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首页> 外文期刊>IEEE Transactions on Cognitive Communications and Networking >QoS Driven Channel Selection Algorithm for Cognitive Radio Network: Multi-User Multi-Armed Bandit Approach
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QoS Driven Channel Selection Algorithm for Cognitive Radio Network: Multi-User Multi-Armed Bandit Approach

机译：认知无线电网络的QoS驱动信道选择算法：多用户多武装匪徒方法

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摘要

In this paper, we deal with the problem of opportunistic spectrum access in infrastructure-less cognitive networks. Each secondary user (SU) Tx is allowed to select one frequency channel at each transmission trial. We assume that there is no information exchange between SUs, and they have no knowledge of channel quality, availability, and other SUs actions, hence, each SU selfishly tries to select the best band to transmit. This particular problem is designed as a multi-user restless Markov multi-armed bandit problem, in which multiple SUs collect a priori unknown reward by selecting a channel. The main contribution of the paper is to propose an online learning policy for distributed SUs, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB, achieves at most logarithmic order regret, for a single-user in a first time and then for multi-user in a second time. Moreover, studies on the achievable throughput, average bit error rate obtained with the proposed policy are conducted and compared to well-known reinforcement learning algorithms.
机译：在本文中，我们处理了无基础设施认知网络中的机会频谱访问问题。每个次要用户（SU）Tx都可以在每次传输尝试中选择一个频道。我们假设SU之间没有信息交换，并且他们不了解信道质量，可用性和其他SU动作，因此，每个SU都会自私地尝试选择最佳频段进行传输。该特定问题被设计为多用户不安的马尔可夫多臂匪问题，其中多个SU通过选择信道来收集先验未知奖励。本文的主要贡献是提出了一种针对分布式SU的在线学习策略，该策略不仅考虑了频段的可用性标准，而且还考虑了与感测到的频段上来自相邻小区的干扰功率相关的质量度量。我们还证明了这种名为分布式不安的QoS-UCB的策略，第一次对单用户而言，第二次对多用户而言，最多实现了对数阶后悔。此外，对通过所提出的策略获得的吞吐量，平均误码率进行了研究，并将其与众所周知的强化学习算法进行了比较。

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