Spectrum sensing is a key technology enabling the cognitive radio system. In this paper, the problem of how to quickly and accurately find an unoccupied channel from a large amount of potential channels is considered. The cognitive radio system under consideration is equipped with a narrow band sensor, hence it can only sense those potential channels in a sequential manner. In this scenario, we propose a novel two-stage mixed-observation sensing strategy. In the first stage, which is named as scanning stage, the sensor observes a linear combination of the signals from a pair of channels. The purpose of the scanning stage is to quickly identify a pair of channels such that at least one of them is highly likely to be unoccupied. In the second stage, which is called refinement stage, the sensor only observers the signal from one of those two channels identified from the first stage, and selects one of them as the unoccupied channel. The problem under this setup is an ordered two concatenated Markov stopping time problem. The optimal solution is solved using the tools from the multiple stopping time theory. It turns out that the optimal solution has a rather complex structure, hence a low complexity algorithm is proposed to facilitate the implementation. In the proposed low complexity algorithm, the cumulative sum test is adopted in the scanning stage and the sequential probability ratio test is adopted in the refinement stage. The performance of this low complexity algorithm is analyzed when the presence of unoccupied channels is rare. Numerical simulation results show that the proposed sensing strategy can significantly reduce the sensing time when the majority of potential channels are occupied.

中文翻译：

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使用混合观测进行最快的连续多波段频谱感测

频谱感知是实现认知无线电系统的关键技术。本文考虑了如何从大量潜在信道中快速准确地找到未占用信道的问题。所考虑的认知无线电系统配备了一个窄带传感器，因此它只能以顺序方式感知那些潜在的信道。在这种情况下，我们提出了一种新颖的两阶段混合观测传感策略。在称为扫描阶段的第一阶段，传感器观察来自一对通道的信号的线性组合。扫描阶段的目的是快速识别一对通道，使得其中至少一个很可能未被占用。在第二阶段，称为细化阶段，传感器仅观察来自第一阶段识别的这两个通道之一的信号，并选择其中一个作为未占用的通道。这个设置下的问题是一个有序的两个串联马尔可夫停止时间问题。使用多重停止时间理论中的工具求解最优解。事实证明，最优解具有相当复杂的结构，因此提出了一种低复杂度的算法以方便实现。在所提出的低复杂度算法中，扫描阶段采用累积和检验，细化阶段采用顺序概率比检验。当未占用信道的存在很少时，分析了这种低复杂度算法的性能。