Research On Bio-inspired Computing Method For Performance Optimization Of Wireless Communications

With the wireless communication system developed rapidly,the performance requirements of all kinds of users are increasing.The application of array antenna in wireless communication system can not only improve the system capacity and gain effectively,but also improve the communication energy efficiency significantly.Among array antenna optimization problems,the radiation pattern optimization is usually considered as the main problem.However,due to this problem is a complex nonlinear optimization problem,a large overhead will be incurred during the process to solve that by traditional methods.The introduction of array antenna and collaborative beamforming in wireless sensor networks(WSNs)can greatly improve communication distance and energy efficiency of a single node.However,the location errors caused by the random distribution of nodes in WSNs,will generate the high sidelobe level(SLL)of the beam pattern,and then directly affects the communication performance of the system.In addition,energy limitation of sensor nodes is also a significant problem of WSNs.Wireless charging technology based on unmanned aerial vehicle(UAV)can supplement the energy of nodes and effectively improve the life cycle of traditional WSNs.However,UAV itself will also face the problem about limited energy,thus,how to schedule the charging UAV(CUAV)is the key issue in the rechargeable wireless sensor networks(WRSNs)based on UAV.Therefore,this paper focuses on three problems: array antenna pattern optimization in wireless communication system,collaborative beamforming(CB)optimization in WSNs based on virtual array antenna and CUAV scheduling optimization in WRSNs.Moreover,the bio-inspired computing methods suitable for solving the above problems are designed respectively.The main contributions and innovations are as follows:1.Array antenna pattern optimization method based on different geometric models(1)A biogeography-based optimization based on improved migration and adaptive mutation strategy(BBOIMAM)is proposed to solve the single objective optimization problems on pattern of linear antenna array(LAA),circular antenna array(CAA)and random antenna array(RAA).BBOIMAM algorithm improves the local and global search ability of standard biogeography-based optimization algorithm(BBO)by introducing generalized sinusoidal migration model,elite learning migration strategy and adaptive mutation strategy based on spring vibration.The effectiveness of the proposed algorithm is verified by the experiments on CEC2017 and CEC2020 standard test function sets.Secondly,based on BBOIMAM,the optimization methods of array element excitation current in antenna array of different geometric models are designed to suppress the maximum sidelobe level(SLL)of different antenna arrays beam pattern.The experimental results show the effectiveness of the algorithm.(2)A multi-objective pattern optimization problem MBPOP aiming at optimizing the SLL and NULL of antenna array is constructed,and an improved multi-objective evolutionary algorithm based on decomposition(IMOEAD)is proposed to solve the proposed optimization problem.The diversity and global search ability of the standard algorithm are improved by introducing normal distribution crossover(NDX),Lévy flight and optimal solution selection mechanism.The effectiveness of the proposed algorithm is verified by the standard multi-objective test function set ZDT.Finally,the multi-objective optimization method on SLL suppression and NULL control of LAA based on IMOEAD algorithm is given,and its effectiveness is also verified.2.Performance optimization method of CB in WSNs based on virtual antenna array(1)For WSNs with static sensor nodes,a joint SLL suppression method JSSA based on virtual concentric circles antenna array(CCAA)is proposed.First,JSSA gives the calculation method for the location of the energy optimal array nodes.Second,the method shows that how to use the CCAA as the guide array to select the nodes of the VNAA.Third,an improved chicken swarm optimization(VPCSO)algorithm is proposed to optimize the excitation current of the selected array nodes.VPCSO algorithm introduces location learning mechanism and mutation mechanism into the traditional chicken swarm optimization(CSO)algorithm.The former is used to improve the local search ability,and the latter is used to improve the global search ability.Finally,the performance,energy consumption analysis and electromagnetic simulation analysis of JSSA based on VPCSO algorithm are given.(2)For WSNs with mobile nodes,a distributed collaborative beamforming(DCB)multi-objective joint optimization problem is constructed to optimizes the maximum SLL,excitation current and mobile energy consumption of DCB nodes,simultaneously.Secondly,a distributed parallel cuckoo search algorithm(DPCSA)is also proposed.The algorithm introduces cluster distribution,parallel computing and global cooperative update mechanism,and then the effectiveness of the proposed algorithm is verified by the CEC2017 standard test function set.Finally,the optimization method of DCB joint optimization problem based on DPCSA,and its effectiveness and stability verification are given.3.Charging performance optimization method of CUAV in WRSNAiming at the charging efficiency optimization in WRSN,the deployment optimization problem of CUAVS(CUAVDOP)is constructed.The goal of CUAVDOP is to jointly increase the number of sensor nodes within the charging range of CUAVS,maximize the charging efficiency between sensor nodes and CUAVS,and minimize the total motion energy consumption of CUAVS.Secondly,a new improved firefly algorithm(IFA)is proposed.The algorithm introduces opposition-based learning model,attraction model and adaptive step operator to make it more suitable for solving the proposed optimization problem.Thirdly,the optimization method of CUAVDOP based on IFA is given.Finally,the analysis about maximum charging distance and application scenario of charging with CUAVS in WRSN are given.