Yau Kok-Lim Alvin, Poh Geong-Sen, Chien Su Fong, Al-Rawi Hasan A A
Faculty of Science and Technology, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 46150 Petaling Jaya, Selangor, Malaysia.
University Malaysia of Computer Science & Engineering, Jalan Alamanda 2, Presint 16, 62150 Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia.
ScientificWorldJournal. 2014;2014:209810. doi: 10.1155/2014/209810. Epub 2014 Jun 5.
Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR.
认知无线电（CR）使非授权用户能够利用授权频谱中未充分利用的频谱，同时将对授权用户的干扰降至最低。强化学习（RL）作为一种人工智能方法，已被应用于使每个非授权用户能够观察并执行最优动作，以在认知无线电的各种方案（如动态信道选择和信道感知）中提高性能。本文在认知无线电网络的背景下对强化学习进行了新的探讨。它广泛回顾了大多数方案是如何通过状态、动作和奖励表示，使用传统和增强型强化学习算法来实现的。强化学习增强的例子（传统强化学习方法中未出现）包括规则和协作学习。本文还回顾了强化学习算法带来的性能提升以及未解决的问题。本文旨在奠定基础，以激发该领域的新研究兴趣。我们的讨论以教程的方式呈现，以便对强化学习和认知无线电专业以外的读者来说具有全面性。
