Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor

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.

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“…In [7], the performance of SS (spectrum sensing) for CR was examined under noise floor and low SNR values were presented.…”

Section: Related Workmentioning

confidence: 99%

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

Usman

Singh

Rajkumar

2022

Wireless Power Transfer

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“…False detection can also occur when the primary signal is misidentification for a noise signal. [3][4]. It is a highly unwanted state as it leads to an interruption of the primary user's transmissions.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Spectrum Sensing Performance Assessment using Machine Learning in Cognitive Radio Sensor Networks

Venkatapathi,

Khan,

Srinivasa Rao

et al. 2024

Eng. Technol. Appl. Sci. Res.

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The Cognitive Radio (CR) is an imminent technology, intended to make more effective use of the available spectrum by giving access to licensed frequency bands by unlicensed Secondary Users (SUs) without affecting Primary licensed Users (PUs). Depending on the region where the energy is being observed, each CR communicates local decisions or the seen energy to the Fusion Center (FC). This study presents the many plots that discuss an enhanced double threshold through the Cooperative Spectrum Sensing (CSS) approach. The FC then combines local decisions with the measured energy values to reach a final decision. The usage of several machine learning methods in spectrum decision with the myopic decision is estimated. The system seeks to enhance the long-term overall performance of the SU.

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“…e main goal of CR is to resolve the bandwidth crises problem by utilizing the natural resources well such as transmitted energy, time, and frequency [11][12][13]. CR technology detects the available spectrum bands and identifies which bands are vacant and where.…”

Section: Introductionmentioning

confidence: 99%

Improving Spectrum Sensing for Cognitive Radio Network Using the Energy Detection with Entropy Method

Usman

Singh

Mishra

et al. 2022

Journal of Electrical and Computer Engineering

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Spectrum is one of the world’s most highly regulated and limited natural resources. Cognitive Radio (CR) is a cutting-edge technology that aims to solve the future spectrum shortage issue in wireless communication systems. CR is one of the most widely used methods for maximizing the use of the wireless spectrum. Spectrum sensing is a critical step in discovering spectrum gaps in CR. Matching filter detection, energy detection (ED), cyclostationary detection, correlation coefficient detection, and wavelet detection are some of the frequency band sensing techniques. ED has received the most attention from many researchers because of its convenience and low computation complexity. However, noise instability, or the random and unavoidable variation of noise that exists in any communication link, greatly decreases the output of ED, especially whenever the signal-to-noise ratio (SNR) is poor. As a result, this research provides an exciting spectrum sensing option known as the energy detection with entropy method technique. In contrast to conventional ED, the proposed energy detection with entropy method offers better sensing performance in low SNR circumstances. According to simulation results, the proposed method has a significant performance improvement of about 18.58% when compared to CED at a given SNR of −18 dB.

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Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor

Cited by 4 publications

References 7 publications

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

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

Cooperative Spectrum Sensing Performance Assessment using Machine Learning in Cognitive Radio Sensor Networks

Improving Spectrum Sensing for Cognitive Radio Network Using the Energy Detection with Entropy Method

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