Jul 2026· Journal of Artificial Intelligence and Technology· 0 citations· 27 references
TL;DR
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.
Abstract
Space–Air–Ground Integrated Networks (SAGINs) have been envisioned to support next-generation communication networks.Due to their heterogeneity and dynamic link characteristics, routing in SAGIN is challenging. Existing shortest-path routing mechanisms do not adapt well to the varying bandwidth and latency, leading to poor quality of service (QoS). In this paper, we propose a deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN. In the proposed model, an agent learns the policy of choosing the suitable path by interacting with the network environment and obtaining rewards. The network is modeled as a weighted graph with delay and bandwidth constraints. We compare our model with a traditional delay minimization baseline over multiple independent runs. Experimental results show that our DRL approach achieves a 6.51% improvement in average throughput and a 29.90% reduction in end-to-end delay compared to the baseline strategy. Statistical analysis confirms the robustness of the delay reduction, highlighting the effectiveness of reinforcement learning in dynamic HetNets. This indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization. Overall, the proposed DRL-based routing framework offers a scalable and intelligent solution for optimizing performance in complex SAGIN architectures, with promising potential for next generation integrated communication systems.
As a core component of future 6G architectures, the Space-Air-Ground-Sea Integrated Network (SAGS) is essential for marine environmental monitoring and emergency communications. However, constrained by scarce energy replenishment and the heterogeneous distribution of marine relay nodes, traditional shortest-path protocols often induce load imbalance and central node congestion, leading to premature failure and network connectivity loss. To address this "energy hole" problem, an Energy-Aware routing framework based on Proximal Policy Optimization (PPO) is proposed. Specifically, a One-Hot encoding mechanism is introduced to reconstruct the network state space, enabling the accurate capture of topological structural features. Furthermore, a composite reward function incorporating an energy penalty term is designed to guide routing decisions toward an optimal balance between path length and residual node energy. Experimental results in a high-fidelity simulation environment characterized by severe energy constraints demonstrate that the proposed algorithm effectively bypasses low-battery nodes while maintaining a 100% Packet Delivery Ratio. Notably, compared to Dijkstra’s algorithm, the proposed method significantly increases the average residual energy of network bottleneck nodes from 45.40% to 66.80%.
Ning Zhou, Xuan He· International Conference on...· 0 citations
Future sixth-generation (6G)-oriented networks require programmable control that can adapt routing to latency and congestion without unsafe online exploration. This study evaluates offline multi-agent deep deterministic policy gradient (MADDPG) with behavior-adjusted training rewards for latency-aware path control in software-defined networking (SDN). Each traffic pair is modeled as an agent selecting one of three retained candidate paths, while centralized critics learn coordinated decisions from topology-specific Ryu–Mininet transition datasets. Nine policies are compared using ten paired seeds on fat-tree, mesh-grid, and WAN-corridors topologies under a deployed utilization–latency weighting of 0.60/0.40, together with flow-completion, latency, congestion, architectural-comparison, sensitivity, robustness, statistical, and controller-overhead analyses. The utilization-aware path heuristic achieves the strongest overall reward ranking. MADDPG is the strongest learned policy on fat-tree, is not significantly outperformed by any evaluated policy on mesh-grid, and remains statistically tied with completion-matched policies on WAN-corridors. Behavior adjustment is topology-dependent rather than uniformly beneficial. The exported policy requires approximately 52μs per joint decision, whereas complete control-loop timing is dominated by network-statistics polling. These results support offline multi-agent SDN control as a competitive, low-overhead option when interpreted jointly with topology structure, flow completion, and strong heuristic baselines.
A. Kyzyrkanov, Y. Nurakhov, Zhenis Otarbay et al.· Technologies· 0 citations
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, Lixia Xiao et al.· IEEE Transactions on Cogniti...· 0 citations
The experiments show that feasibility-aware learning can approach deterministic baseline reliability while retaining learned forwarding capability under hop constraints, and confirm that action masking is the dominant mechanism for maintaining feasible routing decisions, whereas trust mainly provides reliability-aware regularization.
Adeel Iqbal, Muhammad Faisal Siddiqui· Computers, Materials & C...· 0 citations
Future 6G networks will integrate communication and computing capabilities to support intelligent, delay-sensitive services. In heterogeneous cloud-edge environments, however, task offloading and routing decisions are strongly coupled, and dynamic workloads, limited computing resources, and constrained link capacity make efficient service provisioning challenging. Existing reinforcement learning-based offloading methods can improve decision efficiency, but many focus on simplified or single-domain settings and do not adequately account for backbone topology and bandwidth constraints. To address this problem, this paper studies joint task offloading and routing optimization in multi-domain cloud-edge networks, explicitly modeling network topology and link capacity. We propose a cooperative multi-agent deep reinforcement learning method that coordinates distributed edge agents through centralized training and decentralized execution. Routing optimization feedback is further incorporated to guide constraint-aware policy learning. Simulation results demonstrate that the proposed method reduces end-to-end latency, mitigates network congestion, and avoids link and node overload in cloud-edge networks.
Yi Yue, Shuai Zhang, Zhen Han et al.· IEEE International Conferenc...· 0 citations
Objectives: To address dynamic bandwidth allocation with strict Quality of Service (QoS) requirements in Generalized Multi-Protocol Label Switching (GMPLS) optical networks under strain from internet services, real-time multimedia, and cloud infrastructure. Method: A Hybrid Deep Reinforcement Learning (Hyb-DRL) framework combined with the Kookaburra Optimization Algorithm (KkOA) for adaptive weight adjustment is proposed. Dynamically generated input data, including user request rates, queue lengths, server availability, and link stability metrics, were used to simulate real-world traffic. The Hyb-DRL agent learned optimal routing and bandwidth provisioning policies while KkOA optimized model weights for faster convergence and stability. Findings: The simulation results show that the suggested Hyb-DRL-KkOA algorithm performs better than the conventional bandwidth allocation algorithms. In contrast to conventional algorithms, it reduces the blocking probability, makespan, cost, and energy utilization while improving the throughput; therefore, providing enhanced quality of service (QoS). The proposed framework achieves a lower blocking probability by 78%, makespan by 64%, energy consumption by 51%, and operational cost by 47%. In addition to this, it provides better throughput performance by 69% than other conventional techniques. Moreover, it provided a delay of 0.0189 s, minimal energy consumption of 33 mJ, and maximal throughput of 950 Mbps. Novelty: A combination of reinforcement learning and meta-heuristic optimization leads to adaptive decision-making regarding routing and bandwidth allocation in the face of different traffic demands. The performance gain in terms of QoS is due to optimal utilization of network resources with low blocking probability, energy and operational cost. It provides a scalable and adaptive solution for high-speed, reliable data transmission in modern communication networks.
Keywords: GMPLS Optical Networks, Kookaburra Optimization Algorithm, Bandwidth Allocation, Quality of Service (QoS), Blocking Probability
M. Rajagopal, S. Malathi· Indian Journal of Science an...· 0 citations