Optimization Design Of MIMO Antenna

With the rapid development of wireless communication technology,the requirements for network capacity and transmission rate in Mobile Communication System are increasingly stringent.As an indispensable component of modern communication system,the innovation of multi-input and multi-output(MIMO)antenna technology has made a great contribution to alleviate such a problem.However,various applications also have more and more restrictions and requirements of MIMO antenna.Among them,the most important one is the contradiction between the multiplication of the number of antennas and the reduction of the space for antenna arrangement.When multiple antenna elements are arranged in a limited space,it is easy to produce strong mutual coupling.Then the original current distribution of antenna will be changed,which will affect its performance.Therefore,how to obtain MIMO antenna satisfying requirements in limited space quickly and effectively is a very meaningful and valuable work.This paper focuses on the automatic optimization design of MIMO antenna based on intelligent optimization algorithm,then the tedious design procedures of"black imitation"can be avoided as much as possible and new ideas can be provided for MIMO antenna design.And its design complexity and freedom can be improved.Finally,several compact MIMO antennas with good performance have been optimized.The main work of this paper is as follows:1.By analysis of the characteristics and advantages of Multi-objective Evolutionary Algorithm Based on Decomposition(MOEA/D)and Particle Swarm Optimization(PSO)algorithm,a multi-objective optimization scheme MOEA/D-GPSO for continuous parameter optimization is proposed.The effectiveness of above scheme is verified by the general ZDT numerical test functions and the classical LPDA design,especially for tri-objective optimization.Further,considering the practical MIMO antenna design is mostly topology optimization,a binary version MOEA/D-GBPSO,which can be applied for discrete parameter optimization,is proposed with similar strategies.By solving the multi-objective 0-1 knapsack problem and tri-objective optimization design of a compact 4-port MIMO antenna,the performance of MOEA/D-GBPSO is tested.The optimized MIMO antenna can work in a wide band of 1.6～2.9 GHz with good electrical performance.The feasibility of the proposed scheme is illustrated by the optimization design of MIMO antenna.2.The topology,size and location of MIMO antenna decoupling structure always need to be configured simultaneously.That is,the discrete and continuous parameters should be optimized in parallel in the process of MIMO antenna design.Thus,a hybrid parameter multi-objective optimization scheme MOEA/D-M is proposed for such designs,and applied to the decoupling design of compact MIMO antenna successfully.Depending on the same initial antenna reference model,the antenna units and corresponding decoupling topology structure,size and location compact single band and dual band MIMO antennas for WLAN/Wi MAX applications are optimized respectively.The size of optimized single band MIMO antenna and its decoupling structure are17×18 mm~2 and 2×1.6 mm~2 respectively,which can operate in4.78～6.02 GHz;the size of dual band MIMO antenna and its decoupling structure are17×21 mm~2 and 1×3 mm~2,which can operate in 3.30～3.62 GHz and 4.85～5.90 GHz.Moreover,both designs can achieve high isolation and low envelope correlation coefficient(ECC)in the operation bands.3.For 5G mobile phone,it always has high performance requirements for MIMO antenna.Integrating Black Hole(BH)algorithm into MOEA/D,a multi-objective optimization scheme MOEA/D-BH is proposed for high-dimensional parameter optimization.Thus,in the limited space reserved for MIMO antenna,more detailed division of parameter space can be adopted to get better design results.Firstly,a 2-port MIMO antenna working in LTE band 46 is optimized to verify its effectiveness.There are 108 parameters to be optimized and the satisfied designs can be obtained after about 30 iterations.Its corresponding optimization trajectory also shows the superiority of the proposed scheme.Then the design space is divided in more detail and the number of parameters to be optimized is increased to 216.The optimized dual band MIMO antenna can work in LTE bands 42/43/46.The results show that MOEA/D-BH can still maintain good population diversity and optimization capability in the cases of high-dimension parameter optimization.Furthermore,the antenna unit of dual band MIMO antenna is expanded to an 8-port MIMO antenna.Its S-parameters are consistent well with that of 2-port MIMO antenna.Comparing with other similar designs,this design can work in wider bands and maintain comparable electrical and MIMO performance.4.Based on BP neural network(BPNN),a“cheap”surrogate model is proposed to assist the MIMO antenna optimization design.Then the contradiction between the number of iterations and the cost of high-fidelity electromagnetic simulation can be alleviated greatly.The surrogate model can replace the time-consuming electromagnetic simulation process and quickly predict the response of the unknown antenna model in the iterative optimization process.The low-fidelity solution of prediction,i.e.approximate solution,is used to replace the high-fidelity solution obtained by EM simulation.The time cost of MIMO antenna optimization design can be dropped sharply.In order to reduce the training difficultity and improve the prediction accuracy of the BPNN surrogate model,the dimension reduction and re-encoding of the high-dimensional discrete parameters of MIMO antenna are carried out.Further,a dynamic BPNN surrogate strategy is adopted to assist MOEA/D-BH to optimize a single band mobile MIMO antenna,which shows great superiority in saving the time cost in the process of optimization design.