Adaptive and Intelligent Antenna Arrays Systems for 5G and Beyond

Adaptive and reconfigurable antenna arrays are central to the evolution of modern wireless communications, enabling dynamic beamforming, spatial selectivity, and advanced interference mitigation for 5G-Advanced and the emerging 6G era. As wireless networks migrate toward higher frequencies, larger apertures, and increasingly dense and heterogeneous environments, the demand grows for antenna solutions that are flexible, multi-band, and resilient to practical challenges such as RF losses, mutual coupling, imperfect switching, and manufacturing inconsistencies.Beyond time-modulated and high-speed switched arrays, the 6G landscape is increasingly shaped by Reconfigurable Intelligent Surfaces (RIS) and Large Intelligent Surfaces (LIS), which extend the concept of adaptive arrays into programmable environments. These technologies promise unprecedented control over propagation channels but also introduce new trade-offs in efficiency, scalability, and hardware complexity.Artificial intelligence and machine learning are becoming indispensable in array design and tuning. Moving beyond surrogate modeling and black-box optimization, approaches such as Physics-Informed Neural Networks (PINNs) and Explainable AI (XAI) are gaining traction. By embedding Maxwell’s equations into the learning process, these methods provide trustworthy synthesis and calibration tools that antenna engineers can rely on for real-world deployment.Equally critical is the co-design of antennas with RFICs/MMICs, where reconfigurable components such as PIN diodes, varactors, and MEMS introduce non-negligible insertion loss and heat. Thermal management in dense arrays is therefore a pressing challenge, requiring innovative solutions that balance performance, reliability, and efficiency.This Research Topic aims to accelerate the development of adaptive and reconfigurable antenna array technologies by bridging theoretical advances, simulation frameworks, and practical implementation. The goal is to encourage submissions that move beyond idealized studies, focusing on designs and methodologies suitable for deployment in realistic 5G-Advanced and 6G platforms. Contributions are sought across domains such as electronically scanned arrays, cognitive radio, MIMO systems, and emerging paradigms including Reconfigurable Intelligent Surfaces (RIS) and Large Intelligent Surfaces (LIS).Manuscripts presenting innovations in array architectures—including time-modulated, switched, and surface-based designs—robust calibration and control schemes, and optimization approaches that explicitly account for hardware limitations, performance trade-offs, and complexity are particularly welcome. Special emphasis is placed on AI/ML-enabled methodologies that enhance rapid design exploration, in-the-field reconfiguration, and uncertainty management. Approaches such as Physics-Informed Neural Networks (PINNs) and Explainable AI (XAI), which embed Maxwell’s equations into the learning process, are strongly encouraged to ensure trustworthy synthesis and calibration.Equally important are studies on the co-design of antennas with RFICs/MMICs, addressing insertion loss, reconfigurable components (PIN diodes, varactors, MEMS), and thermal management in dense arrays. Through this Topic, we aspire to establish best practices, set meaningful standards, and advance practical developments in adaptive and reconfigurable antenna technologies for next-generation wireless communications.This Research Topic is dedicated to the advancement of adaptive and reconfigurable antenna arrays for 5G-Advanced and 6G systems, with particular attention to real-world deployability and innovation. The research focus is bounded by the need for solutions that address both theoretical and practical challenges within actual operating environments. We welcome articles addressing, but not limited to, the following themes:- Advanced architectures for electronically scanned, time-modulated, switched, multi-band/wideband antenna arrays, as well as Reconfigurable Intelligent Surfaces (RIS) and Large Intelligent Surfaces (LIS)- Techniques for beam steering, interference mitigation, and spatial multiplexing at the antenna, array, or surface level- Design solutions for feed-network, RF front-end, and mutual coupling under real-world constraints- Strategies for robust calibration, compensation of losses, and resilience to switching or manufacturing nonidealities- Hardware- and physics-aware AI/ML approaches for array synthesis, tuning, calibration, and real-time reconfiguration, with emphasis on Physics-Informed Neural Networks (PINNs) and Explainable AI (XAI) that respect Maxwell’s equations- Co-design of antennas with RFICs/MMICs, including considerations for insertion loss, reconfigurable components (PIN diodes, varactors, MEMS), and thermal management in dense arraysDr. Saleh is employed by WiSAR (Wireless Sensor Applied Research), a research centre within Atlantic Technological University (ATU). The other Topic Editors declare no conflict of interest. Topic editors: Sahar Saleh, Arun Raj, AVISHEK DAS Source: https://www.frontiersin.org/research-topics/78241/adaptive-and-intelligent-antenna-arrays-systems-for-5g-and-beyond