| With the development of new generation network technology and the arrival of the Internet of Things era,the rapid growth of various users has increased the demand for communication capacity and transmission rate of wireless communication systems,resulting in single antenna systems being unable to effectively ensure the quality of service of wireless networks.Beamforming technology based on multi-antenna systems can effectively improve system capacity,signal gain,and energy efficiency.Similarly,collaborative beamforming(CB)introduces beamforming ideas into distributed wireless networks,forming a virtual antenna array of communication nodes to generate high gain and directional beams for signal transmission,thereby improving the network capacity of distributed systems such as unmanned aerial vehicles(UAV).In various types of antenna arrays and virtual antenna array systems,optimizing the excitation current weights of array elements to obtain the optimal beam pattern is an important issue that affects communication performance.However,this problem is a complex nonlinear optimization problem,so using traditional optimization methods to solve it has a high cost; Furthermore,introducing CB into UAV networks can significantly improve the transmission distance and communication energy efficiency of single node.However,due to the random distribution of nodes in the UAV network,position errors will generate higher sidelobe levels(SLL),thereby reducing the signal-to-noise ratio(SNR)of beamforming communication.In addition,UAVs face the problem of limited onboard energy,so it is crucial to design the optimal excitation current weights and deploy UAV positions to improve beamforming communication performance and energy efficiency.This article focuses on three issues in wireless communication: beam pattern optimization in antenna arrays with different geometric shapes,beamforming performance optimization based on large-scale antenna arrays,and joint optimization of CB communication performance and energy consumption control in UAV-based virtual antenna arrays(UVAA).Moreover,based on evolutionary computation,methods are designed to solve the above problems.The main contributions and innovations are as follows:1.Optimization of antenna array pattern based on different geometric shapesAn invasive weed optimization algorithm with random mutation and lévy flight(IWORMLF)is proposed to solve optimization problem of suppressing the maximum SLL of linear antenna array(LAA)and circular antenna array(CAA),as well as the joint optimization problem of maximum SLL and mainlobe width.The algorithm balances search and development capabilities through introduction of random mutation operators and lévy flight mechanism.Firstly,the effectiveness of IWORMLF algorithm is verified by using standard test function set CEC 2014; Then,in LAA and CAA based on different numbers of array elements,IWORMLF algorithm is used to design excitation current weights of array elements,thereby suppressing the maximum SLL of different antenna arrays and jointly reducing the maximum SLL and controlling mainlobe width.The experimental results verify effectiveness and stability of the improvement factors;Finally,electromagnetic simulation experiments are used to evaluate the performance of IWORMLF algorithm to optimize beam pattern under mutual coupling.2.Optimization of beamforming performance based on large-scale antenna arraysA particle swarm optimization algorithm with global search and population mutation(PSOGP)is proposed to solve joint optimization problem of SLL and nulls control for large-scale antenna arrays.The algorithm improves global search ability and population diversity by introducing global search and population mutation factors.Firstly,the standard test function set CEC 2014 are used to verify effectiveness of PSOGP;Then,in a large-scale linear antenna array(LSLAA)with different numbers of array elements,PSOGP is used to optimize the excitation current weights of array elements to jointly reduce the maximum SLL and control nulls.The experimental results demonstrate convergence speed and stability of the algorithm; Finally,simulation experiments are conducted to verify the effectiveness of improvement mechanisms in PSOGP.3.Joint optimization of CB communication performance and energy consumption control based on UVAA(1)A particle swarm optimization algorithm with weed optimization mechanism(PSOWOM)is proposed to solve the joint optimization problem of SNR and energy consumption control for CB communication in UVAA.The algorithm introduces weed optimization mechanism to avoid optimization stagnation and improve global search performance.Firstly,the effectiveness of the PSOWOM algorithm is verified by using the standard test function set CEC 2014; Then,based on different numbers of UAVs in the virtual antenna array,the PSOWOM algorithm is used to design the positions of UAV nodes to simultaneously improve the SNR and reduce energy consumption of CB communication,experimental results demonstrate the effectiveness of the algorithm;Finally,simulation experiments are conducted to evaluate the stability of the PSOWOM algorithm and the ability of improvement mechanism to solve the formulated problem.(2)On the basis of the above research,this article further investigates the CB communication performance and energy consumption control of UAV networks.Firstly,an enhanced multi-objective ant lion optimization(EMOALO)algorithm based on Chaos theory is proposed to solve multi-objective optimization problem of maximum SLL,transmission rate,and UAV energy consumption in UVAA.The algorithm improves the quality of the initial solutions and increase the search ability by introducing Chaos initialization and adjustable mode factors; Then,based on specified numbers of UAVs in the virtual antenna array,the EMOALO algorithm is used to design the positions and excitation current weights of UAV nodes to simultaneously reduce the maximum SLL,improve transmission rate,and reduce UAV energy consumption.The experimental results show the effectiveness of the improved mechanism of the algorithm; Finally,simulation experiments are conducted to evaluate the performance of the EMOALO algorithm in unexpected situations,such as single broken UAV,position deviations of UAVs,and imperfect phase synchronization.The experimental results show that the EMOALO still has computational performance in optimizing CB communication performance of UVAA under imperfect conditions. |
