Degree Optimization of Batched Sparse Codes Using Outer Code Block Encoding

To address the issues of repeated decoding and resource waste caused by random batch generation of the outer code in existing outer code block coding schemes for batched sparse (BATS) codes, the optimization of theoretical batch count and dynamic adaptability of BATS codes was systematically investigated based on the outer code block encoding scheme. First, under the condition of a known packet loss rate, a batch consumption analysis model for BATS codes was established, and an optimal degree value computation method was derived to tackle the challenges in existing schemes regarding theoretical batch count calculation and optimal degree value determination for minimizing batch count consumption. Second, for scenarios with unknown packet loss rates in the channel, a reinforcement learning-based dynamic degree optimization method for BATS codes was proposed, enabling real-time acquisition of degree values through an intelligent learning mechanism. Finally, simulation experiments were conducted to evaluate the theoretical model and the proposed dynamic optimization method. Simulation results have shown that the established transmission model based on outer code blocks and its batch count computation formula can be used to calculate batch consumption and determine the optimal degree distribution. Simulation results demonstrate that the proposed reinforcement learning-based optimization scheme achieves lower average batch count consumption than fixed-degree value schemes with unknown packet loss rates and maintains great performance in dynamic channel environments.