A Review of the Design and AI-Driven Optimization Techniques on Patch Antennas for 5G Applications

Marcus Ibekwe, Frederick Alapa, Shamsuddeen Mikail, Stanislaus Ogbuokebe

Abstract


The rapid demand for faster data rates, ultra-low latency, and energy efficiency has accelerated the shift from LTE to 5G networks. A major challenge in 5G communication is multipath fading, which reduces signal strength due to wave propagation around obstacles. To counter this, advanced antenna designs are needed that can enhance coverage, mitigate fading, and meet strict 5G requirements. Modern antenna design must address multiple factors such as gain, bandwidth, radiation patterns, coupling suppression, compactness, fabrication, and cost. Traditional trial-and-error methods are inadequate for these complex optimization needs. Artificial intelligence (AI) and machine learning (ML) now play a crucial role in antenna development by enabling adaptive optimization, accurate prediction, and reduced dependency on costly simulations. This review highlights the challenges of multilayer patch antenna design for millimetre-wave 5G applications and emphasizes how AI-driven techniques are transforming antenna engineering into more adaptive, reliable, and high-performance systems for next-generation wireless communication.


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References


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