Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Similar papers

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,...

Wei-Dan 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 p...

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

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

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.