Function approximation based energy detection in cognitive radio using radial basis function network

Dey, Barnali; Hossain, Ashraf; Bhattacharjee, Ananya; Dey, Rajeeb; Bera, Rabindranath

doi:10.1080/10798587.2016.1217632

Cited by 3 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. (a) First the thresholds λ 1 and λ 2 for the energy detector is computed using pre trained RBFN for a specified probability of false alarm exactly in the similar fashion as discussed in [7]. For easy understanding of the reader the flow chart is presented here in Fig.…”

Section: Proposed Computational Methodologymentioning

confidence: 99%

“…For easy understanding of the reader the flow chart is presented here in Fig. 5 (Readers are advised to refer [7] for detailed explanation).…”

Section: Proposed Computational Methodologymentioning

confidence: 99%

“…Recently in [7] authors for the first time utilized the function approximation capability of radial basis function neural network (RBFN) to learn the threshold function for a pre-determined range of P FA and N such that there is no need to compute λ d every time and thereby reducing sensing latency.…”

Section: Conventional Energy Detectormentioning

confidence: 99%

“…2 for clarity of understanding to the readers. The proposed detection method first uses the radial basis neural network based function approximation technique [7] to learn the threshold function λ d for a given P FA and thereby detect PU signal as per (11). Next an improved fuzzy decision rule base is proposed to account for the decision in region between λ 1 and λ 2 (no decision region shown in Figure 1).…”

Section: Proposed Nfdt Techniquementioning

confidence: 99%

See 3 more Smart Citations

Improved Energy Detector for Spectrum Sensing Using Neuro-Fuzzy Double Threshold Technique

Dey¹,

Hossain²,

Balas³

et al. 2017

STUD INFORM CONTROL

Self Cite

View full text Add to dashboard Cite

This paper reports a modified and computationally efficient energy detection method for spectrum sensing in cognitive radio (CR) system utilising hybrid soft computing paradigms. The proposed soft computing method uses artificial neural network and fuzzy logic for learning and decision making as a solution to the problems when CR system is subjected to threshold uncertainty and uncertainties in noise variance. The proposed soft computing method should be referred thereafter as Neuro-Fuzzy double threshold (NFDT) technique. The developed technique utilizes the concept of double sensing credibility while formulating the fuzzy decision rule and artificial neural network to learn the threshold function. Through simulation results it is validated that proposed method leads to overall improved probability of detection compared to conventional and existing methods.

show abstract

Section: Proposed Computational Methodologymentioning

confidence: 99%

“…For easy understanding of the reader the flow chart is presented here in Fig. 5 (Readers are advised to refer [7] for detailed explanation).…”

Section: Proposed Computational Methodologymentioning

confidence: 99%

Section: Conventional Energy Detectormentioning

confidence: 99%

Section: Proposed Nfdt Techniquementioning

confidence: 99%

See 2 more Smart Citations