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2026

Distributed Routing for LEO Satellite Networks: A Multi-Agent Deep Reinforcement Learning Approach With State Information Lag

Multi-agent deep reinforcement learning (MADRL) offers a promising solution for routing in low Earth orbit (LEO) satellite networks. However, large inter-satellite propagation delays lead to severe state information lag in agent interactions, giving rise to decision biases and degraded routing timeliness. To this end, this paper proposes a distributed routing algorithm named time-aware prediction and dynamic attention routing (TAP-DAR). Specifically, it constructs a delay compensation model that incorporates ephemeris data and queue prediction to generate near real-time neighbor state estimates. In addition, a multi-head attention fusion mechanism considering temporal reliability is designed to achieve adaptive aggregation of asynchronous neighbor states. Simulation results demonstrate that across various constellation configurations and network load conditions, the proposed algorithm achieves a maximum reduction of 16.16% in end-to-end (E2E) latency, an average decrease of nearly 30% in packet loss rate, and a maximum improvement of 19.41% in throughput compared to the baseline. Moreover, it substantially curtails communication overhead by more than 90% relative to the global state flooding mechanism.

Weidan Liu, Tong Liu, Li-Xia Xiao et al. · 0 citations
Open access Aug 2026

TLQ-Geo: a two-level q-learning-based geographic routing protocol for flying ad hoc networks

A two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs, which significantly reduces convergence time and computational overhead and integrates hierarchical decision-making with adaptive reinforcement learning.

Mehdi Hosseinzadeh, Jawad Tanveer, Amir Masoud Rahmani et al. · 0 citations
Conference Jul 2026

Load-Balanced and Congestion-Aware Routing for LEO Laser Satellite Networks Based on Deep Reinforcement Learning

To address load imbalance in low earth orbit (LEO) laser satellite networks (LSN), this paper proposes a deep reinforcement learning (DRL) based routing algorithm, which combines proximal policy optimization (PPO) and K-shortest path (KSP) strategies to transform the large-scale routing problem into a decision-making process over a small set of paths. Simulation results demonstrate that, compared with traditional Dijkstra and random routing algorithms, the proposed algorithm fully exploits network resources, effectively prevents network bottlenecks, and significantly enhances the network’s service-carrying capacity.

Haoxin Li, Junling Yuan, Xu-Hong Li et al. · 0 citations
Review Open access 2026

Topology-Aware Learning for Routing In Satellite–Terrestrial Integrated Networks: A Review of Graph Neural Network and Reinforcement Learning Approaches

This review analyses topology-aware learning-based routing for STINs, concentrating on Graph Neural Networks (GNNs) and hybrid GNN–Reinforcement Learning (GNN–RL) frameworks.

Eyeneka J. Ntuen, A. Obot, K. Udofia et al. · 0 citations
Open access Jul 2026

Deep Reinforcement Learning-Based QoS-Aware Routing Protocol for Space–Air–Ground Integrated Networks

A deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN and indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization.

Nilu Mishra, Sanakat Bhanjan Prusty, Sachin Sharma · 0 citations
Open access Jul 2026

Optimized Multi-Path Relay Node Selection Using Deep Reinforcement Learning for Reliable and Energy-Efficient MANET Routing

Mobile Ad-hoc Network (MANET) are highly dynamic and infrastructure-less wireless network in which frequent topology changes, node mobility, packet collision, and energy constraints significantly affect routing performance and network reliability. This research suggests a Deep Reinforcement Learning (DRL)-based Optimized Multi-Path Relay Node Selection method for dependable and energy-efficient MANET routing. The suggested method takes into account important network metrics such as residual energy, node mobility, link stability, congestion level, and packet collision probability in order to intelligently choose the best relay nodes and different routing options using a Deep Q-Network (DQN)-based learning model. In comparison to traditional MANET routing protocols, simulation results show that the suggested DRL-based relay node selection technique greatly improves network lifetime, Packet Delivery Ratio (PDR), throughput and routing stability while lowering packet collision, end-to-end delay and energy consumption.

A. A. Samathu, G. Ravi, A. R. Mohamed Shanavas · 0 citations

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