Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1023-1028· 0 citations· 26 references
Abstract
Adaptive routing techniques that go beyond the constraints of conventional algorithms are required due to the extraordinary increase in network traffic. The advancements in deep reinforcement learning (DRL) techniques enforce the dynamic learning of the paths in network. This paper presents a hybrid DRL based Graph Neural Network combined with Soft Actor Critic(GNN-SAC) method in a dynamic Software Defined Network(SDN) environment using M/M/1 queuing model. Its performance is compared with other four DRL techniques-Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC). The experimental results demonstrate that the hybrid GNN-SAC achieves the best path cost of 4, highest throughput of 25.18 Mbps, and lowest packet loss of 6.02\%, whereas SAC achieves the lowest delay of 14.74 ms and jitter of 1.16 ms, however, A2C achieves the fastest training time of $0.23 s$ and best link utilization of 45.58\%. This platform also provides real-time animated packet routing visualization with live failure simulation capabilities.
Results affirm that the combination of structural learning, adaptive decision‐making, and automated evaluation on a cloud platform offers a realisable, scalable route to intelligent, autonomous, programmable network management that can be used in next‐generation communication infrastructures.
Muhammad Hasnain, Faisal Naeem, Imran Ghani· Applied AI Letters· 0 citations
A path selection model that combines bottleneck link usage and reinforcement learning that achieves superior state awareness and adaptive routing performance in multi-source heterogeneous networks and hence can be used effectively for intelligent routing in next-generation power communication networks.
Ying Zeng, Xingnan Li, Yubeng Bao et al.· EAI Endorsed Transactions on...· 0 citations
Networks with highly dynamic data transmission demands and network topologies are common in real world. A fundamental problem in such networks is achieving scalable traffic allocation to maximize long-term total throughput under link capacity constraints. However, state-of-the-art (SOTA) works lack scalability. This is primarily due to two reasons in large-scale networks: first, they require solving constrained optimization problems online, which leads to high decision latency; second, they rely on reinforcement learning algorithms for policy optimization, which are inefficient in exploration and challenging to train effectively. To address these issues, we propose the Fast Networked Control (FNC) policy framework, which firstly utilizes parallelizable neural network modules to process the state and generate raw decisions, followed by basic operations such as normalizations and comparisons, which do not require iteration or optimization, to obtain decisions that satisfy the constraints. Hence, FNC policy avoids solving constrained optimization problems and supports parallel execution, significantly reducing decision latency. Furthermore, this policy preserves gradient flow and supports backpropagation, which enable us to design an imitation learning algorithm to efficiently train the policy in an end-to-end manner. Experiments in large-scale networks show that our FNC policy achieves an average 8% improvement in demands satisfaction and 10 times reduction in decision latency versus SOTA works.
Zhaoxing Yang, Guiyun Fan, An-Jie Cao et al.· IEEE Transactions on Network...· 0 citations
This survey delivers the first systematic exploration of how artificial intelligence can bolster SR, from traffic classification and segment-list computation to fast reroute, service-function chaining, and multi-domain orchestration by spanning supervised and unsupervised learning, reinforcement learning, and hybrid pipelines that fuse forecasting, neural optimization, and heuristic search.
Noha W. Hassan, M. Khalil, Hazem M. Abbas· Telecommunications Systems· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
Results highlight the effectiveness and practicality of the proposed HybridRL-RNP framework as an intelligent topology control solution for Wireless Mesh Networks towards 6G, where the synergy between AI-driven optimization and heuristic knowledge plays a pivotal role in achieving globally optimal network connectivity.
Le Huu Binh, Thuy-Van T Duong, Le Duc Huy· IEEE Access· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
