Dueling-DQN Based Spectrum Sharing Between MIMO Radar and Cellular Networks

Atiquzzaman Mondal, Aparajita Dutta, S. Biswas
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引用次数: 0

Abstract

We investigate a two-tier distributed spectrum sharing framework between a multi-cell multi-user mobile broadband network (MBN) and a multiple-input multiple-output (MIMO) radar. While multi-agent reinforcement learning (RL) is used for transmit power allocation at MBN's base-stations to improve the quality of service of its users subject to the constraint of the probability of detection of the radar, the interference from the radar towards the MBN is mitigated via null-space based waveform projection. In the RL framework, the multiple cells in the MBN operate as agents, and the average signal-to-noise ratio value is the reward. Accordingly, we propose a deep RL network called dueling deep Q-network (DDQN) to enable co-existence by taking into account the physical layer parameters of the MBN and radar communication. The DDQN is compared to two other baseline RL algorithms, namely Q- learning and deep Q-network (DQN). Numerical results show that DDQN learns to obtain the best power allocation policies for distributed spectrum access without needing a centralized controller to control interference towards the radar. In particular, over time, the advantage network of DDQN allows the agent to take actions having higher advantage value, thus leading to faster convergence and a more stable spectrum sharing framework.
基于duelling - dqn的MIMO雷达与蜂窝网络频谱共享
我们研究了多小区多用户移动宽带网络(MBN)和多输入多输出(MIMO)雷达之间的两层分布式频谱共享框架。多智能体强化学习(RL)用于MBN基站的发射功率分配,在受雷达检测概率约束的情况下提高用户的服务质量,通过基于零空间的波形投影来减轻雷达对MBN的干扰。在RL框架中,MBN中的多个单元作为代理运行,平均信噪比值是奖励。因此,我们提出了一种称为dueling deep Q-network (DDQN)的深度RL网络,通过考虑MBN和雷达通信的物理层参数来实现共存。DDQN与另外两种基线RL算法,即Q-学习和深度Q-网络(DQN)进行了比较。数值结果表明,DDQN在不需要集中控制器控制对雷达干扰的情况下,学习得到分布式频谱接入的最佳功率分配策略。特别是随着时间的推移,DDQN的优势网络允许agent采取具有更高优势价值的动作,从而使收敛速度更快,频谱共享框架更加稳定。
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