Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks

Yong Zhang, Canping Kang, Yinglei Teng, Sisi Li, Weijun Zheng, Jinghui Fang
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引用次数: 6

Abstract

In this study, a deep reinforcement learning (DRL) method was employed to solve the joint optimization problem for user association, resource allocation, and power allocation in heterogeneous networks (HetNets), which is an NP-hard problem. Existing studies have taken various optimization objectives into account. The heterogeneous network-deep-Q- network frame-work (HetDQN) is proposed to solve this type of optimization problem in HetNets. Based on maximum spectral efficiency, we designed a 6- layer deep neural network. The state space, objective function, and reward function are presented. In comparison with the existing solution, HetDQN can achieve a higher spectral efficiency. The simulation results revealed that HetDQN has better performance in term of convergence.
异构网络中联合资源分配的深度强化学习框架
本研究采用深度强化学习(DRL)方法解决异构网络(HetNets)中用户关联、资源分配和功率分配的联合优化问题,这是一个NP-hard问题。现有的研究考虑了各种优化目标。为了解决这类优化问题,提出了异构网络-深度q网络框架(HetDQN)。基于最大频谱效率,我们设计了一个6层深度神经网络。给出了状态空间、目标函数和奖励函数。与现有方案相比,HetDQN可以实现更高的频谱效率。仿真结果表明,HetDQN在收敛性方面具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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