Thinning of antenna array via adaptive memetic particle swarm optimization

Xiu Zhang,Xin Zhang

doi:10.1186/s13638-017-0968-2

Xiu Zhang, Xin Zhang

Open Access

https://doi.org/10.1186/s13638-017-0968-2

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Abstract

Massive multiple input multiple output antenna array is crucial for the fifth generation wireless communication. Proper antenna array design can reduce interference among different signals and generate desirable beamforming. Sparse antenna array is able to form narrower beam with lower sidelobe than equally spaced antenna array given the same number of array elements. However, determining the position of elements is non-deterministic polynomial-time hard. To effectively solve such problem, this paper proposes adaptive memetic particle swarm optimization (AMPSO) algorithm. The algorithm adaptively tunes algorithmic parameters of particle swarm optimization (PSO). Moreover, crossover operator is added to enhance local exploiting search information of PSO. Sparse antenna array design is modeled as a minimization by thinning method. It is then tackled by the proposed algorithm. In terms of peak sidelobe level, the AMPSO algorithm shows good performance compared with PSO and genetic algorithm.

Highlights

1 Introduction Massive multiple input multiple output antenna array is crucial for the fifth generation wireless communication [1]
The idea is reasonable as memetic with crossover operator can enlarge and promote algorithm’s search; parameter control is an effective way to make the algorithm adapt to different design problems and saves the fine-tuning efforts of users
Sparse antenna array design can be classified to two categories

Summary

Introduction

Massive multiple input multiple output antenna array is crucial for the fifth generation wireless communication [1]. EAs contain genetic algorithm (GA) [16], evolutionary strategies [17], and differential evolution [18] These algorithms simulate the evolution of genetic process of livings. A large number of iterations is needed for standard PSO algorithm to obtain a satisfactory solution [27] This is not acceptable for users as antenna simulation often takes a long time. Standard PSO is modified by adding a crossover operator and a parameter adaptation method. The idea is reasonable as memetic with crossover operator can enlarge and promote algorithm’s search; parameter control is an effective way to make the algorithm adapt to different design problems and saves the fine-tuning efforts of users.

Sparse antenna array and related works

Conclusions