用强化学习优化能量采集型认知无线电网络性能
Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning
- 基于深度Q网络决定能量收集或发送，动态选择发射功率
- 结合主用户干扰与环境射频双重能量源，提升平均数据速率
- 适合研究无线能量采集与智能频谱共享的学者参考
本文提出一种强化学习方法，用于优化能量受限的认知无线电网络（CRN）性能。在主用户（PU）存在的情况下，两个次级用户（SU）以共存模式接入授权频段。假设SU发射机为能量受限设备，需通过能量采集来传输信号。为此，我们考虑两种能量来源：主用户传输产生的干扰以及环境射频（RF）能量。SU根据预设阈值决定从主用户还是仅从环境源获取能量，利用时隙开关策略实现对主用户信号的能量采集。基于深度Q网络（DQN），SU在每个时隙中决策是否收集能量或发送数据，并选择最优发射功率以最大化平均数据速率。仿真结果表明，该方法优于基准策略且具备收敛性。
原文摘要 · Abstract (English)
In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs' transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs' messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.
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