Spectrum Access In Cognitive Radio Using a Two-Stage Reinforcement Learning Approach
Spectrum Access In Cognitive Radio Using a Two-Stage Reinforcement Learning Approach
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DOI：
10.1109/jstsp.2018.2798920
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发表时间：
2018-02-01
影响因子：
7.5
通讯作者：
Kalyani, Sheetal
中科院分区：
文献类型：
--
作者：
Raj, Vishnu;Dias, Irene;Kalyani, Sheetal
随着第五代无线标准的出现以及对更高吞吐量的需求的增加，提高无线系统的频谱效率的方法已经变得非常重要。在认知无线电的上下文中，如果次级用户可以做出关于感测哪个信道以及何时或多久感测一次的智能决策，则吞吐量的大幅增加是可能的。在这里，我们提出了一种算法，不仅选择一个信道进行数据传输，但也预测多久的信道将保持空闲，使花费在信道检测的时间可以他最小化。我们的算法学习在两个阶段-一个强化学习方法的通道选择和贝叶斯方法，以确定持续时间的感知可以跳过。与其他方法的比较，通过广泛的模拟。我们表明，感测操作的数量最小化，可以忽略不计的主用户干扰的增加，这意味着较少的能量是由次要用户在感测中花费的，也更高的吞吐量是通过节省在感测上花费的时间来实现的。
With the advent of the fifth generation of wireless standards and an increasing demand for higher throughput, methods to improve spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, we propose an algorithm to not only select a channel for data transmission, but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can he minimized. Our algorithm learns in two stages-a reinforcement learning approach for channel selection and a Bayesian approach to determine the duration for which sensing can be skipped. Comparisons with other methods are provided through extensive simulations. We show that the number of sensing operations is minimized with negligible increase in primary user interference; this implies that less energy is spent by the secondary user in sensing, and also higher throughput is achieved by saving the time spent on sensing.