Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a...

Introduction Underwater acoustic (UWA) communication systems confront significant challenges due to the unique, dynamic, and unpredictable nature of acoustic channels, which are impacted by low signal-to-noise ratio (SNR), severe multipath propagation, latency, Doppler spread, and a shortage of real-world data. Orthogonal frequency division multiplexing (OFDM) is essential for establishing resilient and reliable data transmiss...