An Unmanned Aerial Vehicle-Assisted Method and Device for Power Communication User Association and Spectrum Allocation

Abstract

The present invention discloses a method and device for user association and spectrum allocation in power communication assisted by an unmanned aerial vehicle (UAV), including: establishing a joint optimization problem of base station user association, sub-channel allocation, and power allocation in the system with the goal of maximizing system throughput; considering user requirements, base station load, intra-layer interference, and inter-layer interference, establishing corresponding constraints on user rate, base station capacity, power, and signal interference intensity; solving the joint optimization problem under the corresponding constraints, specifically including using a matching algorithm to solve the optimization problems of user association and sub-channel allocation, and then using the Lagrangian dual method to solve the power allocation problem. The present invention can effectively increase the user communication rate, improve the capacity of the heterogeneous network system, and optimize the overall heterogeneous network.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to a method and device for user association and spectrum allocation in a drone-assisted power communication system. Background Art

[0002] In recent years, the emergence of smart grids has broken through the traditional power supply and consumption models. Smart grids utilize 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 relies on an effective, reliable, and powerful communication system.

[0003] The smart power communication network covers facilities such as power plants, substations, distributed power sources, and electricity meters, supports information interaction between the distribution side and the upper control center, and enables services such as distribution operation monitoring and power consumption information collection. Therefore, the smart power communication network directly faces power customers and society, and is characterized by a wide coverage area, a large number of devices, and complex service types.

[0004] To meet the complex and diverse communication requirements, multiple communication networks complement each other to form a heterogeneous network to achieve high-speed seamless coverage of the smart power communication network. To coordinate multi-user access and maximize system throughput, the heterogeneous network must provide flexible resource allocation, intelligent network management, and fine-grained traffic control. At the same time, the deployment of wireless terminals in the smart power communication network shows diversity and complexity in terms of spatial location. To meet the electricity demand of users as much as possible, some terminal devices are deployed in areas with dense buildings and a large number of user aggregations, 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 situations where a large number of users simultaneously access the power communication network. This poses 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 due to their low cost, high mobility, and adaptive capabilities. Therefore, there is an urgent need for a drone-assisted power communication system.

[0005] In a drone-assisted power communication network, low-power drone base stations are deployed within the area covered by a macro base station to form a heterogeneous network. The macro base station and the drone base stations 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 drone base stations through single-connection or multi-connection technologies to communicate. The traditional network access selection mechanism simply considers connecting users to the base station that can provide the maximum signal-to-noise ratio. In a heterogeneous network, due to ignoring the differences between base stations, this algorithm is prone to cause load imbalance and low resource utilization, and cannot meet the requirements of intelligent power communication. Literature 1 (User-network association algorithm based on matching game in large-scale MIMO heterogeneous networks [J]) studied the user association algorithm based on one-to-many matching in heterogeneous networks, and obtained the user association strategy that maximizes the system energy efficiency through matching game while ensuring the user QoS (Quality of Service). However, it ignored the consideration of base station power control, 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. Literature 2 (Cooperative optimization of user association and base station power in heterogeneous cellular networks [J]) studied the cooperative optimization scheme of user association mechanism and base station power control in heterogeneous networks, and obtained the optimal solution by using constraint relaxation and convex optimization iteration. However, the relaxation variable may lead to a loose upper bound solution, resulting in the final obtained upper bound solution not being the optimal solution of the original problem. The patent application with the application number CN202110526421.8 studied the user association joint power allocation strategy based on deep learning in heterogeneous networks, obtained the user association and power allocation strategies as a data set by using the Hungarian algorithm and the water filling algorithm respectively, and then trained a deep neural network according to the data set to achieve online decision-making. However, the training of the deep neural network model requires a large amount of data and there are problems with model generalization. Summary of the Invention

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

[0007] To achieve the above object of the invention, an embodiment provides a method for user association and spectrum allocation in drone-assisted power communication. The method is applied to a drone-assisted power communication heterogeneous network system composed of several drone base stations, one macro base station, and several randomly distributed users, and includes the following steps:

[0008] Taking the maximization of system throughput as the optimization goal, establish a joint optimization problem of base station-user association, sub-channel allocation, and power allocation in the system;

[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 strength;

[0010] The joint optimization problem is solved under the corresponding constraints, specifically 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, with 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 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 represents the signal-to-interference-and-noise ratio of the received signal of user k, which is calculated as follows:

[0015]

[0016] Among them, p k,n represents the signal power of the 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 the drone base station m. When the subchannel n of the drone base station m is occupied by user k, y k,m,n =1, otherwise, y k,m,n= 0; Usage As a set of user association variables, As a set of sub-channel allocation variables, As a set of user power allocation variables;

[0018] Based on the above definitions, considering factors such as base station-user association, sub-channel allocation, and user signal transmission power, with the goal of maximizing system throughput, the following joint optimization problem is established:

[0019]

[0020] Among them, Respectively represent the set of UAV base stations, the set of users, and the set of orthogonal sub-channels.

[0021] Preferably, considering user requirements, base station load, intra-layer interference, and inter-layer interference, corresponding constraints are established for 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 single-connection technology, a total of 9 corresponding constraints are established, which are:

[0023]

[0024]

[0025] Among them, constraint (a1) ensures that each user k can obtain a communication rate that can meet its minimum requirements Constraint (b1) means that the signal interference of the UAV base station m on the macro base station on sub-channel n should be controlled within the interference threshold Inside, h k,0,n Represents the channel gain from the UAV base station user k to the macro base station 0; Constraint (c1) means that a user can only be connected to one base station; Constraint (d1) means that the number of users served by the UAV base station m cannot exceed the maximum number of users q it can serve m To ensure load balancing; Constraints (e1) and (f1) mean that for each UAV base station, each sub-channel can be allocated to at most one user, and each user can be allocated at most one sub-channel; Constraints (g1) and (h1) mean that the variable x k,m And the variable y k,m,n Are binary variables; Constraint (i1) means that the signal transmission power of each user should be controlled within a certain range formed by the maximum signal transmission power And the minimum transmission power Formed Inside.

[0026] Preferably, considering user requirements, 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:

[0027] When the UAV-assisted power communication system is applied to a multi-connection scenario, that is, when a user establishes connections with multiple base stations through multi-connection technology and communicates, the corresponding constraints established are also a total of 9. Only the 3rd constraint (c1) is modified to:

[0028]

[0029] The remaining 8 constraints are the same as the constraints (a1)-(b1), (d1)-(i1) in the single-connection scenario. The constraint (c2) means that each user k can be connected to at most b k base stations.

[0030] Preferably, under the corresponding constraints, the joint optimization problem is solved, specifically including using a matching algorithm to solve the user association and sub-channel allocation optimization problems, and then using the Lagrangian dual method to solve the power allocation problem, including:

[0031] 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, the interference threshold, the base station capacity, the power range, the sub-channel bandwidth, and the signal noise. At the same time, initialize the user association variable set X, the sub-channel allocation variable set Y, and the user power allocation variable set P;

[0032] Step 2: Under the constraints, according to the sub-channel allocation variable set Y and the user power allocation variable set P, solve the user association problem through a 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, solve the sub-channel allocation problem through a matching algorithm to obtain Y;

[0034] Step 4: Under the constraints, according to X and Y obtained in Steps 2 and 3, solve the power allocation problem through the 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 obtain the final base station-user association, sub-channel allocation, and power allocation results.

[0036] Preferably, Step 2 includes:

[0037] The user side uses the average signal-to-interference-plus-noise ratio of all available sub-channels of the UAV base station m as the utility function for evaluating the base station m That is:

[0038]

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

[0040]

[0041] where γ k,m,n represents the signal-to-interference-plus-noise ratio of the received signal of user k, and p k,n represents the signal power magnitude of user k of the UAV base station on subchannel n, and h k,m,n represents the channel gain from user k of the UAV base station to UAV base station m, and S m represents the set of available subchannels of base station m;

[0042] According to the utility function on the user side The user constructs a preference list for the UAV base stations and sends a connection request to one or b k UAV base stations with the largest utility function among them. The UAV base station then decides whether to accept the user's connection request according to the utility function on the base station side and its remaining capacity;

[0043] If the user is rejected, the UAV base station is deleted from the preference list, and the user continues to try to send a connection request to one or b k UAV base stations with the largest utility function in the preference list, and so on, until a UAV base station that accepts itself is matched for each user and a connection is established to construct the user association variable set X;

[0044] where, when the UAV-assisted power communication system is applied to a single connection scenario, a connection request is sent to one UAV base station, and when the UAV-assisted power communication system is applied to a multi-connection scenario, a connection request is sent to b k UAV base stations.

[0045] Preferably, step 3 includes:

[0046] For each UAV base station, it only needs to allocate subchannels to the users that have established connections with itself, without considering the user connections and subchannel allocations of other UAV base stations, and regards the subchannel allocation problem as an independent subproblem. Based on this, for UAV base station m, the user side uses the communication rate R k,m,n on subchannel n as its utility function to evaluate this subchannel That is:

[0047]

[0048] On the sub-channel side, the weighted result of the communication rate of user k on sub-channel n and the interference caused by the user to other base stations on sub-channel n is used as the utility function for evaluating user k. That is:

[0049]

[0050] Among them, represents the minimum required communication rate, p k,n represents the signal power of drone base station user k on sub-channel 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 drone base station user k to macro base station 0, and α and β respectively represent the weighting coefficients;

[0051] According to the user-side utility function The user constructs a preference list for sub-channels and sends a request to the base station for the sub-channel with the largest utility function among them. The base station then determines whether to allocate a sub-channel to the user according to the utility function on the sub-channel side and the channel occupancy situation;

[0052] If the user is rejected, the sub-channel is deleted from the preference list, and the user continues to try to send a connection request to the sub-channel with the largest utility function in the base station's sub-channel preference list, and so on, until a sub-channel that accepts itself is matched for each user and a connection is established to construct the sub-channel allocation variable set Y.

[0053] Preferably, step 4 includes:

[0054] For different orthogonal sub-carriers, since different sub-carriers do not interfere with each other, the power allocation problem of user-base station pairs using different sub-channels is regarded as an independent sub-problem;

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

[0056] To achieve the above invention purpose, the embodiment also provides a drone-assisted power communication user association and spectrum allocation device, including a memory and one or more processors. Executable code is stored in the memory. The device is applied to a drone-assisted power communication heterogeneous network system composed of several drone 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 above-mentioned drone-assisted power communication user association and spectrum allocation method.

[0057] Compared with the prior art, the beneficial effects of the present invention at least include:

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

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 is a flowchart of the UAV-assisted power communication user association and spectrum allocation method provided by the embodiment;

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

[0062] Figure 3 is a flowchart of the solution to the joint optimization problem provided by the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] To make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the protection scope of the present invention.

[0064] The inventive concept of the present invention is as follows: in a drone-assisted power communication heterogeneous network system, while maximizing the system throughput, it is necessary to ensure the communication quality of users, and also to ensure the load balance of the base stations, and to perform real-time regulation on the signal interference within the system. To achieve the above requirements, it is necessary to comprehensively consider the user association, sub-channel allocation, and power allocation problems in the heterogeneous network. The current research work mainly focuses on the research of a certain aspect, without optimizing the overall heterogeneous network, and there are respective deficiencies. In view of this technical problem, the embodiments of the present invention provide a drone-assisted power communication user association and spectrum allocation scheme, which is applied to a scenario where there is one macro base station, several drone base stations, and several randomly distributed users in a drone-assisted power communication heterogeneous network system. Among them, by deploying drone base stations in the coverage blind area or hot spot area, the traffic volume of the macro base station can be shared, and the communication requirements in the blind area or the area with large traffic can be met, thereby improving the system capacity and improving the network quality.

[0065] In the scenario, as Figure 2 shown, the macro base station coexists with M drone base stations, and the coverage areas overlap, so as to provide high-speed seamless communication services for K randomly distributed users. For the sake of convenience of representation, use to represent the set of drone base stations, and use to represent the set of users. In order to improve the spectrum utilization rate, the system uses OFDMA (Orthogonal Frequency Division Multiple Access) for communication. The macro base station and the drone base stations share a set of orthogonal sub-channels for communication. This set of orthogonal sub-channels is represented by the set . Users establish connections with the macro base station or drone base stations through single-connection or multi-connection technologies and communicate. In the single-connection scenario where single-connection is adopted, users establish connections with the macro base station or one drone base station through single-connection technology and communicate.

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

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

[0068] S1. With the goal of maximizing system throughput, establish a joint optimization problem for base station-user association, sub-channel allocation, and power allocation in the system.

[0069] In the embodiment, the process of establishing the joint optimization problem for base station-user association, sub-channel allocation, and power allocation in the system is as follows:

[0070] According to the Shannon formula, when user k establishes a connection with drone base station m on sub-channel n, its communication rate R k,m,n is expressed as:

[0071] R k,m,n = B n log2(1 + γ k,m,n ),

[0072] where B n represents the bandwidth of sub-channel n, and γ k,m,n represents the SINR (Signal to Interference Plus Noise) of the signal received by user k, and its calculation formula is as follows:

[0073]

[0074] where p k,n represents the signal power of drone base station user k on sub-channel n, p j,n represents the signal power of drone base station user j on sub-channel n, p l,n represents the signal power of macro base station user l on sub-channel n, h k,m,n , h j,m,n , h l,m,n respectively represent 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, and σ 2 represents the noise power, represents the set of drone base station users communicating on sub-channel n.

[0075] Define the user association variable x k,m to represent 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 the sub-channel allocation variable y k,m,n to represent the sub-channel allocation situation of drone base station m. When sub-channel n of drone base station m is occupied by user k, y k,m,n = 1; otherwise, y k,m,n = 0. Use as the set of user association variables, as the set of sub-channel allocation variables, As a set of user power allocation variables;

[0076] Based on the above definitions, considering factors such as base station - user association, sub - channel allocation, and user signal transmission power, with the goal of maximizing system throughput, the following joint optimization problem is established:

[0077]

[0078] S2. Considering user requirements, base station load, intra - layer interference, and inter - layer interference, corresponding constraints are established for user rate, base station capacity, power, and signal interference intensity.

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

[0080]

[0081] Among them, constraint (a1) ensures that each user k can obtain a communication rate that can meet its minimum requirements. Constraint (b1) indicates that the signal interference of the UAV base station m on the macro - base station on sub - channel n should be controlled within the interference threshold. h k,0,n represents the channel gain from the UAV base station user k to the macro - base station 0; constraint (c1) indicates that a user can only be connected to one base station; constraint (d1) indicates that the number of users served by the UAV base station m cannot exceed its maximum number of servable users q m to ensure load balancing; constraints (e1) and (f1) indicate that for each UAV base station, each sub - channel can be allocated to at most one user, and each user can be allocated at most one sub - channel; constraints (g1) and (h1) indicate that the variable x k,m and the variable y k,m,n are binary variables; constraint (i1) indicates that the signal transmission power of each user should be controlled within a certain range formed by the maximum signal transmission power and the minimum transmission power inside.

[0082] In the multi - connection scenario, that is, when the user establishes connections with multiple base stations through multi - connection technology and communicates, the corresponding constraints established are also a total of 9. Only the 3rd constraint (c1) is modified to:

[0083]

[0084] ​The remaining eight constraints are the same as the constraints (a1)-(b1), (d1)-(i1) in the single-connection scenario. Constraint (c2) means that each user k can be connected to at most b k base stations.

[0085] S3. Solve the joint optimization problem under the corresponding constraints, specifically including using a matching algorithm to solve the user association and sub-channel 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 and cannot be directly solved. To solve this problem, the present invention uses the block coordinate descent (BCD) algorithm to split the optimization problem into three sub-problems: the user association problem, the sub-channel allocation problem, and the 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 constraint conditions and optimization objectives, and the matching algorithm is used to solve the user association and sub-channel allocation optimization problems in the heterogeneous network, and the Lagrangian dual method is used to solve the power allocation problem.

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

[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, the interference threshold, the base station capacity, the power range, the sub-channel bandwidth, and the signal noise. At the same time, initialize the user association variable set X, the sub-channel allocation variable set Y, and the user power allocation variable set P.

[0089] Step 2: Under the constraints, according to the sub-channel allocation variable set Y and the user power allocation variable set P, solve the user association problem through the 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 That is:

[0091]

[0092] The base station side uses the received signal strength of user k as the utility function for evaluating user k That is;

[0093]

[0094] Among them, Sm Denote the set of available sub-channels of base station m;

[0095] According to the utility function on the user side The user constructs a preference list for the UAV base stations and sends connection requests to one or b k UAV base stations with the largest utility function among them. The UAV base stations then decide whether to accept the user's connection request according to the utility function on the base station side and its remaining capacity;

[0096] If the user is rejected, the UAV base station is deleted from the preference list, and the user continues to try to send connection requests to one or b k UAV base stations with the largest utility function in the preference list, and so on, until a UAV base station that accepts itself is matched for each user and a connection is established to construct 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 one 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 UAV base stations.

[0098] Step 3: Under the constraints, according to X obtained in Step 2 and the set P of user power allocation variables, solve the sub-channel allocation problem through a matching algorithm to obtain Y.

[0099] In the embodiment, for each UAV base station, it only needs to allocate sub-channels to the users that have established connections with itself, without considering the user connections and sub-channel allocations of other UAV base stations, and regards the sub-channel allocation problem as an independent sub-problem. Based on this, for UAV base station m, the communication rate R k,m,n on sub-channel n on the user side is used as its utility function for evaluating this sub-channel That is:

[0100]

[0101] On the sub-channel side, the weighted result of the communication rate of user k on sub-channel n and the interference brought by the user to other base stations on sub-channel n is used as the utility function for evaluating user k That is:

[0102]

[0103] Among them, represents the minimum required communication rate, p k,n represents the signal power magnitude of UAV base station user k on sub-channel n, h k,q,nDenote the channel gain from the UAV base station user \(k\) to the UAV base station \(q\) as \(h\). k,0,n Denote the channel gain from the UAV base station user \(k\) to the macro base station \(0\) as \(\alpha\) and \(\beta\) respectively, where the value ranges of \(\alpha\) and \(\beta\) are \([0, 1]\).

[0104] According to the user - side utility function The user constructs a preference list for sub - channels and sends a request to the base station for the sub - channel with the maximum utility function among them. Then, the base station decides whether to allocate a sub - channel to the user according to the utility function on the sub - channel side and the channel occupancy situation;

[0105] If the user is rejected, the sub - channel is deleted from the preference list, and the user continues to try to send a connection request to the sub - channel with the maximum utility function in the base station's sub - channel preference list, and so on, until a sub - channel that accepts the user is matched for each user and a connection is established to construct the sub - channel allocation variable set \(Y\).

[0106] Step 4: Under the constraints, according to \(X\) and \(Y\) obtained in Steps 2 and 3, solve the power allocation problem through the Lagrangian dual method to obtain \(P\).

[0107] In the embodiment, for different orthogonal sub - carriers, since there is no interference between different sub - carriers, the power allocation problems of user - base station pairs using different sub - channels are regarded as independent sub - problems; therefore, for the power allocation problems of user - base station pairs on different sub - channels, Lagrange multipliers are introduced to construct the Lagrangian equation, and then the KKT (Karush - Kuhn - Tucker) conditions are used to obtain the optimal solution of power allocation to obtain the user power allocation variable set \(P\).

[0108] Step 5: Repeat Steps 2, 3, and 4 until the maximum number of iterations is reached or convergence occurs to obtain the final base station - user association, sub - channel allocation, and power allocation results.

[0109] In Steps 1 - 5, by using the 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, the embodiment also provides a UAV - assisted power communication user association and spectrum allocation device, including a memory and one or more processors. The memory stores executable code. When the one or more processors execute the executable code, it is used to implement the above - mentioned UAV - assisted power communication user association and spectrum allocation method, specifically including the following steps:

[0111] S1. Establish a joint optimization problem for base station user association, sub-channel allocation, and power allocation in the system with the optimization goal of maximizing system throughput;

[0112] S2. Considering user requirements, base station load, intra-layer interference, and inter-layer interference, establish corresponding constraints for user rate, base station capacity, power, and signal interference intensity;

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

[0114] The UAV-assisted power communication user association and spectrum allocation device provided by the embodiment, at the hardware level, in addition to including a processor and a memory, also includes an internal bus, a network interface, a memory, and other hardware required for other services. The memory is a non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the UAV-assisted power communication user association and spectrum allocation method described in S1-S3 above. Of course, in addition to the software implementation method, the present invention does not exclude other implementation methods, such as logical 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 logical unit, and can also be hardware or a logical device.

[0115] The specific embodiments described above have elaborated on 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 used to limit the present invention. Any modifications, supplements, equivalent replacements, etc. made within the scope of the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for UAV-assisted power communication user association and spectrum allocation, characterized in that The method is applied to a drone-assisted power communication heterogeneous network system composed of several drone base stations, one macro base station, and several randomly distributed users, and includes the following steps: Taking maximizing the system throughput as the optimization objective, establish 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, establish corresponding constraints on user rate, base station capacity, power, and signal interference intensity; Under the corresponding constraints, solve the joint optimization problem. Split the optimization problem into three sub-problems: user association problem, sub-channel allocation problem, and power allocation problem. Then solve the three sub-problems in blocks and iteratively update the variables to achieve overall optimization. Among them, design a utility function according to the constraint conditions and optimization objectives and use a matching algorithm to solve the user association and sub-channel allocation optimization problems in the heterogeneous network, and use the Lagrangian dual method to solve the power allocation problem, including: (1) Under the constraints, according to the sub-channel allocation variable set Y and the user power allocation variable set P, solve the user association problem through the matching algorithm to obtain the user association variable set X. At this time, the user side uses the average SINR of all available sub-channels of the drone base station m as the utility function to evaluate the base station m, and the base station side uses the received signal strength of the user k as the utility function to evaluate the user k; (2) Under the constraints, according to the user association variable set X and the user power allocation variable set P obtained in step (2), solve the sub-channel allocation problem through the matching algorithm to obtain the sub-channel allocation variable set Y. At this time, for the drone base station m, the user side uses the communication rate on the sub-channel n as the utility function to evaluate the sub-channel, and the sub-channel side uses the weighted result of the communication rate of the user k on the sub-channel n and the interference brought by the user to other base stations on the sub-channel n as the utility function to evaluate the user k; (3) Under the constraints, according to the user association variable set X and the sub-channel allocation variable set Y obtained in steps (2) and (3), solve the power allocation problem through the Lagrangian dual method to obtain the user power allocation variable set P; (4) Repeat steps (2), (3), and (4) until the maximum number of iterations is reached or convergence occurs, and obtain the final results of base station-user association, sub-channel allocation, and power allocation.

2. The method for drone-assisted power communication user association and spectrum allocation according to claim 1, wherein ​ According to Shannon's formula, when user k establishes a connection with drone base station m on sub-channel n, its communication rate R k,m,n is expressed as: R k,m,n = B n log2(1 + γ k,m,n ), Among them, B n represents the bandwidth of sub-channel n, and γ k,m,n represents the signal-to-interference-plus-noise ratio of the received signal of user k, and its calculation formula is as follows: where p k,n represents the signal power of drone base station user k on sub-channel n, p j,n represents the signal power of drone base station user j on sub-channel n, p l,n represents the signal power of macro base station user l on sub-channel n, h k,m,n , h j,m,n , h l,m,n respectively represent 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 sub-channel n; Define the user association variable x k,m Indicates the connection relationship between the UAV base station m and the user k. When the user k establishes a connection with the base station m, x k,m = 1, otherwise, x k,m = 0; Define the sub-channel allocation variable y k,m,n Indicates the sub-channel allocation situation of the UAV base station m. When the sub-channel n of the UAV base station m is occupied by the user k, y k,m,n = 1, otherwise, y k,m,n = 0; Use as the set of user association variables, as the set of sub-channel allocation variables, as the set of user power allocation variables; ​ Among them, respectively represent the set of UAV base stations, the set of users, and the set of orthogonal sub-channels.

3. The method for drone-assisted power communication user association and spectrum allocation according to claim 1, characterized in that ​ ​ Among them, constraint (a1) ensures that each user k can obtain a communication rate that can meet its minimum requirements Constraint (b1) means that the signal interference of the UAV base station m on the macro base station on the subchannel n should be controlled within the interference threshold Inside, h k,0,n represents the channel gain from the UAV base station user k to the macro base station 0; constraint (c1) means that a user can only be connected to one base station; constraint (d1) means that the number of users served by the UAV base station m cannot exceed the maximum number of users q it can serve m to ensure load balancing; constraints (e1) and (f1) mean that for each UAV base station, each subchannel can be allocated to at most one user, and each user can be allocated at most one subchannel; constraints (g1) and (h1) mean that the variable x k,m and the variable y k,m,n are binary variables; constraint (i1) means that the signal transmission power of each user should be controlled within a certain range formed by the maximum signal transmission power and the minimum transmission power Inside the range formed .

4. The method for UAV-assisted power communication user association and spectrum allocation according to claim 3, wherein, Considering user requirements, 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 users establish connections and communicate with multiple base stations through multi-connection technology, the corresponding constraints established are also a total of 9. Only the 3rd constraint (c1) is modified to: The remaining eight constraints are the same as the constraints (a1)-(b1), (d1)-(i1) in the single-connection scenario. Constraint (c2) means that each user k can connect to at most b k base stations.

5. The method for drone-assisted power communication user association and spectrum allocation according to claim 1, characterized in that Step (1) includes: The user side uses the average signal-to-interference-plus-noise ratio of all available sub-channels of the drone base station m as the utility function to evaluate the base station m That is: The base station side uses the received signal strength of user k as the utility function for evaluating user k That is; Among them, γ k,m,n represents the signal-to-interference-plus-noise ratio of the signal received by user k, p k,n represents the signal power of the drone base station for user k on subchannel n, h k,m,n represents the channel gain from the drone base station user k to the drone base station m, S m represents the set of available subchannels of base station m; According to the utility function on the user side The user constructs a preference list for the drone base stations and sends a connection request to one or b drone base stations with the largest utility function k Among them, and the drone base station decides whether to accept the user's connection request according to the utility function on the base station side and its remaining capacity; If the user is rejected, the UAV base station is deleted from the preference list, and the connection request continues to be sent to one or b UAV base stations with the largest utility function in the preference list, and so on, until a UAV base station that accepts itself is matched for each user and a connection is established to construct the set X of user association variables; k A connection request is sent to the UAV base station, and so on, until a UAV base station that accepts itself is matched for each user and a connection is established to construct the set X of user association variables; Among them, when the UAV-assisted power communication system is applied to a single-connection scenario, a connection request is sent to one 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 UAV base stations.

6. The method for drone-assisted power communication user association and spectrum allocation according to claim 1, wherein Step (2) includes: For each drone base station, it only needs to allocate sub-channels to the users that have established connections with itself, without considering the user connections and sub-channel allocations of other drone base stations, and regards the sub-channel allocation problem as an independent sub-problem. Based on this, for drone base station m, the communication rate R on sub-channel n is adopted on the user side k,m,n as its utility function for evaluating this sub-channel That is: The sub-channel side uses the weighted result of the communication rate of user k on sub-channel n and the interference brought by the user to other base stations on sub-channel n as the utility function for evaluating user k That is: Among them, represents the minimum required communication rate, p k,n represents the signal power magnitude of the drone base station user k on the sub-channel n, h k,q,n represents the channel gain from the drone base station user k to the 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, and α and β respectively represent the weighting coefficients; According to the user-side utility function The user constructs a preference list for sub-channels and sends a request to the base station for the sub-channel with the largest utility function among them. The base station then decides whether to allocate a sub-channel to the user according to the utility function on the sub-channel side and the channel occupancy situation; If the user is rejected, the sub-channel is deleted from the preference list, and the connection request continues to be sent to the sub-channel with the maximum utility function in the preference list of the base station sub-channels, and so on, until a sub-channel that accepts itself is matched for each user and a connection is established to construct the set Y of sub-channel allocation variables.

7. The method for drone-assisted power communication user association and spectrum allocation according to claim 1, wherein Step (3) includes: For different orthogonal sub-carriers, since different sub-carriers do not interfere with each other, the power allocation problem of user-base station pairs using different sub-channels is regarded as an independent sub-problem; For the power allocation problem of user-base station pairs on different sub-channels, Lagrange multipliers are introduced to construct the Lagrangian equation, and then the KKT conditions are used to obtain the optimal solution of power allocation to obtain the set P of user power allocation variables.

8. An unmanned aerial vehicle (UAV)-assisted power communication user association and spectrum allocation device, comprising a memory and one or more processors, wherein executable code is stored in the memory, characterized in that, The device is applied to a UAV-assisted power communication heterogeneous network system composed 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 according to any one of claims 1-7.

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