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Optimized Design of Sparse Antenna Array for 2D Subarrays Based on GA-PSO Algorithm and Ambiguity Function.

Jian Yang1, Jian Lu2, Tong Zhu1

  • 1School of Engineering, Rocket Force University of Engineering, Xi'an 710025, China.

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Summary
This summary is machine-generated.

This study introduces an optimal design for sparse antenna arrays in full-phased multiple input multiple output (FPMIMO) systems. The novel approach uses a hybrid genetic algorithm-particle swarm optimization (GA-PSO) and ambiguity functions for efficient array design.

Keywords:
2D subarraysambiguity functiongenetic algorithm–particle swarm optimization (GA–PSO)sparse antenna array

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Antenna Theory

Background:

  • Sparse antenna arrays offer reduced complexity and cost while enhancing aperture.
  • Full-phased multiple input multiple output (FPMIMO) systems benefit from efficient array configurations.

Purpose of the Study:

  • To propose a new optimal design for sparse antenna arrays in FPMIMO operation.
  • To enhance global optimization performance and adaptively adjust iterations.

Main Methods:

  • Utilizing a hybrid genetic algorithm-particle swarm optimization (GA-PSO).
  • Incorporating ambiguity functions for final array determination.
  • Adaptive adjustment of optimization iterations.

Main Results:

  • Demonstrated effectiveness of the proposed GA-PSO algorithm in optimizing sparse antenna arrays.
  • Successful integration of ambiguity functions for precise array configuration.
  • Validation through simulation tests confirming the algorithm's efficacy.

Conclusions:

  • The proposed GA-PSO algorithm provides an effective method for designing sparse antenna arrays in FPMIMO systems.
  • The adaptive iteration adjustment ensures robust global optimization.
  • This design contributes to reduced system complexity and hardware costs.