认知无线电网络空闲频谱利用的阶数优化策略
An order optimal policy for exploiting idle spectrum in cognitive radio networks
摘要：
本文提出了一种基于最近探测的认知无线电网络频谱感知策略。我们制定的问题，找到一个频谱感知政策的多频段动态频谱接入作为一个随机不安分的多臂强盗问题与固定未知的奖励分布。在认知无线电网络中，当决定在无线电频谱中的何处寻找可以有效地用于数据传输的空闲频率时，出现多臂强盗问题。我们考虑两个模型的动态的频段：1）独立的模型，其中的状态的带随机独立于过去和2）吉尔伯特-埃利奥特模型，其中的状态演变根据2状态马尔可夫链。结果表明，在这些条件下，所提出的传感策略达到渐近对数弱遗憾。本文提出的策略是一种索引策略，其中频段的索引由样本均值项和基于最近的勘探奖励项组成。样本平均值促进频谱开发，而探索奖金鼓励进一步探索提供高数据速率的空闲频带。所提出的基于新近度的方法容易地允许构造探索奖金，使得它将指数地增长次优频带的连续感测时刻之间的时间间隔，这然后导致渐进地增加弱遗憾。仿真结果证实对数弱遗憾，它被发现，所提出的政策往往提供更好的性能，在低复杂度比其他国家的最先进的政策在文献中。
英文摘要：
In this paper a spectrum sensing policy employing recency-based exploration is proposed for cognitive radio networks. We formulate the problem of finding a spectrum sensing policy for multi-band dynamic spectrum access as a stochastic restless multi-armed bandit problem with stationary unknown reward distributions. In cognitive radio networks the multi-armed bandit problem arises when deciding where in the radio spectrum to look for idle frequencies that could be efficiently exploited for data transmission. We consider two models for the dynamics of the frequency bands: 1) the independent model where the state of the band evolves randomly independently from the past and 2) the Gilbert-Elliot model, where the states evolve according to a 2-state Markov chain. It is shown that in these conditions the proposed sensing policy attains asymptotically logarithmic weak regret. The policy proposed in this paper is an index policy, in which the index of a frequency band is comprised of a sample mean term and a recency-based exploration bonus term. The sample mean promotes spectrum exploitation whereas the exploration bonus encourages for further exploration for idle bands providing high data rates. The proposed recency based approach readily allows constructing the exploration bonus such that it will grow the time interval between consecutive sensing time instants of a suboptimal band exponentially, which then leads to logarithmically increasing weak regret. Simulation results confirming logarithmic weak regret are presented and it is found that the proposed policy provides often improved performance at low complexity over other state-of-the-art policies in the literature.
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