Deep reinforcement learning based secondary user transmit power control for underlay cognitive radio networks
Deep reinforcement learning based secondary user transmit power control for underlay cognitive radio networks
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DOI：
10.1145/3538641.3561484
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Kouhei Katou;Xiaoyan Wang;M. Umehira;Yusheng Ji
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文献类型：
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作者：
Kouhei Katou;Xiaoyan Wang;M. Umehira;Yusheng Ji
为了提高频谱利用效率，底层认知无线电网络近年来得到了广泛的研究。如果次要用户对主要用户造成的干扰低于给定阈值，则底层范例允许次要用户进行操作。对于底层认知无线电网络来说，次要用户的发射功率控制问题至关重要且具有挑战性，特别是当网络场景是动态的时。在这项工作中，我们提出了一种基于深度强化学习的底层认知无线电网络二次用户发射功率控制方案。该方案动态控制移动次用户的发射功率，目的是提高系统的频谱利用效率，同时满足主用户所需的SINR（信号干扰加噪声功率比）。所提出的方案在干扰比和吞吐量方面的性能通过大量的仿真得到了验证。
To improve the spectral utilization efficiency, underlay cognitive radio network has been extensively investigated in recent years. The underlay paradigm allows secondary users to operate if the interference they cause to primary user is below a given threshold. The transmit power control problem of the secondary user is critical and challenging for underlay cognitive radio networks, especially when the network scenario is dynamic. In this work, we propose a deep reinforcement learning based secondary user transmit power control scheme for underlay cognitive radio networks. The proposed scheme dynamically controls the transmit power of a mobile secondary user, with the purpose of improving the system's spectral utilization efficiency and meanwhile satisfying the required SINR (signal-to-interference plus noise power ratio) for primary users. The performance of the proposed scheme in terms of interference ratio and throughput is validated by extensive simulations.