2026· IEEE Transactions on Networking· Vol 34, pp. 6214-6229· 0 citations· 45 references
Abstract
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.
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
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
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
This work introduces TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor to allocate a fixed capacity budget across nodes to resist cascades under local load redistribution.
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.
Traffic load balancing is a key near-real-time control function in dense and heterogeneous radio access networks, where uneven and time-varying traffic distributions can severely degrade resource utilisation and quality of service. Within the Open Radio Access Network (O-RAN) architecture, cell individual offset (CIO) control provides an effective mechanism to steer user handovers for load balancing. However, existing rule-based and centralised deep reinforcement learning (DRL) approaches suffer from limited adaptability and poor scalability, due to static assumptions, excessive signalling overhead, and the exponential growth of the joint action space. To address these challenges, this paper proposes a graph-based multi-agent reinforcement learning (MARL) framework with centralised training and distributed execution for CIO-based load balancing in O-RAN. Distributed actors perform decentralised near-real-time CIO control at individual RAN, while a centralised critic exploits graph-structured representations of inter-cell interactions during training. Graph attention mechanisms are employed to capture the heterogeneous and time-varying influence of neighbouring cells, improving learning stability and scalability. Simulation results demonstrate that the proposed approach achieves better performance than existing baselines.
Jinsheng Yuan, Mengbang Zou, Weisi Guo· International Mediterranean...· 0 citations