mlsanityData to 14 Oct 2026

Reliable Transmission of LTP Using Reinforcement Learning-Based Adaptive FEC

arXiv 2506.22470 ↗First posted 19 Jun 2025cs.NI cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 45 references that are in the map. First posted on arXiv 19 Jun 2025.

Builds on

3 of 45 references.

References · 3

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  3. 2022Future Space Networks: Toward the Next Giant Leap for Humankindcited by 7

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks
  2. 2026Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems
  3. 2025Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning
  4. 2026Queue-Aware and Resilient Routing in LEO Satellite Networks Using Multi-Agent Reinforcement Learning ↗
  5. 2025Deep Reinforcement Learning-Based Cooperative Rate Splitting for Satellite-to-Underground Communication Networks ↗
  6. 2024Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning
  7. 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
  8. 2026Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems