Energy-Aware DRL-Based Dual-Perception Fountain Codes for Resource-Constrained UASNs

Fountain codes with online adaptation (OFCs) are promising for underwater acoustic sensor networks (UASNs), since they exploit limited feedback to reduce transmission over head. Yet, the performance of conventional OFCs is severely degraded in UASNs due to high error rates, long propagation delays, and sparse feedback, resulting in poor recovery efficiency and high energy cost. To address these challenges, we propose an energy-aware dual-perception OFC framework driven by deep reinforcement learning (DRL-OFC). In this design, a DRL agent jointly perceives channel dynamics and feedback sparsity to learn an optimal degree distribution that suppresses redundant coding and enhances intermediate decoding. In addition, a feedback aware transmission strategy is developed to cope with the long delay characteristics of UASNs, further reducing unnecessary retransmissions. Simulation results show that DRL-OFC achieves significant gains over existing OFC schemes in terms of decoding efficiency, transmission overhead, recovery performance, and energy consumption, confirming its suitability for resource constrained underwater communications.