel n, h k,q,n represents the channel gain from drone base station user k to drone base station q, h k,0,n represents the channel gain from the drone base station user k to the macro base station 0, α and β represent the weighting coefficients respectively; According to the user-side utility function The user builds a preference list for subchannels and sends a request to the base station for a subchannel with the largest utility function. The base station then selects the subchannel based on the utility function on the subchannel side. and channel occupancy to determine whether to allocate a sub-channel to the user; If the user is rejected, the subchannel is deleted from the preference list, and the connection request is continued to be sent to the subchannel with the largest utility function in the preference list of the base station subchannel, and so on, until a subchannel that accepts itself is matched for each user and a connection is established to construct a subchannel allocation variable set Y.

8. The method for unmanned aerial vehicle-assisted power communication user association and spectrum allocation according to claim 5, characterized in that: Step 4 includes: For different orthogonal subcarriers, since different subcarriers will not interfere with each other, the power allocation problem of user-base station pairs using different subchannels is considered as an independent subproblem; For the power allocation problem of user-base station pairs with different sub-channels, Lagrange multipliers are introduced to construct the Lagrange equation, and then the KKT condition is used to obtain the optimal solution of power allocation, and the user power allocation variable set P is obtained.

9. A drone-assisted power communication user association and spectrum allocation device, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: The device is applied to a UAV-assisted power communication heterogeneous network system consisting of several UAV base stations, one macro base station, and several randomly distributed users. When the one or more processors execute the executable code, it is used to implement the UAV-assisted power communication user association and spectrum allocation method described in any one of claims 1-8.

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