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首页> 外文会议>IEEE Conference on Computer Communications >Online Bayesian Learning for Rate Selection in Millimeter Wave Cognitive Radio Networks
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Online Bayesian Learning for Rate Selection in Millimeter Wave Cognitive Radio Networks

机译：毫米波认知无线电网络中用于速率选择的在线贝叶斯学习

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摘要

We consider the problem of dynamic rate selection in a cognitive radio network (CRN) over the millimeter wave (mmWave) spectrum. Specifically, we focus on the scenario when the transmit power is time varying as motivated by the following applications: i) an energy harvesting CRN, in which the system solely relies on the harvested energy source, and ii) an underlay CRN, in which a secondary user (SU) restricts its transmission power based on a dynamically changing interference temperature limit (ITL) such that the primary user (PU) remains unharmed. Since the channel quality fluctuates very rapidly in mmWave networks and costly channel state information (CSI) is not that useful, we consider rate adaptation over an mmWave channel as an online stochastic optimization problem, and propose a Thompson Sampling (TS) based Bayesian method. Our method utilizes the unimodality and monotonicity of the throughput with respect to rates and transmit powers and achieves logarithmic in time regret with a leading term that is independent of the number of available rates. Our regret bound holds for any sequence of transmits powers and captures the dependence of the regret on the arrival pattern. We also show via simulations that the performance of the proposed algorithm is superior than the stateof-the-art algorithms, especially when the arrivals are favorable.
机译：我们考虑在毫米波（mmWave）频谱上的认知无线电网络（CRN）中动态速率选择的问题。具体来说，我们重点关注以下应用激发发射功率随时间变化的场景：i）能量采集CRN，其中系统仅依赖于采集的能源; ii）底层CRN，其中次要用户（SU）会根据动态变化的干扰温度限制（ITL）限制其传输功率，以使主要用户（PU）不受损害。由于信道质量在mmWave网络中波动非常快并且昂贵的信道状态信息（CSI）没那么有用，因此我们将mmWave信道上的速率自适应视为在线随机优化问题，并提出了一种基于汤普森采样（TS）的贝叶斯方法。我们的方法利用了速率和传输功率方面的吞吐量的单峰性和单调性，并以对数表示后悔，及时获得对数的后悔，其前导项与可用速率的数量无关。我们的后悔界限适用于任何顺序的发射功率，并捕获了后悔对到达模式的依赖性。我们还通过仿真表明，所提出算法的性能优于最新算法，尤其是在到达时间令人满意的情况下。

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