物联网中基于截断SPRT的改进协同频谱感知方案

Shihao Gao, Ningbo Zhang, Guixia Kang
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引用次数: 3

摘要

认知无线电的关键技术是频谱感知。实验结果表明,协同频谱感知(CSS)可以提高检测概率。然而,在物联网高实时性场景的CSS中,不能忽略感知和报告周期,从而导致高延迟和低吞吐量。在本文中,我们考虑了一种改进的CSS方案,其中每个认知节点对每个收集到的观测向量采用多锥度感知方法(MTM),并对MTM结果进行截断序列概率比检验(T-SPRT)。基于所设计的框架结构,推导了时延和吞吐量的理论分析和表达式。仿真结果表明,该方案降低了感知延迟,提高了吞吐量,具有较高的检测概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Cooperative Spectrum Sensing Scheme Using Truncated SPRT in Internet of Things
The key technique of cognitive radio(CR) is spectrum sensing. It shows that the cooperative spectrum sensing(CSS) can improve the detection probability. However, in the CSS of high real-time scene of internet of things(IoT), sensing and reporting period cannot be neglected, which leads to the high latency and low throughput. In this paper, we consider an improved CSS scheme, where each cognitive node employs the multi-taper sensing method(MTM) on every collected observation vector and performs truncated sequential probability ratio test (T-SPRT) on MTM results. Theoretical analysis and expressions for latency and throughput are derived based on the designed frame structure. Simulation results demonstrate that the proposed scheme decreases sensing latency and improves throughput with high detection probability.
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