A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio networks, spectrum sensing is a crucial component in the discovery of spectrum opportunities for secondary systems (or unlicensed systems). The performance of spectrum sensing is characterized by both accuracy and efficiency. Currently, significant research effort has been made on improving the sensing accuracy. Several exemplary techniques include energy detectors, feature detectors, and cooperative sensing. In these schemes, either one or multiple secondary users (SUs) perform sensing on a single and the same channel during each sensing period. This strategy on simultaneously sensing a single channel by several SUs may limit the sensing efficiency to a large extent. In this paper, we propose a new parallel spectrum sensing. In this scheme, several SUs are optimally selected to perform sensing. During a sensing period, each of the selected SUs senses a different channel. As a consequence, multiple channels can be simultaneously sensed in one sensing period, and the sensing efficiency is envisioned to improve significantly. An analytical model is presented to investigate the tradeoff between the transmitted data and the sensing overhead. A throughput maximization problem is formulated to find key design parameters: the number of SUs that perform parallel sensing and the threshold in stopping the sensing. Both saturation and nonsaturation situations are investigated with respect to throughput, transmission gain, overhead, and delay. Numerical examples demonstrate that our proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing mechanisms.

在认知无线电网络中，频谱感知是次级系统（或无执照系统）发现频谱机会的关键组成部分。频谱感知的性能由准确性和效率来表征。目前，在提高感知准确性方面已经做出了大量的研究努力。几种典型的技术包括能量检测器、特征检测器和协作感知。在这些方案中，在每个感知周期内，一个或多个次级用户（SUs）在单个相同的信道上进行感知。这种由几个次级用户同时感知单个信道的策略可能在很大程度上限制感知效率。在本文中，我们提出一种新的并行频谱感知。在该方案中，最优地选择几个次级用户进行感知。在一个感知周期内，每个被选中的次级用户感知不同的信道。因此，在一个感知周期内可以同时感知多个信道，并且预计感知效率将显著提高。提出了一个分析模型来研究传输数据和感知开销之间的权衡。制定了一个吞吐量最大化问题以找到关键设计参数：进行并行感知的次级用户数量以及停止感知的阈值。针对吞吐量、传输增益、开销和延迟对饱和和非饱和情况都进行了研究。数值示例表明，与现有机制相比，我们提出的方案能够实现更高的吞吐量和更低的延迟。