An Intelligent Fuzzy Based Energy Detection Approach for Cooperative Spectrum Sensing

Bharatula, Sitadevi; Meenakshi, M.

doi:10.4236/cs.2016.76088

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Since fuzzy logic can conceptualize fuzziness into a clear quantifiable parameter, the model based on fuzzy logic can be used for signal detection. Fuzzy decision thinking was exploited in [24], [25] to solve the problematic decision problem in signal detection, and the experimental results proved that fuzzy logic could improve detection performance. SR system mainly improves detection performance by increasing the SNR.…”

Section: A Related Workmentioning

confidence: 99%

“…Fuzzy logic states that a statement is both true and false, neither true nor false, and therefore fuzzy logic is non-monotonic logic. From the perspective of operation, fuzzy logic is a one-shot method and does not iterate [25], thus it operates very fast. In addition, it is easy to modify because it is subject to user-defined rules.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…It can be seen from (26) that t i is a value less than 1, and the sum of all t i is 1. Therefore, we use the same fuzzy decision threshold as (25) to obtain the fuzzy output. The fuzzy out is expressed as…”

Section: B Fusion Center Decisionmentioning

confidence: 99%

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A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic

et al. 2020

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In this paper, an interference detection algorithm based on fuzzy logic fusion is proposed for the interference monitoring system (IMS) of the global satellite navigation system (GNSS). In particular, for the problem of reduced detection performance caused by noise fluctuations in a complex electromagnetic environment, the proposed algorithm introduces the idea of fuzzy logic. According to the credibility of local detection results in different regions determined by noise uncertainty, we adopt a two-step cooperative detection mechanism in the fusion center (FC). When a definite detection result is obtained in the first step, the decision is finished. Otherwise, we will execute the decision algorithm based on the fuzzy logic fusion of the second step. The experimental results show that the proposed algorithm has the best detection performance, compared with the traditional energy detection algorithm and the energy detection algorithm based on soft data. Furthermore, compared with soft data, the system overhead of the proposed algorithm is also reduced without increasing the amount of calculation.

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Section: A Related Workmentioning

confidence: 99%

Section: Fuzzy Logicmentioning

confidence: 99%

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A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic

et al. 2020

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“…The hopping group results are finally used to assign the channels with high, medium, and low ranks. Bharatula and Murugappan [21] proposed a multiple-attribute decision making scheme to choose the better network for SU transmissions. In the proposed scheme, different weights are assigned to each input parameter, such as delay, packet loss ratio (PLR), and price, according to the nature of the transmissions (e.g., audio, video, and data).…”

Section: Related Workmentioning

confidence: 99%

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

et al. 2019

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The proliferation of mobile devices and the heterogeneous environment of wireless communications have increased the need for additional spectrum for data transmission. It is not possible to altogether allocate a new band to all networks, which is why fully efficient use of the already available spectrum is the demand of the day. Cognitive radio (CR) technology is a promising solution for efficient spectrum utilization, where CR devices, or secondary users (SUs), can opportunistically exploit white spaces available in the licensed channels. SUs have to immediately vacate the licensed channel and switch to another available channel when they detect the arrival of the incumbent primary user. However, performance for the SU severely degrades if successive channel switching happens. Moreover, taking the channel-switching decisions based on crisp logic is not a suitable approach in the brain-empowered CR networks (CRNs) where sensing information is not only imprecise and inaccurate but also involves a major uncertainty factor. In this paper, we propose a fuzzy logic-based decision support system (FLB-DSS) that jointly deals with channel selection and channel switching to enhance the overall throughput of CRNs. The proposed scheme reduces the SU channel switching rate and makes channel selection more adaptable. The performance of the proposed scheme is evaluated using a Matlab simulator, and a comprehensive comparison study with a baseline scheme is presented. The simulation results are promising in terms of the throughput and the number of handoffs and making our proposed FLB-DSS a good candidate mechanism for SUs while making judicious decisions in the CR environment. INDEX TERMS Cognitive radio network, fuzzy logic, resource allocation, channel selection, handoff rate.

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“…A fuzzy fusion was proposed to make a local soft decision at a CU by considering the SNR of the PU signal that is familiar to a CU [21]. Most existing CSS schemes were based on the assumption that the SNR of the PU at the CU was fully familiar [22]. However, it is very difficult to calculate the SNR value of the PU signal in a given spectrum band as there is no cooperation between the CU and PU.…”

Section: Introductionmentioning

confidence: 99%

Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System

Amin

Rahman

Hossain

et al. 2018

BDCC

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In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.

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An Intelligent Fuzzy Based Energy Detection Approach for Cooperative Spectrum Sensing

Cited by 8 publications

References 23 publications

A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic

A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System

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