Innovative Fuzzy-Based Spectrum Sensing Technique for Noisy Conditions

Bharatula, Sitadevi; Murthy, B. S.

doi:10.1007/978-981-15-9019-1_64

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…From Figures 7 and 8 , it is observed that the PU detection using single threshold fails as the noise uncertainty increases, because the threshold fixed is independent of the noise variance. Realizing Eqn (10)(11)(12)(13)(14)(15)(16)(17) in MATLAB and by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed adaptive thresholding using fuzzy logic method for ED under Laplacian noise, provides reliable detection compared to the other methods when the noise uncertainty increases. Another interesting observation drawn is on the improvement in signal detection in the low SNR regimes.…”

Section: Resultsmentioning

confidence: 99%

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Spectrum Sensing in Non-Gaussian Noise

Reddy¹,

Ravinder²

2021

IJST

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Background: Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. Objectives: Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum sensing with the assumption that the noise follows a non-Gaussian distribution with heavier tails. Methods/findings: A fuzzy logicbased method is proposed for primary user detection under non Gaussian Noise. The results are highlighted for the Laplacian noise. Through Monte Carlo simulations it is observed that Laplacian noise noticeably affects the performance of energy detector. Also, a fractional change in noise uncertainty degrades the performance of energy detector. The performance of the proposed scheme is presented through receiver operating characteristic (ROC) and plot of the detection probability versus signal-to-noise ratio (SNR) using simulations. It is shown that by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed fuzzy logic method for spectrum sensing provides reliable detection.

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Section: Resultsmentioning

confidence: 99%

“…Through analytical and simulated results, it is shown that this method is robust to dynamically varying noise (8) . In (9)(10)(11) fuzzy logic is used to address the decision on the PU activity. This scheme showed better detection performance in low signal to noise ratio (SNR) regions.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing in Non-Gaussian Noise

Reddy¹,

Ravinder²

2021

IJST

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Smart sensing enabled dynamic spectrum management for cognitive radio networks

Fraz,

Muslam,

Hussain

et al. 2023

Front. Comput. Sci.

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Cognitive Radio Networks (CRNs) have ushered in a transformative era in wireless communication, reshaping the landscape of radio spectrum utilization and management. At the core of CRNs lies the pivotal capability to sense the radio frequency spectrum dynamically and adapt transmission parameters to preemptively address interference and optimize spectrum utilization. This article addresses the escalating challenges associated with Quality of Service (QoS) management in CRNs, exacerbated by their dynamic nature, especially in scenarios characterized by high mobility. Concurrently, the article underscores the critical significance of energy efficiency, given its direct implications on network operational costs and sustainability. To effectively navigate the intricate interplay between QoS and energy management in CRNs, we propose a Smart Sensing Enabled Dynamic Spectrum Management scheme (SSDSM). Within the SSDSM framework, cognitive user energy undergoes intelligent sensing, while QoS is governed through dynamic spectrum management. The proposed scheme optimizes service response time by refining fuzzy-based controllers and curtails energy consumption through periodic sensing triggered by predefined rules. Operationalizing within a centralized paradigm, the entire network is overseen by a central controlling node, tasked with formulating an optimal channel list using the SSDSM scheme and allocating it to cognitive users. The efficacy of the proposed scheme is evaluated and validated through rigorous testing using MATLAB. Results reveal tangible enhancements in system efficiency, encompassing maximized throughput, reduced handoff ratio, and minimized service response delay. This research contributes to the ongoing discourse on advancing the performance metrics of cognitive radio networks in the pursuit of reliable and sustainable wireless communication services.

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Innovative Fuzzy-Based Spectrum Sensing Technique for Noisy Conditions

Cited by 2 publications

References 12 publications

Spectrum Sensing in Non-Gaussian Noise

Spectrum Sensing in Non-Gaussian Noise

Smart sensing enabled dynamic spectrum management for cognitive radio networks

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