Multi-radio access technologies (RATs) networks, where various heterogeneous networks (HetNets) coexist, are in service nowadays and considered a main enabling technology for future networks. In such networks, managing radio resources is challenge. In this letter, we address the problem of RATs-edge devices (EDs) association and joint power and bandwidth allocation in multi-RAT multi-homing HetNets. The problem is formulated as mixed-integer non-linear programming, whose objective is to cost-effectively maximize the network constrained sum-rate. Due to the high complexity of the problem, we propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve it. Simulation results show that our proposed scheme efficiently learns the optimal policy and enhances the network sum-rate by 80.95% compared to key benchmarks.

中文翻译：

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基于 DRL 的联合 RAT 关联、未来 HetNet 的功率和带宽优化


多种异构网络（HetNet）共存的多无线接入技术（RAT）网络现已投入使用，并被认为是未来网络的主要使能技术。在此类网络中，管理无线电资源是一项挑战。在这封信中，我们解决了多 RAT 多宿主异构网络中 RAT 与边缘设备 (ED) 关联以及联合功率和带宽分配的问题。该问题被表述为混合整数非线性规划，其目标是经济有效地最大化网络约束总速率。由于问题的复杂性很高，我们提出了一种基于多智能体深度强化学习（DRL）的方案来解决它。仿真结果表明，我们提出的方案有效地学习了最优策略，并且与关键基准相比，网络总和率提高了 80.95%。