Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

Abstract

The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

Enhancing Spectrum Sensing Performance in Low SNR Environments for CRN using Entropy-Based Approach

“…Diverse variants of EnD exist, including but not limited to Shannon, Renyi, Kapurs, Tsallis, and Escort Tsallis Entropy. Among these types of entropy detection, Kapurs and Renyi entropy are used for proposed techniques since they have a better spectrum sensing performance compared to others (Usman, Singh, & Rajkumar, 2022).…”

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

“…The sequential methods fail to achieve better performance because of time complexity and energy consumption to use of high rate analog-to-digital converters whereas the simultaneous spectrum sensing mechanism require more number of sensors and synchronization function which increases the implementation complexity [13]. Similarly, Usman et al [14] presented a study where suggested that prior information of PU signals can be used to classify spectrum sensing and these methods can be classified as coherent and non-coherent schemes. Further, these approaches can be categorized based on transmitter, receiver and interference for spectrum sensing.…”

“…The idea of learning from the environment is an essential aspect of cognitive radios, which involve monitoring and adjusting operating characteristics to changing conditions. In order to facilitate this learning process, several researchers have explored the use of machine learning systems [11][12][13][14][15][16] for spectrum sensing. Since channel conditions can be difficult to estimate due to fading and shadowing, spectrum sensing based solely on current sensing slots may not be reliable in determining the PU status.…”