DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction

This document is an arXiv feed (multiple new submissions) focused on wireless communications, physical-layer methods, and ML-driven communications. Key contributions include: DeepOFW — a deep-learning framework for OFDM-flexible waveform design that reduces PAPR while preserving low-complexity transceiver hardware; analyses and architectures for channel prediction (digital-twin-assisted GPs) and THz wireless data-center DTs; a three-color Rydberg atomic quantum receiver (3C5L-RAQR) improving sensitivity and low-frequency detection; RIS-assisted distributed MIMO for energy-efficient 6G indoor U