Digital beamforming using evolutionary optimization algorithms

• Main Author: Yao, Qiang
• Other Authors: Lu Yilong
• Format: Final Year Project
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/71568
• Institution: Nanyang Technological University

Digital Beamforming (DBF) is a critical and important technology in different kinds of areas such as modern radar and wireless communication systems. With DBF, multiple adaptive beams can be flexibly formed to enhance signals and suppress interferences. Due to the complexity of digital beamforming problem, new and promising methods should be applied to solve beamforming problem fast and accurately. With the development of Information Technology, computers have a higher capacity of calculations, hence, Evolutionary Algorithms (EA) can be used to simulate and solve the problem. The general mechanism of Evolutionary Algorithm is to imitate the biological evolution and find the optimized solution for the specific problem. In Digital Beamforming problem, some evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been applied widely already. However, the performance of such conventional Evolutionary Algorithms is generally slow while Bat Algorithm (BA) is a relatively new and faster algorithm. In this paper, the digital beamforming fundamentals will be analyzed firstly, then the two Evolutionary Algorithms (PSO and BA) mentioned before will be applied to solve a few types of beamforming designs. A comparison study will be conducted to investigate effectiveness and efficiency of these evolutionary algorithms for digital beamforming designs. Numerical experiments show that both evolutionary algorithms can solve beamforming problem, while BA is much more efficient and effective than the other. The overall results show that, for adaptive beamforming in antennas and microwave applications, BA is a useful and promising tool.