• ASHWINI KUMAR VARMA Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India Author
  • AJEET KUMAR VISHWAKARMA Department of Computer Science and Engineering, Tula Institute, Dehradun, India Author
  • ANOOP KUMAR TIWARI Department of Computer Science and Information Technology, Central University of Haryana, Haryana, India Author
  • DEBJANI MITRA Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India Author




Cognitive radio, Low signal-to-noise ratio (SNR), Variable noise floor, Variance of multi-scale moving average, Wideband sensing


Spectrum-aware devices and cognitive radios with wideband spectrum sensing will be an integral part of 5G or beyond wireless broadband. They must be fast and energy efficient for opportunistic dynamic access to the licensed spectrum. Compressed sensing (CS) methods can implement wideband sensing with reduced time and power consumption but are inaccurate at low SNR. Eigen methods are one of the best among non-CS methods but are high in computational cost. In this paper, we present a simple feature named the variance of multi-scale moving average (VMMA) that can be directly used as a decision statistic, discriminating signal from noise very accurately, even at a low signal-to-noise ratio (SNR). VMMA computes variance in a specific way over the entire band after comparing the short-term and long-term moving averages. Tests on experimental spectrum data and numerical simulations show that the proposed algorithms are not only fast but also have higher detection probability than the algorithms developed in the literature. Analytical expressions for the probability of detection and false alarm, along with the complexities of the algorithms, are also derived.


(1) A. Marţian, Evaluation of spectrum occupancy in urban and rural environments of Romania, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 59, 1, pp. 87–96 (2014).

(2) W.S.H.M.W. Ahmad et al., 5G technology: towards dynamic spectrum sharing using cognitive radio networks, IEEE Access, 8, pp. 14460–14488 (2020).

(3) G.P. Aswathy, K. Gopakumar, Sub-Nyquist wideband spectrum sensing techniques for cognitive radio: A review and proposed techniques, AEU Int J Electron Commun, 104, pp. 44–57 (May 2019).

(4) Y. Arjoune, N. Kaabouch, A comprehensive survey on spectrum sensing in cognitive radio networks: recent advances, new challenges, and future research directions, Sensors, 19, 126, pp. 1–32 (2019).

(5) A. Ali, W. Hamouda, Advances on spectrum sensing for cognitive radio networks: theory and applications, IEEE Communications Surveys & Tutorials, 19, 2, pp. 1277–1304 (Second quarter 2017).

(6) A. Eslami, S. Karamzadeh, Double threshold maximum to minimum Eigen-value spectrum sensing proposal and performance analysis, 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), pp. 646–649 (2016).

(7) Q. Lu, S. Yang, F. Liu, Wideband spectrum sensing based on riemannian distance for cognitive radio networks, Sensors, 17, 661, pp. 1–18 (2017).

(8) A. Badarudeen, G. Student, K. Gopakumar, Wideband spectrum sensing using multi stage Weiner filter, 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, pp. 1–5 (2016).

(9) P.Y. Dibal, E.N. Onwuka, J. Agajo, C.O. Alenoghena, Application of wavelet transform in spectrum sensing for cognitive radio: A survey, Physical Communication, 28, pp. 45–57, June 2018).

(10) N. Abbas, Y. Nasser, K. E. Ahmad, Recent advances on artificial intelligence and learning techniques in cognitive radio networks, EURASIP Journal on Wireless Communications and Networking, 174, pp. 1–20 (2015).

(11) O.P. Awe, Z. Zhu, S. Lambotharan, Eigenvalue and support vector machine techniques for spectrum sensing in cognitive radio networks, 2013 Conference on Technologies and Applications of Artificial Intelligence, Taipei, pp. 223–227 (2013).

(12) S. Koley, V. Mirza, S. Islam, D. Mitra, Gradient-based real-time spectrum sensing at low SNR, IEEE Communications Letters, 19, 3, pp. 391–394 (March 2015).

(13) A.K. Varma, D. Mitra, Cognitive wideband sensing using correlation of inverted spectrum segments, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 65, 1-2, pp. 97–102 (July 2020).

(14) J. Vartiainen, J. J. Lehtomaki, H. Saarnisaari, Double-threshold based narrowband signal extraction, 2005 IEEE 61st Vehicular Technology Conference, Stockholm, 2, 2005, pp. 1288–1292.

(15) A. K. Varma, D. Mitra, A novel cloud-based self-adaptive cognitive radio network architecture, AEU - Int J of Electron and Commun, 106, pp. 32–39 (2019).

(16) M. Bkassiny, A. Lima De Sousa, S. K. Jayaweera, wideband spectrum sensing for cognitive radios in weakly correlated non-Gaussian noise, IEEE Communications Letters, 19, 7, pp. 1137–1140 (July 2015).

(17) I. S. Gradshteyn, I. M. Ryzhik, Table of Integrals, Series and Products, 7th ed., Academic Press (2000).

(18) C. Walch, Hand-book on Statistical Distributions for Experimentalists, Internal Report SUF-PFY/96-01 (2007).

(19) K. Krishnamoorthy, Handbook of Statistical Distributions with Applications, 2nd ed., CRC Press (2016).






Électronique et transmission de l’information | Electronics & Information Technology

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