Reliable Transmission of LTP Using Reinforcement Learning-Based Adaptive FEC

TL;DR

This paper introduces a reinforcement learning-based adaptive FEC algorithm for LTP in DTN, significantly improving data transmission reliability and efficiency in interplanetary networks by proactively adjusting coding based on predicted channel conditions.

Contribution

It presents a novel RL-based adaptive FEC method that outperforms static and delay-feedback-based approaches in highly variable deep space channels.

Findings

Matrix decoding failures reduced by at least 2/3

Improved data transmission reliability in simulated interplanetary links

Enhanced coding efficiency by minimizing redundancy during good channel conditions

Abstract

Delay/Disruption Tolerant Networking (DTN) employs the Licklider Transmission Protocol (LTP) with Automatic Repeat reQuest (ARQ) for reliable data delivery in challenging interplanetary networks. While previous studies have integrated packet-level Forward Erasure Correction (FEC) into LTP to reduce retransmission time costs, existing static and delay-feedback-based dynamic coding methods struggle with highly variable and unpredictable deep space channel conditions. This paper proposes a reinforcement learning (RL)-based adaptive FEC algorithm to address these limitations. The algorithm utilizes historical feedback and system state to predict future channel conditions and proactively adjust the code rate. This approach aims to anticipate channel quality degradation, thereby preventing decoding failures and subsequent LTP retransmissions and improving coding efficiency by minimizing redundancy during favorable channel conditions. Performance evaluations conducted in simulated Earth-Moon and Earth-Mars link scenarios demonstrate this algorithm's effectiveness in optimizing data transmission for interplanetary networks. Compared to existing methods, this approach demonstrates significant improvement, with matrix decoding failures reduced by at least 2/3.