Optimized hyper beamforming of linear antenna arrays using collective animal behaviour

Area of Science:

• Antenna Theory and Design
• Optimization Algorithms
• Signal Processing

Background:

• Hyper beamforming in linear antenna arrays is crucial for directional signal transmission.
• Conventional optimization methods like RGA, PSO, and DE have limitations in achieving optimal hyper beam patterns.
• Sidelobe level (SLL) and first null beam width (FNBW) are key performance metrics for antenna arrays.

Purpose of the Study:

• To introduce and evaluate a novel optimization technique based on Collective Animal Behavior (CAB) for hyper beamforming.
• To compare the performance of CAB against established algorithms (RGA, PSO, DE) for antenna array design.
• To demonstrate the efficacy of CAB in optimizing current excitation weights and interelement spacing for improved antenna performance.

Main Methods:

• Development of a novel optimization technique mimicking Collective Animal Behavior (CAB).
• Application of CAB to optimize current excitation weights and uniform interelement spacing for linear antenna arrays.
• Comparative analysis of CAB with Real Coded Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE).

Main Results:

• CAB achieved significant reductions in Sidelobe Level (SLL) and First Null Beam Width (FNBW) compared to RGA, PSO, and DE.
• The proposed CAB algorithm demonstrated the ability to find near global optimal solutions.
• Optimization efficacy was validated across 10-, 14-, and 20-element linear antenna arrays.

Conclusions:

• Collective Animal Behavior (CAB) offers superior performance for hyper beamforming optimization in linear antenna arrays.
• CAB provides a more effective approach to minimizing SLL and FNBW than conventional optimization techniques.
• The study establishes CAB as a promising algorithm for advanced antenna array design.