Design of 4x4 butler smart antenna array for 5G communication system

This thesis responds to the escalating demand for high-performance wireless communication networks by introducing an innovative antenna system that merges a 4x4 Butler matrix with a 1x4 linear array antenna. This cutting-edge system incorporates machine learning algorithms deployed on an embedded control chip, fostering optimized performance and adaptability to meet the requirements for faster data rates, higher capacity, and improved coverage. The exploration begins with the scrutiny of three distinct antenna designs for comparison and selection to construct the antenna array within the Butler Matrix network. The first design involves a printed Meander Line Antenna (MLA) tailored for LTE mobile terminals, showcasing a bandwidth of 57 MHz, compact dimensions (32 × 28 × 1.1) mm³, and a frequency of 2.5 GHz. The second design features a Multiband Hand-Held Antenna with E-shaped Monopole Feeding, validated through simulation and measurements. The third design adopts an inverted U slot antenna for a 5G antenna array, demonstrating low reflection coefficient, gain, and omni-directional radiation patterns at 28 GHz. Following the individual designs, the multi-beam antenna array is established, achieving matching at 50Ω and high gain with four beams directed at specific angles. The Butler matrix, reduced in size and components, generates four beams with high isolation and transmission coefficients. The final design's compact size (23 mm × 20.5 mm) facilitates integration with other devices, outperforming traditional designs with up to 11 dB gain. The ultimate proposal introduces an intelligent antenna system amalgamating a multi-beam antenna for 5G at 28 GHz, a 4x4 modified Butler matrix, a 1x4 linear array antenna, and the Random Forest Decision algorithm implemented on an embedded control chip. This advanced system allows dynamic beam steering and adaptive beamforming, vital for efficient 5G communication, adapting to network needs based on user demand and environmental factors. Simulation studies and experimental results comprehensively analyze the proposed antenna system, focusing on beamforming, spatial diversity, interference mitigation, and the effectiveness of machine learning algorithms. The system exhibits superiority over conventional antennas, showcasing potential applications in IoT devices, mobile communication networks, and scenarios requiring adaptive antenna systems. The integration of machine learning algorithms on the embedded control chip equips the system to tackle diverse challenges associated with these applications. This work significantly contributes to the ongoing endeavors in miniaturizing Butler Matrix Networks while preserving, and even enhancing, their performance. This work also contributes to addressing a pertinent issue in traditional antenna systems, which often consume substantial power regardless of the receiver's actual requirements. Our modeling approach incorporates innovative power conservation mechanisms. This addition responds to the growing concern of energy efficiency in wireless communication networks. By intelligently adapting power consumption based on the communication scenario and environmental factors, the proposed system aligns with contemporary efforts to create sustainable and eco-friendly communication technologies.

Text FK 2024 34 - Declaration Form.pdf Restricted to Repository staff only Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (846kB) Text FK 2024 34 - Full Text.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) Text FK 2024 34.pdf Restricted to Repository staff only Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) Official URL or Download Paper: http://ethesis.upm.edu.my/id/eprint/18999

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