A Deep Model-Based Channel Interference Mitigation for OTFS Signals in ISAC Systems

In recent years, Orthogonal Time Frequency Space Modulation (OTFS) has gained popularity in integrated sensing and communications (ISAC) system due to its robustness against Doppler offset and delay changes. Traditional pilot-based methods for accurate channel parameter estimation are complex and struggle with rapidly changing channel conditions. In this letter, we propose a deep encode-decode network (DED-Net). It uses DL to automatically learn and eliminate channel interference from OTFS signals. The framework employs a deep encoding and decoding network, similar to a filter, learning complex signal features to effectively remove interference. Our experiments demonstrate DED-Net’s ability to eliminate interference in OTFS modulation signals, offering an alternative to pilot-based methods and showcasing DL’s potential for ISAC systems.