• Saptarshi Majumder
• Arka Rajak
• Soumyadeep Mukherjee
• Rup Mondal
• Souvik Singha

This study offers a thorough analysis of artificial intelligence (AI) methods used to optimise antenna designs for 5G and the soon-to-be 6G wireless networks. Antenna design complexity has increased dramatically as wireless communication systems have developed due to the demand for larger data rates, reduced latency, and increased reliability. Conventional design techniques frequently fall short in tackling the difficulties presented by cutting-edge technologies like reconfigurable intelligent surfaces (RIS), beamforming systems, and huge multiple-input multiple-output (MIMO) arrays. The creation of antennas with improved performance, efficiency, and flexibility has been made possible by artificial intelligence (AI), especially machine learning (ML) methods like deep learning (DL) and evolutionary computation. The optimisation of enormous MIMO arrays, which are essential to 5G networks, has shown great success with AI-driven approaches. Researchers can maximise spectral efficiency and reduce mutual coupling and interference by optimising the placement, orientation, and configuration of antenna parts using deep learning. Beamforming systems have also been transformed by AI, where techniques like deep reinforcement learning (DRL) allow for real-time radiation pattern optimisation to adjust to changing network conditions. With algorithms that optimise the arrangement of RIS parts to dynamically alter electromagnetic waves, AI has also helped reconfigurable intelligent surfaces, a crucial 6G technology. AI in antenna design has made great strides in practice, and there are already tools and frameworks available to incorporate AI into current processes. By optimising mmWave and terahertz (THz) antennas, ultra-dense networks (UDNs), and other intricate systems, these technologies have decreased the time and expense associated with design. Nonetheless, there are still issues, such as the requirement for sizable, superior datasets and the interpretability of AI algorithms. Future studies should focus on refining data production methods, increasing model transparency, and combining AI with cutting-edge technologies like improved materials and quantum computing.

Artificial Intelligence (AI), Antenna Design Optimization, 5G Wireless Networks,6G Wireless Networks, Machine Learning (ML), Deep Learning (DL)Deep Reinforcement Learning (DRL).

"AI-Optimized Antenna Design for 5G/6G Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.f276-f281, May-2025, Available :http://www.jetir.org/papers/JETIR2505630.pdf

"AI-Optimized Antenna Design for 5G/6G Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppf276-f281, May-2025, Available at : http://www.jetir.org/papers/JETIR2505630.pdf