This paper proposes an Integrated Channel Sounding and Communication (ICSC) framework that deeply integrates channel sounding and communication to address limitations of traditional channel modeling in dynamic, complex wireless networks like SAGSIN. ICSC enables real-time acquisition of channel characteristics, intelligent scenario identification, and adaptive waveform optimization to enhance communication performance and establish comprehensive channel model libraries.

This paper introduces Sign-Prioritized Federated Learning (SP-FL), a novel framework that improves wireless federated learning by prioritizing the transmission of critical gradient information, specifically gradient signs. It addresses unreliable communication in resource-constrained wireless networks by implementing an importance-aware hierarchical resource allocation strategy, demonstrating significant accuracy improvements.

This paper introduces CaFTRA, a framework for 6G wireless systems that eliminates real-time channel state information (CSI) feedback in MIMO transmission by predicting CSI from user geolocation using a Transformer network. It also proposes a many-to-one matching algorithm for efficient multi-base station (BS) association and resource block (RB) allocation in fully-decoupled radio access networks (FD-RAN), demonstrating significant improvements in spectral efficiency and user fairness compared to 5G.