In exploiting large propagation delays in underwater acoustic (UWA) networks, the time-domain interference alignment (TDIA) mechanism aligns interference signals through delay-aware slot scheduling, creating additional idle time for improved transmission at the medium access control (MAC) layer. However, perfect alignment remains challenging due to arbitrary delays. This study enhances TDIA by incorporating power allocation into its transmission scheduling framework across the physical and MAC layers, following the cross-layer design principle. The proposed quasi-interference alignment (QIA) mechanism enables controlled interference on useful signals by jointly optimizing the transmission schedule and power. The formulated optimization problem to maximize network throughput is divided into two sub-problems: one for coarse slot scheduling and another for refining both scheduling and power allocation. The simulation results validate the QIA framework’s superiority over the traditional TDIA and genetic algorithm benchmarks.

Dear Editor, Underwater distributed antenna systems (DAS) are stationary infrastructures consisting of multiple geographically distributed antenna elements (DAEs) which are interconnected through high-rate backbone networks [1]. Compared to centralized systems, the DAS could provide a larger coverage area and higher throughput for underwater acoustic (UWA) transmissions. In this work, exploiting the low sound speed in water, a multi-agent reinforcement learning (MARL)-based approach is proposed to secure underwater DAS against eavesdropping at the physical layer. Specifically, the theoretical secrecy rate is firstly derived for time-slotted UWA networks (UWANs) considering the large propagation delays. Furthermore, we investigate the long-term sum secrecy rate optimization problem under the MARL framework, where each DAE learns its optimal transmission strategy online. Simulation results show that the proposed method achieves higher secrecy performance compared to competing benchmark methods.