A Review of Advanced 5G Antenna Designs Using Intelligent Computational Models

This paper proposes a survey and critical analysis of recent advancements in antenna design for fifth-generation (5G) communication systems. It thoroughly examines the integration of machine learning (ML), deep learning (DL) and intelligent optimization techniques for enhancing antenna performance. Different architectures like Massive MIMO, phased arrays and beam forming frameworks are compared in structural design, operational mechanisms and implementation challenges. A quantitative comparison of various methods shows trends and differences in spectral efficiency, throughput, latency, connectivity and signal quality. The analysis reveals that AI-assisted optimization significantly improves adaptability, interference control and computational efficiency. The study concludes with insights into current limitations and provides directions for future research toward energy-efficient, self-adaptive and intelligence-driven antenna systems for next-generation 5G and beyond networks.