Channel Energy Statistics Learning in Compressive Spectrum Sensing
Channel Energy Statistics Learning in Compressive Spectrum Sensing
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DOI：
10.1109/twc.2018.2872712
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发表时间：
2018-10
影响因子：
10.4
通讯作者：
Haoran Qi;Xingjian Zhang;Yue Gao
中科院分区：
文献类型：
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作者：
Haoran Qi;Xingjian Zhang;Yue Gao
频谱感知是认知无线电系统中实现动态频谱接入的一种主动方式，而压缩频谱感知（CSS）技术缓解了宽带频谱感知中对高速采样的需求。大多数现有文献讨论了Neyman-Pearson信道能量检测和阈值自适应方案，以在传统的非压缩频谱感测场景中实现检测的最佳性能。然而，在CSS中，发现信道能量统计和最佳阈值不仅取决于噪声能量，而且还取决于压缩比，频谱的稀疏性和恢复算法的性质。为了研究恢复频谱的信道能量统计，我们假设了一个统计模型的信道能量CSS和提出了一种学习算法的基础上的混合模型和期望最大化技术。此外，在验证了假设模型的有效性，我们提出了一个实用的阈值自适应方案的CSS旨在保持恒定的虚警率在信道能量检测。仿真结果表明，在各种信道模型和恢复算法的情况下，利用该算法学习得到的信道能量统计模型与经验分布拟合良好。此外，它提出了建议的阈值自适应方案保持虚警率接近预定义的常数，这反过来验证了假设的模型。
Spectrum sensing is a proactive way in cognitive radio systems to achieve dynamic spectrum access; and compressive spectrum sensing (CSS) techniques alleviate the demand for high-speed sampling in wideband spectrum sensing. Most existing literature discusses Neyman–Pearson channel energy detection and threshold adaption schemes to achieve an optimal performance of detection in a conventional non-compressive spectrum sensing scenario. However, in the CSS, it is found that the channel energy statistics and optimal threshold depend not only on noise energy but also on compression ratio, sparsity of spectrum, and nature of recovery algorithms. To investigate the channel energy statistics of recovered spectrum, we postulate a statistical model of channel energy for CSS and propose a learning algorithm based on a mixture model and expectation–maximization techniques. In addition, having verified the validity of the postulated model, we propose a practical threshold adaption scheme for CSS aiming to maintain constant false alarm rates in channel energy detection. In simulations, it is shown that the postulated channel energy statistic models with parameters learned by the proposed learning algorithm fit well with empirical distributions under circumstances of various channel models and recovery algorithms. Moreover, it is presented that the proposed threshold adaption scheme maintains the false alarm rate near the predefined constant, which in turn validates the postulated model.