DRL-Based Fountain Codes for Concurrent Multipath Transfer in 6G Networks

Concurrent multipath transfer (CMT) has greatly potential to significantly improve the end-to-end throughout with its multihoming property. However, due to the extremely high unpredictability of 6G heterogeneous networks, the receive buffer blocking problem seriously degrades the overall transmission reliability. To address this problem, this paper proposes a learning-based fountain codes for CMT (CMT-FC) scheme to mitigate the negative influence of the path diversity for 6G heterogeneous networks. Specifically, we first formulate a multidimensional optimal problem to mitigate receive buffer blocking phenomenon and improve the transmission rate with requirement constrains. Then, we transform the data scheduling and redundancy coding rate problem into a Markov decision process, and propose a deep reinforcement learning (DRL)-based fountain coding algorithm to dynamically adjust data scheduling policy and redundancy coding rate. Extensive experiments indicate the proposed algorithm mitigates the packet out-of-order problem, and improves the average throughput compared with traditional multipath transmission scheme.