Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy
M. Ajay Kumar1, N. Rajesha2, Anupama Deshpande3

1M. Ajay Kumar, Research Scholar, Dept of ECE, Shri JJT University, Jhunjhunu-01, Rajasthan, India.
2N. Rajesha, Dept of ECE, Malla Reddy Institute of Engg and Tech, Hyderabad, T.S, India.
3Anupama Deshpande, Dept of ECE, Shri JJT University, Jhunjhunu Rajasthan, India

Manuscript received on September 15, 2019. | Revised Manuscript received on 23 September, 2019. | Manuscript published on October 10, 2019. | PP: 4257-4261 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27041081219/2019©BEIESP | DOI: 10.35940/ijitee.L2704.1081219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: This paper represents the unique system model for cognitive radio based on the energy spotting method to enhance the performance of the accuracy by managing the queue regarding energy-samples and also estimating their average in order to characterize the decision-threshold. Consequently, these typical values summed and estimated over the sum of the samples are repeatedly correlated and analyzed with the recent energy values to determine whether the frequency band is vacant or occupied most accurately. The energy spotting technique’s performance is analyzed and estimated analytically for distinct decision-thresholds. Conventionally Such evaluations interprets that; the advances made to energy spotting algorithm which have enhanced the sensing accuracy in spectrum under the differing signal-to-noise ratio values. Consequently, we shown the utilities and advantages of proposed model that increases the cognitive radio ability. The performance has measured by utilizing the AWGN (Additive White Gaussian Noise) channel and receiver operating-characteristics curves varying under various SNR values alike as: -20 dB, -15 dB, -5 dB, 0 dB, 5db and 10db. With small-tradeoffs among the detection and false-alarm probabilities, the model increases and enhances the ability of spectrum sensing mechanism greatly in the lower SNR situations while tested with number of samples. By that, improving the conventional performance by increasing the sensing accuracy of cognitive radio networks under the low SNR have been the promising achievement of this research work.
Keywords: Cognitive Radio, False Alarm and Detection Probability, Spectrum Sensing, Threshold.
Scope of the Article: Cognitive Radio Networks