arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1804.04048cs.NIcs.ITcs.LGmath.ITstat.ML
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基于代价感知的频谱接入学习与优化

Cost-Aware Learning and Optimization for Opportunistic Spectrum Access

The Pennsylvania State University(宾夕法尼亚州立大学);University of Science and Technology of China(中国科学技术大学)

Chao Gan, Ruida Zhou, Jing Yang, Cong Shen

摘要:

本文研究了认知无线电系统中多信道机会频谱接入的代价感知联合学习和优化问题。我们研究了一个离散时间模型,其中时间轴被划分为帧。每个帧由感测阶段和随后的传输阶段组成。在感测阶段,用户能够在决定在接下来的传输阶段使用其中一个之前顺序地感测通道的子集。我们假设信道状态根据帧到帧的独立伯努利随机过程在忙和空闲之间交替。为了捕捉信道感知中固有的不确定性,我们假设当信道空闲时每次传输的奖励是随机变量。我们还将随机成本与传感和传输行为相关联。我们的目标是理解在离线和在线设置中,动作的成本和回报将如何影响用户的最佳行为,并且设计相应的机会主义频谱接入策略以最大化预期的累积净回报(即,奖励减去成本)。我们从一个离线设置开始,在这个设置中,渠道状态、成本和奖励的统计数据是事先已知的。我们发现,最优策略表现出递归的双阈值结构,用户需要比较的信道统计与这些阈值顺序,以决定其行动。有了这样的见解,我们然后研究在线设置,其中的渠道,成本和奖励的统计信息是未知的先验。我们明智地平衡探索和利用,并表明,累积的遗憾规模在O(log T)。我们还建立了一个匹配的下界,这意味着我们的在线算法是顺序最优的。仿真结果证实了我们的理论分析。

英文摘要:

In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost). We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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