Chaos-Enhanced Whale Optimization Algorithm for Smart Beam Steering in Phased Array Antennas

Abstract

Enhancing signal quality, minimizing interference, allowing high-speed data transmission, and beam steering in phased array antennas are pivotal in today’s wireless communication systems.  In terms of global optimality, low side lobe levels, and fast convergence, conventional optimization methods often fail to deliver. The ease and adaptability of metaheuristic algorithms, such as the Whale Optimization Algorithm (WOA), have rendered them desirable solutions for managing such complex optimization problems. However, traditional WOA may still suffer from premature convergence and failure to exploit the search space fully.  This study proposes a Chaos-Enhanced Whale Optimization Algorithm (CWOA) in intelligent beam steering for phased array antennas. A significant enhancement is that the algorithm can now escape local optima more effectively and attain higher global convergence due to the integration of chaos theory into the WOA framework.  Chaotic maps such as the logistic and tent maps generate dynamic control parameters that guide the search for a more balanced exploration-exploitation trade-off. To reduce side lobe levels and give accurate main lobe steering, the proposed CWOA is employed to enhance phase excitation of the antenna elements in a uniform linear array (ULA). Beam direction accuracy, side lobe suppression, and convergence speed are three domains where the CWOA performs better in simulations than normal WOA, PSO, and GA.  By comparing various approaches, we realize that chaotic integration significantly enhances the performance of WOA in adaptive beamforming and real-time applications. Innovative and self-adaptive beam steering in dynamic environments like 5G, radar, and satellite communications can be built on this research’s efficient and resilient optimization framework for next-generation antenna systems.