A Recursive Algorithm for Wideband Temporal Spectrum Sensing
A Recursive Algorithm for Wideband Temporal Spectrum Sensing
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DOI：
10.1109/tcomm.2017.2749578
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发表时间：
2018
影响因子：
8.3
通讯作者：
J. Bruno;B. L. Mark
中科院分区：
文献类型：
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作者：
J. Bruno;B. L. Mark
宽带频谱感测技术确定给定频谱带的哪些部分在频域中被占用或空闲。空闲部分表示可能被次要或未授权用户利用的频谱空洞。然而，用于宽带感测的现有方法没有考虑频谱带内的主用户或许可用户的时间活动。我们提出了一种算法，识别主用户的活动在一个宽的频谱带，并提供了一个统计特性的主用户信号的频带。该算法采用隐马尔可夫模型的频谱带的分层分区表示，连同递归树搜索。与现有的宽带感知算法不同，所提出的宽带时域感知方法即使在主用户信号突发的情况下也能够准确地检测频谱空洞。此外，主用户信号的隐马尔可夫建模使得能够准确检测和预测主用户随时间的活动。数值结果表明，所提出的算法的显着的性能增益超过现有的宽带频谱感知算法，特别是在存在低占空比的主用户信号。
Wideband spectrum sensing techniques determine which portions of a given spectrum band are occupied or idle in the frequency domain. The idle portions represent spectrum holes that can potentially be exploited by secondary or unlicensed users. Existing methods for wideband sensing, however, do not take into account the temporal activity of the primary or licensed users within the spectrum band. We propose an algorithm that identifies primary user activity over a wide spectrum band and provides a statistical characterization of the primary user signals in the band. The algorithm applies hidden Markov modeling to a hierarchically partitioned representation of the spectrum band, together with a recursive tree search. Different from existing wideband sensing algorithms, the proposed wideband temporal sensing method is able to accurately detect spectrum holes even in the presence of bursting primary user signals. Moreover, the hidden Markov modeling of the primary user signals enables the accurate detection and the prediction of primary user activity over time. Numerical results demonstrate the significant performance gain of the proposed algorithm over existing wideband spectrum sensing algorithms, particularly in the presence of low duty-cycle primary user signals.