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Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks

机译：OFDM认知无线电网络的新型频谱感知算法

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摘要

Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm “Differential Characteristics-Based OFDM (DC-OFDM)” for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a “Differential Characteristics-Based Cyclic Prefix (DC-CP)” detector and a “Differential Characteristics-Based Pilot Tones (DC-PT)” detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.
机译：频谱感测技术在认知无线电网络中扮演着越来越重要的角色。因此，在文献中已经提出了几种频谱感测算法。在本文中，我们提出了一种新的频谱检测算法“基于差分特性的OFDM（DC-OFDM）”，用于基于差分特性检测OFDM信号。我们将主要值放在零附近的信道增益θ上，以检测主要用户的存在。此外，使用相同的差分运算方法，我们改进了两种传统的OFDM感知算法（循环前缀和导频音检测算法），并提出了“基于差分特征的循环前缀（DC-CP）”检测器和“差分特征-基于“ Pilot Tones（DC-PT）”的检测器。 DC-CP检测器基于自相关矢量来检测频谱，而DC-PT检测器则以PT的频域互相关作为测试统计量来检测主要用户。此外，推导了三种方法的检验统计量的分布。仿真结果表明，在存在定时延迟的情况下，这三种方法均能在低信噪比（SNR）下获得良好的性能。具体而言，DC-OFDM检测器在提出的检测器中获得最佳性能。而且，与它们相应的原始检测器相比，DC-CP和DC-PT检测器均实现了显着改进。

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