Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks

Collection of recent arXiv papers (eess/sp) on advances for next‑generation wireless systems and supporting tools. Key topics include: Transformer-enhanced reinforcement learning for resource allocation, security, and long-horizon control in communication networks; data-detection methods for massive MIMO with 1-bit dithered DACs/ADCs and ML/low-complexity detectors; 3D spherical fluid antenna systems for spatially reconfigurable 6G apertures; variational Bayesian sparse CSI estimation and TTD-enabled wideband beamforming for low-resolution THz MIMO; surveys and proposals for wireless-native/fq