Publication

Abstract : In wireless communication systems, beamforming techniques focus on enhancing signal reception or transmission by directing energy toward a specific target. Recently, the application of machine learning architectures has revolutionized the calculation of optimal beamforming weights and phase settings by leveraging algorithms to adapt to input channel conditions. This paper introduces a microcontroller-based phase shifter that controls the beam of an antenna array composed of four individual antennas. Each antenna receives inputs from a programmable phase shifter integrated circuit (IC). The microstrip patch antenna setup is designed to resonate in the ISM/Wi-Fi band (2.40-2.48 GHz) ensuring proper feed and maintaining good isolation among the ports. The microcontroller-based design manages the control voltage of each phase shifter IC and generates progressive phase shifts. The paper also includes a comprehensive overview of machine learning-based optimization techniques in beamforming, discussing training strategies, model architectures, and performance evaluation metrics. This approach overcomes the limitations of traditional electromechanical phased array systems in terms of cost, weight, size, power consumption, and complexity. This design enhances the practicality of phased-array antennas for widespread use in society, facilitating high-speed data transmission, improved coverage, and increased network capacity. By combining the potential of machine learning with beamforming techniques, wireless communication systems can achieve higher gains, better coverage, and enhanced adaptability. The paper further explores the potential benefits and challenges of these advanced machine-learning approaches in optimizing beamforming for various applications. Experimental results validate the effectiveness of these machine learning-based methods. Finally, the paper discusses future research directions and outlines various practical applications.