异构蜂窝网络中基于深度强化学习的博弈论用户关联与资源分配编排
Deep Reinforcement Learning Orchestration of Game-Theoretic User Association and Resource Allocation in HetNets
AI总结：
本文针对异构蜂窝网络中动态用户关联与资源分配的挑战，提出一种双层框架，通过分布式多目标非合作博弈结合深度强化学习控制器编排，提升了网络吞吐量并保持稳定性能。
AI中文摘要：
管理现代异构蜂窝网络（HetNets）中的动态用户关联与资源分配（UARA）仍是关键的开放挑战。现有数学优化和强化学习方法在动态流量条件下处理低延迟决策时存在局限。本文提出一种针对HetNets中博弈论UARA的新型编排方案。所提出的双层框架将UARA决策通过多目标非合作博弈分配给用户设备；在分布式博弈之上，一个集中式深度强化学习控制器通过动态配置博弈的效用参数来编排网络性能，实现功率感知、覆盖增强与均衡运行之间的切换。在符合3GPP TR 38.901信道建模的城市HetNet拓扑上评估时，该框架能紧密逼近所考虑操作目标的最优策略，同时比传统关联方法提供更高的网络吞吐量，且计算开销低，在评估的流量密度下无需重新训练即可保持稳定性能。
英文摘要：
Managing dynamic User Association and Resource Allocation (UARA) in modern Heterogeneous Cellular Networks (HetNets) remains a critical open challenge. Existing mathematical optimization and Reinforcement Learning approaches face limitations in handling low-latency decision-making under dynamic traffic conditions. This paper introduces a novel orchestration scheme for game-theoretic UARA in HetNets. The proposed bilevel framework distributes UARA decisions to User Equipment through a multi-objective non-cooperative game. Overlaying the distributed game, a centralized Deep Reinforcement Learning controller orchestrates network performance by dynamically configuring the game's utility parameters, enabling transitions between power awareness, coverage enhancement, and balanced operation. Evaluated on urban HetNet topologies with 3GPP TR 38.901-compliant channel modeling, the proposed framework closely approximates the optimal policy for the considered operational objectives, while delivering higher network throughput than conventional association methods. Furthermore, it incurs low computational overhead and maintains stable performance across the evaluated traffic densities without retraining.
