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arXiv 2203.02791cs.NI
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中断约束干扰系统中基于深度Q学习的资源分配

Deep Q-Learning Based Resource Allocation in Interference Systems With Outage Constraint

Saniul Alam, Sadia Islam, Muhammad R. A. Khandaker, Risala T. Khan, Faisal Tariq, Apriana Toding

摘要:

该对应关系考虑了链路中断约束下无线干扰信道(IC)中的资源分配问题。由于优化问题本质上是非凸的,因此找到最优功率分配的现有方法是计算密集的,因此实际上是不可行的。最近,深度强化学习在解决复杂性降低的非凸优化问题方面表现出了很好的效果。在这种通信中,我们利用深度Q学习(DQL)方法,该方法与无线环境交互,并学习无线IC的最佳功率分配,同时最大化系统的总速率并保持每个链路的可靠性要求。我们使用了两个独立的深度Q网络来消除学习过程中固有的不稳定性。仿真结果表明,所提出的DQL方法优于现有的基于几何规划的解决方案。

英文摘要:

This correspondence considers the resource allocation problem in wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in nature, existing approaches to find the optimal power allocation are computationally intensive and thus practically infeasible. Recently, deep reinforcement learning has shown promising outcome in solving non-convex optimization problems with reduced complexity. In this correspondence, we utilize a deep Q-learning (DQL) approach which interacts with the wireless environment and learns the optimal power allocation of a wireless IC while maximizing overall sum-rate of the system and maintaining reliability requirement of each link. We have used two separate deep Q-networks to remove the inherent instability in learning process. Simulation results demonstrate that the proposed DQL approach outperforms existing geometric programming based solution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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