Detection of Primary User in Wide-band Cognitive Radio Networks over Fading Channel using Compressed Sensing | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 4, Volume 29, Issue 1, January 2020, Page 31-38 PDF (1.32 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2020.69013 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Sabah M. Ahmed1; M. Abo-Zahhad1; Mohammed Farrag2; Khaled Ali BaAli3 | ||||
| 1Electrical and Electronics Engineering Department, Faculty of Engineering, Assiut University, Egypt. | ||||
| 2Electrical and Electronics Engineering Department, Faculty of Engineering, Assiut University, Egypt. Electrical Engineering Department, King Khalid University (KKU), Abha, KSA. | ||||
| 3Electrical and Electronics Engineering Department, Faculty of Engineering, Assiut University, Egypt. Electronics and Communication Department, Faculty of Engineering and Petroleum, Hadhramaut University, Yemen | ||||
| Abstract | ||||
| In wideband cognitive radio networks, Nyquist sampling rate is very challenging problem. It required expensive high speed analog to digital converter and large storage spaces. Lately, compressive sensing has been emerged as significant solution to crack the conventional sampling rate requirements. It proved the ability to sample below Shannan-Nyquist criteria and reconstructing back the signal after considerable dimensional reduction. Mostly in cognitive radio networks, energy detection is widely used due to its simple implementation and blind detection property. However, regardless that energy detection is subject to noise uncertainty as well as shadowing and fading which deteriorate its detection performance. Several articles have been published to improve energy detection performance using large number of measurements. In this paper, since, the detection performance using small number of measurements or compressed measurements achieved significant performance using energy detection under additive white Gaussian noise channel. This motivated us to investigate the performance of compressed measurements-based detection over fading channels which has not been studied yet. The proposed algorithm has been implemented using MATLAB. We also studied the tradeoff between the compression ratios and using fraction of transmitted signal and its impact on detection performance and threshold choice. In comparison with the ordinary compressed energy detection over the Rayleigh fading channel the results reveal that the proposed enhanced compressed measurements-based energy detection is better in performance of detection. | ||||
| References | ||||
|
[1] T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE communications surveys & tutorials, vol. 11, no. 1, pp. 116-130, 2009.
[2] M. Abo-Zahhad, S. M. Ahmed, M. Farrag, and K. A. BaAli, "Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey," Journal of Signal and Information Processing, vol. 9, p. 122, 2018.
[3] Q. Zhao and B. M. Sadler, "A survey of dynamic spectrum access," Signal Processing Magazine, IEEE, vol. 24, no. 3, pp. 79-89, 2007.
[4] F. S. P. T. Force, "Report of the spectrum efficiency working group," ed: Nov, 2002.
[5] Z. Lei and S. J. Shellhammer, "IEEE 802.22: The first cognitive radio wireless regional area network standard," IEEE communications magazine, vol. 47, no. 1, pp. 130-138, 2009.
[6] S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing (no. 3). Birkhäuser Basel, 2013.
[7] E. J. Candè and M. B. Wakin, "An introduction to compressive sampling," Signal Processing Magazine, IEEE, vol. 25, no. 2, pp. 21-30, 2008.
[8] H. Urkowitz, "Energy detection of unknown deterministic signals," Proceedings of the IEEE, vol. 55, no. 4, pp. 523-531, 1967.
[9] M. López-Benítez and F. Casadevall, "Improved energy detection spectrum sensing for cognitive radio," IET communications, vol. 6, no. 8, pp. 785-796, 2012.
[10] X. Xie and X. Hu, "Improved energy detector with weights for primary user status changes in cognitive radios networks," in Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th, 2014, pp. 53-58: IEEE.
[11] J. Shen, S. Liu, Y. Wang, G. Xie, H. F. Rashvand, and Y. Liu, "Robust energy detection in cognitive radio," IET communications, vol. 3, no. 6, pp. 1016-1023, 2009.
[12] K. Arshad and K. Moessner, "Robust spectrum sensing based on statistical tests," Iet Communications, vol. 7, no. 9, pp. 808-817, 2013.
[13] J. Song, Z. Feng, P. Zhang, and Z. Liu, "Spectrum sensing in cognitive radios based on enhanced energy detector," IET communications, vol. 6, no. 8, pp. 805-809, 2012.
[14] E. Abdessamad, R. Saadane, M. El Aroussi, M. Wahbi, and A. Hamdoun, "Spectrum sensing with an improved Energy detection," in Multimedia Computing and Systems (ICMCS), 2014 International Conference on, 2014, pp. 895-900: IEEE.
[15] A. Taherpour, S. Gazor, and M. Nasiri-Kenari, "Wideband spectrum sensing in unknown white Gaussian noise," IET communications, vol. 2, no. 6, pp. 763-771, 2008.
[16] O. Altrad and S. Muhaidat, "A new mathematical analysis of the probability of detection in cognitive radio over fading channels," EURASIP Journal on Wireless Communications and Networking, vol. 2013, no. 1, p. 159, 2013.
[17] F. F. Digham, M.-S. Alouini, and M. K. Simon, "On the energy detection of unknown signals over fading channels," IEEE transactions on communications, vol. 55, no. 1, pp. 21-24, 2007.
[18] V. I. Kostylev, "Energy detection of a signal with random amplitude," in Communications, 2002. ICC 2002. IEEE International Conference on, 2002, vol. 3, pp. 1606-1610: IEEE.
[19] F. B. de Carvalho, W. T. Lopes, and M. S. Alencar, "Performance of Cognitive Spectrum Sensing Based on Energy Detector in Fading Channels," Procedia Computer Science, vol. 65, pp. 140-147, 2015.
[20] I. E. Atawi, O. S. Badarneh, M. S. Aloqlah, and R. Mesleh, "Spectrum-sensing in cognitive radio networks over composite multipath/shadowed fading channels," Computers & Electrical Engineering, vol. 52, pp. 337-348, 2016.
[21] A.-A. A. Boulogeorgos, N. D. Chatzidiamantis, and G. K. Karagiannidis, "Spectrum sensing with multiple primary users over fading channels," IEEE Communications Letters, vol. 20, no. 7, pp. 1457-1460, 2016.
[22] J. Li, B. Li, and M. Liu, "Performance analysis of cooperative spectrum sensing over large and small scale fading channels," AEU-International Journal of Electronics and Communications, vol. 78, pp. 90-97, 2017.
[23] V. M. Patil, R. Ujjinimatad, and S. R. Patil, "Signal Detection in Cognitive Radio Networks over AWGN and Fading Channels," International Journal of Wireless Information Networks, vol. 25, no. 1, pp. 79-86, 2018.
[24] H. Sun, D. I. Laurenson, and C.-X. Wang, "Computationally tractable model of energy detection performance over slow fading channels," IEEE Communications Letters, vol. 14, no. 10, pp. 924-926, 2010.
[25] A. Rao and M.-S. Alouini, "Performance of cooperative spectrum sensing over non-identical fading environments," IEEE Transactions on Communications, vol. 59, no. 12, pp. 3249-3253, 2011.
[26] K. Ruttik, K. Koufos, and R. J'antti, "Detection of unknown signals in a fading environment," IEEE communications letters, vol. 13, no. 7, 2009.
[27] P. C. Sofotasios, E. Rebeiz, L. Zhang, T. Tsiftsis, D. Cabric, and S. Freear, "Energy detection based spectrum sensing over and extreme fading channels," Vehicular Technology, IEEE Transactions on, vol. 62, no. 3, pp. 1031-1040, 2013.
[28] V. R. S. Banjade, N. Rajatheva, and C. Tellambura, "Performance analysis of energy detection with multiple correlated antenna cognitive radio in Nakagami-m fading," IEEE Communications Letters, vol. 16, no. 4, pp. 502-505, 2012.
[29] D. A. Shnidman, "The calculation of the probability of detection and the generalized Marcum Q-function," IEEE Transactions on Information Theory, vol. 35, no. 2, pp. 389-400, 1989.
[30] M. A. Davenport, M. B. Wakin, and R. G. Baraniuk, "Detection and estimation with compressive measurements," Dept. of ECE, Rice University, Tech. Rep, 2006.
[31] T. A. Khalaf, M. Y. Abdelsadek, and M. Farrag, "Compressed measurements based spectrum sensing for wideband cognitive radio systems," International Journal of Antennas and Propagation, vol. 2015, 2015.
[32] Abo-Zahhad, M., Sabah M. Ahmed, Mohammed Farrag, and Khaled Ali BaAli. "Detection of primary user signal in wideband cognitive radio networks exploiting DCT as sensing matrix." In Radio Science Conference (NRSC), 2017 34th National, pp. 152-159. IEEE, 2017.
[33] A. V. Oppenheim, Discrete-time signal processing. Pearson Education India, 1999.
[34] R. Ujjinimatad and S. R. Patil, "Mathematical analysis for detection probability in cognitive radio networks over wireless communication channels," The Journal of Engineering, vol. 1, no. 1, 2014.
[35] M. K. Simon and M.-S. Alouini, Digital communication over fading channels. John Wiley & Sons, 2005.
[36] M. K. Simon, Probability distributions involving Gaussian random variables: A handbook for engineers and scientists. Springer Science & Business Media, 2007. | ||||
|
Statistics Article View: 235 PDF Download: 147 |
||||
