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Bayesian compressive sensing for primary user detection

机译：贝叶斯压缩感知用于主要用户检测

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摘要

In compressive sensing (CS)-based spectrum sensing literature, most studies consider accurate reconstruction of the primary user signal rather than detection of the signal. Furthermore, possible absence of the signal is not taken into account while evaluating the spectrum sensing performance. In this study, Bayesian CS is studied in detail for primary user detection. In addition to assessing the signal reconstruction performance and comparing it with the conventional basis pursuit approach and the corresponding lower bounds, signal detection performance is also considered both analytically and through simulation studies. In the absence of a primary user signal, the trade-off between probabilities of detection and false alarm is studied as it is equally important to determine the performance of a CS approach when there is no active primary user. To reduce the computation time and yet achieve a similar detection performance, finally the effect of number of iterations is studied for various systems parameters including signal-to-noise-ratio, compression ratio, mean value of accumulated energy and threshold values. The presented framework in this study is important in the overall implementation of CS-based approaches for primary user detection in practical realisations such as LTE downlink OFDMA as it considers both signal reconstruction and detection.
机译：在基于压缩感测（CS）的频谱感测文献中，大多数研究都考虑了对主要用户信号的准确重建，而不是对信号的检测。此外，在评估频谱感测性能时，不会考虑信号的可能缺失。在本研究中，针对主要用户检测对贝叶斯CS进行了详细研究。除了评估信号重建性能并将其与传统的基本追踪方法和相应的下限进行比较之外，还从分析和仿真研究的角度来考虑信号检测性能。在没有主要用户信号的情况下，研究了检测概率与错误警报之间的折衷，因为在没有活跃的主要用户时确定CS方法的性能同等重要。为了减少计算时间并获得类似的检测性能，最后针对各种系统参数（包括信噪比，压缩比，累积能量的平均值和阈值）研究了迭代次数的影响。这项研究中提出的框架在基于LTE的下行链路OFDMA等实际实现中，对于基于CS的主要用户检测方法的整体实施非常重要，因为它同时考虑了信号重建和检测。

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