频谱感知次序的在线最优选择

动态频谱接入是解决无线电频谱资源短缺和频谱使用效率低下问题的有效方法,它允许次级用户在授权频谱空闲时动态地接入,以进行数据传输。而频谱感知是实现动态频谱接入的关键挑战之一。由于次级用户的感知能力有限,为了获得更多的频谱接入机会,需要尽快找到频谱空闲概率最大的频段,并研究频谱感知次序问题。考虑到频谱空闲概率对次级用户是不可知的,并且会随时间变化,提出了在线学习框架,把频谱感知次序问题归纳成经典多摇臂赌博机问题,并利用在线学习方法——满意折现汤普森抽样算法处理优化问题。仿真结果表明,和其他算法相比,所提算法可以获得更多的频谱接入机会并且能够跟踪频谱空闲概率的变化。

Online optimal selection of spectrum sensing order

Dynamic spectrum access is deemed as an effective solution to the radio spectrum scarcity and spectrum usage in efficiency problem, which allows secondary users to access the spectrum dynamically for data transmission when the licensed spectrum is idle. However, spectrum sensing is one of the key challenges for dynamic spectrum access. Since the secondary user was equipped with limited sensing capability, in order to obtain more spectrum access opportunities, the spectrum sensing order problem was investigated to find the frequency band with the highest probability of being idle as soon as possible. Considering that the probability of the spectrum being idle was not available for the secondary users and changes over time, an online learning framework in which the spectrum sensing order problem was formulated as a classical multi-armed bandit problem was proposed, and it was addressed by using an online learning method, referred to as satisficing discounted Thompson sampling. Simulation results indicate that compared with other algorithms, the proposed algorithm yields more spectrum opportunities and can track the changes of the probability of the spectrum being idle.