Physically Consistent Channel Modeling and Signal Processing for Reconfigurable Wireless Systems
Reconfigurable antennas are increasingly integrated into multi-antenna communication systems to exploit large apertures while reducing the hardware complexity, energy consumption, and implementation costs of classical massive arrays. Their reconfigurable electromagnetic (EM) properties, including dynamically varying radiation patterns and state-dependent mutual coupling, challenge the fixed-antenna and decoupled-port assumptions of conventional channel models. This motivates a physically consistent framework connecting Maxwell's equations, circuit theory, and information theory. In this tutorial, we develop a unified framework spanning three coupled dimensions: (i) reconfigurable antenna and transceiver architectures, (ii) physically consistent channel modeling, and (iii) physically consistent signal processing. We first establish a taxonomy covering tunable antennas, reconfigurable transceivers, and emerging array architectures, highlighting their reconfiguration mechanisms and hardware-performance trade-offs. We then develop modeling approaches based on Maxwell's equations, wavenumber-domain representations, multiport network theory, and computational electromagnetics, and use them to construct end-to-end channel and noise models that capture near-field propagation, mutual coupling, and circuit-level impairments. Building on these models, we examine architecture-aware channel estimation, beamforming, data detection, and channel decoding, emphasizing how physical structure reshapes algorithm design and performance-complexity trade-offs. Overall, the tutorial treats physical architecture, channel and noise models, and communication algorithms as coupled components of an end-to-end design, providing a unified foundation for physically consistent reconfigurable wireless systems.