用图强化学习优化5G网络负载，显著降低服务中断风险。
Graph Reinforcement Learning for QoS-Aware Load Balancing in Open Radio Access Networks
- 基于图神经网络的强化学习框架，自动处理基站与用户设备关系。
- 减少53%服务质量违规，最优用户速率提升4倍。
- 适合研究无线网络调度或智能资源分配的开发者。
下一代无线网络需为新兴应用提供无与伦比的服务质量（QoS），要求在链路层数据速率等方面具备严格保障。实现这一目标的关键挑战在于防止小区拥塞，需通过负载均衡确保每小区有足够的无线资源服务其指定用户设备（UE）。本文提出一种新型面向QoS的负载均衡（LB）方法，在多频段开放无线接入网（O-RAN）中优化保证比特率（GBR）和尽力而为（BE）流量的性能，同时满足QoS与资源约束。该方法基于图强化学习（GRL），将负载均衡建模为马尔可夫决策过程，状态以图形式表示，并在状态表征和奖励设计中融合QoS考量。采用离策略双值深度Q网络（DQN）训练代理，结合图神经网络架构。该设计使策略对节点（用户或小区）顺序无关，适应不同规模网络，并能捕捉负载决策中的空间依赖性。与两种基线方法相比，实验显示该方案在性能上取得显著提升：QoS违规减少53%，BE流量第5百分位速率提升四倍。
原文摘要 · Abstract (English)
Next-generation wireless cellular networks are expected to provide unparalleled Quality-of-Service (QoS) for emerging wireless applications, necessitating strict performance guarantees, e.g., in terms of link-level data rates. A critical challenge in meeting these QoS requirements is the prevention of cell congestion, which involves balancing the load to ensure sufficient radio resources are available for each cell to serve its designated User Equipments (UEs). In this work, a novel QoS-aware Load Balancing (LB) approach is developed to optimize the performance of Guaranteed Bit Rate (GBR) and Best Effort (BE) traffic in a multi-band Open Radio Access Network (O-RAN) under QoS and resource constraints. The proposed solution builds on Graph Reinforcement Learning (GRL), a powerful framework at the intersection of Graph Neural Network (GNN) and RL. The QoS-aware LB is modeled as a Markov Decision Process, with states represented as graphs. QoS consideration are integrated into both state representations and reward signal design. The LB agent is then trained using an off-policy dueling Deep Q Network (DQN) that leverages a GNN-based architecture. This design ensures the LB policy is invariant to the ordering of nodes (UE or cell), flexible in handling various network sizes, and capable of accounting for spatial node dependencies in LB decisions. Performance of the GRL-based solution is compared with two baseline methods. Results show substantial performance gains, including a $53\%$ reduction in QoS violations and a fourfold increase in the 5th percentile rate for BE traffic.
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