动态频谱接入网主用户业务分类
Primary User Traffic Classification in Dynamic Spectrum Access Networks
摘要：
本文重点分析研究的主要用户（PU）流量分类问题。观察到伽马分布可以表示正偏斜数据和指数分布（在通信网络性能分析文献中流行），在此将其视为PU业务描述符。我们研究了两个PU流量分类器，利用完美测量的PU活动（繁忙）和不活动（空闲）周期：（i）最大似然分类器（MLC）和（ii）多假设序贯概率比测试分类器（MSPRTC）。然后，放松完美的周期测量的假设，我们考虑通过信道采样的PU流量观察。对于两次采样之间PU状态变化的概率可以忽略的特殊情况，我们提出了一种最小方差PU忙/闲周期长度估计器。随后，放松PU周期长度分布的参数的完整知识的假设，我们提出了两个PU流量分类方案：（i）估计然后分类（ETC），和（ii）平均似然函数（ALF）分类器考虑时域波动的PU流量参数。数值结果表明，当分布假设之间的距离较小时，MLC和MSPRTC对周期测量误差敏感;当分布假设之间的距离较大时，MLC和MSPRTC对分布参数估计误差敏感。对于PU交通参数的分布的部分先验知识，ETC优于ALF时，假设之间的距离是小的，而相反的情况下，当距离大。
英文摘要：
This paper focuses on analytical studies of the primary user (PU) traffic classification problem. Observing that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.
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