LSTM-based deep reinforcement learning for ISI mitigation in maritime LoRaWAN
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Date
2026-02-28
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
For reliable, long-range, low-power Maritime Internet of Things (MIoT) communication (e.g., vessel tracking,
ocean monitoring, and offshore automation), LoRaWAN offers attractive coverage and energy efficiency. How
ever, sea-surface reflections, wave motion, and platform mobility create time-varying multipath with large delay
spread, which induces inter-symbol interference (ISI) and degrades packet delivery ratio (PDR) and energy
performance. This paper proposes a Long Short-Term Memory (LSTM)-assisted Deep Reinforcement Learning
(DRL) framework LSTM–DDPG Adaptive Modulation and Coding (LD-AMC) that proactively mitigates ISI by
predicting short-term channel evolution and adapting the LoRaWAN physical-layer parameters. An LSTM pre
dictor learns temporal correlations in observed link metrics (RSSI, SNR, PER, and RMS delay spread) and pro
vides one-step-ahead forecasts, which are appended to the agent state. A Deep Deterministic Policy Gradient
(DDPG) controller then selects the spreading factor (SF), coding rate (CR), bandwidth (BW), and transmit power
(P
) within LoRaWAN constraints to maximize a reward that favors reliable delivery and throughput while
penalizing energy cost and ISI severity. MATLAB/Simulink simulations under coastal and offshore two-ray
maritime channels show that LD-AMC reduces ISI-induced symbol errors by up to 58%, improving PDR by up
to 47% and reducing energy per delivered packet by up to 32% compared with standard and enhanced ADR
baselines.
Sustainable Development Goals
SGD-7: Affordable and Clean Energy