基于电平触发采样的协作序列频谱感知
Cooperative Sequential Spectrum Sensing Based on Level-triggered Sampling
摘要：
我们提出了一种用于认知无线电网络中协作频谱感知的新框架，该框架基于一类新型非均匀采样器（称为事件触发采样器）和顺序检测。在该方案中，每个次用户根据自己的信道输出计算其本地感知判决统计量，当该判决统计量超过预定义的阈值时，次用户将向融合中心发送一个（或多个）比特的信息。融合中心异步接收来自不同次用户的比特，并更新全局感知决策统计，以执行顺序概率比测试（SPRT），以达到感知决策。我们提供了上述方案的渐近分析，并在不同的条件下，我们比较了它与传统的基于均匀采样和顺序检测的合作感知方案。仿真结果表明，在目标错误概率、信噪比和SU数变化的情况下，该方案即使只使用1比特，也能优于使用无穷多比特的均匀采样方案。
英文摘要：
We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers, called the event-triggered samplers, and sequential detection. In the proposed scheme, each secondary user computes its local sensing decision statistic based on its own channel output; and whenever such decision statistic crosses certain predefined threshold values, the secondary user will send one (or several) bit of information to the fusion center. The fusion center asynchronously receives the bits from different secondary users and updates the global sensing decision statistic to perform a sequential probability ratio test (SPRT), to reach a sensing decision. We provide an asymptotic analysis for the above scheme, and under different conditions, we compare it against the cooperative sensing scheme that is based on traditional uniform sampling and sequential detection. Simulation results show that the proposed scheme, using even 1 bit, can outperform its uniform sampling counterpart that uses infinite number of bits under changing target error probabilities, SNR values, and number of SUs.
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