In recent years, the professional wireless audio
transmission systems such as Program Making andSpecial
Event (PMSE) devices have been risen causing the need for a
higher spectrum efficiency. Cognitive radio (CR) paradigm
has been proposed to maximize the audio quality and
ensuring interference free operation all the time. The
motivation to a reliable spectrum sensing (SS) technique for
the detection of PMSE devices has been considered, e.g. the
detection of ProfessionalWireless Microphone Systems
(PWMS). In this paper, anovel threshold estimation
technique for the spectrum sensing (SS) using stochastic
approach for energy detection (ED) has been presented. The
performance of stochastic threshold estimation approach
under noise uncertainty environment has been tested. Under
noise uncertainty and obeying the 802.22 standard, our
stochastic thresholdhas achieved comparable results and
even outperform the doublethreshold in a low signal to noise
ratio (SNR).
Alaa Rabie Mohamed : Electronics & Communications Dept., Modern Academy for Eng. & Tech.
Cairo, Egypt.
Hatem Yousry : Electronics & Communications Dept., American University in Cairo
Cairo, Egypt.
Mohammed Al-Saeed Abd Al-Aleem Bayomy : Electronics & Communications Dept., Modern Academy for Eng. & Tech.
Cairo, Egypt.
Cognitive radio
Spectrum Sensing
Energy
Detection
Wireless Microphone
Threshold
Power Spectral
Density
Stochastic Approach
Noise Uncertainty
Simulation results show that the proposed stochastic
threshold outperforms the double threshold at aPFA =
0.1andsensing time 8.2ms in the presence of 1 dB noise
uncertainty by more than 0.1 dB.. Further experimental
work using stochastic threshold equations could be needed
to try it for U= 2dB and 3dB and measure its performance
w.r.t PMD and PFA condition. Thenthe validity of the
stochastic thresholddecision could be achieved.
[1] J. Mitola III, "Cognitive Radio: An Integrated
Architecture for Software Defined Radio," Ph. D.
Thesis, Royal Institute of Technology, Sweden, May
2000.
[2] ITU: Recommendation ITU-R SM.1134-1: Intermodulation
InterferenceCalculations in the Land-
Mobile Service.
[3] FCC: Second Report and Order and Memorandum
Opinion and Order,ET Docket No. 08-260 (Nov 2008)
[4] IEEE: Standard to Enhance Harmful Interference
Protection for Low-Power Licensed Devices Operating
in TV Broadcast Bands (ANSI/IEEE Std. 802.22.1-
2010). Institute of Electrical and Electronics Engineers,
Inc. (Nov 2010).
[5] ChristophK¨onig, Michael Bredel, and M. D. Perez
Guirao,” Spectrum-Aware Frequency Planning for
Professional Wireless Microphone Systems,” in IEEE
international Multi-Disciplinary Conference on
congintive Methods in Situation Awareness and
Decision Support(CogSIMA),San Diego,2013.
[6] T. Yucek and H. Arslan, "A Survey of Spectrum
Sensing Algorithms for Cognitive Radio Applications",
IEEE Communications Surveys & Tutorials, Vol. 11,
Issue:1, pp. 116-130, First Quarter 2009.
[7] A. Sonnenschein and P. M. Fishman, “Radiometric
detection of spreadspectrum signals in noise of
uncertainty power,” IEEE Trans. Aerosp. Electron.
Syst., vol. 28, no. 3, pp. 654–660, Jul. 1992.
[8] H. Urkowitz, “Energy detection of unknown
deterministic signals,”Proc. IEEE, vol. 55, pp. 523–
531, Apr. 1967.
[9] H. Chen, W. Gao, and D. G. Daut, ”Spectrum Sensing
for Wireless Microphone Signals,” IEEE SECON
Workshop, pp. 1-5, June 2008.
[10] Yousry, H.; Elezabi, A.; Newagy, F.; Elramly,
S.,”Periodogram-Based spectrum sensing technique for
FM wireless microphone signals,” in Proceedings of
18th International Conference on Telecommunications
(ICT),2011.
[11] R. Tandra and A. Sahai,"SNR Walls for Signal
Detection," IEEE 1. Sel.Topics Signal Process., Vol.2,
pp. 4-17, Feb.2008.
[12] Mai H. Hassan and Omar A. Nasr,” Adaptive Spectrum
Sensing of Wireless Microphones with Noise
Uncertainty,” in IEEE 2nd International Symposium on Personal, Indoor and Mobile Radio
Communications, 2011.
[13] Zhuan Ye, G Memik, John Grosspietsch, "Energy
Detection using Estimated Noise Variance for
Spectrum Sensing in Cognitive Radio Networks", in
Proc. Wireless Communication and
NetworkingConference, April 2008, Las Vegas, NV,
pp. 711 - 716.
[14] A Gorcin, Khalid A Q,H Celebi, H Arslan, "An
Adaptive Threshold Method for Spectrum Sensing
in Multi-Channel Cognitive Radio Networks", in
Proc. IEEE 17th International Conference
onTelecommunications (ICT), April 2010, Doha,
Qatar, pp. 425-429.
[15] H H Choi, K Jang, Y Cheong, "Adaptive Sensing
Threshold Control Based on Transmission Power in
Cognitive Radio Systems", Cognitive Radio Oriented
Wireless Networks and Communications, May 2008,
Singapore, pp. 1 -6.
[16] J W Lee, J H Kim, H J Oh and S H Hwang,
"Energy Detector using Hybrid Threshold in
Cognitive Radio Systems", IEICE Transactions on
Communications, volume: E92-B,No: 10, Oct 2009,
pp. 3079-3083.
[17] G. Yu, C. and W. Xi, "A Novel Energy Detection
Scheme Based on Dynamic Threshold in Cognitive
Radio Systems," Journal ofComputational Information
Systems, Vol. 8, pp. 2245-2252, Mar. 2012.
[18] S. M. Kay, Fundamentals of Statistical Signal
Processing: DetectionTheory. Englewood Cliffs, NJ:
Prentice-Hall, 1998, vol. 2.
[19] P. D. Welch, "The use of fast Fourier transform for the
estimation of power spectra: A method based on time
averaging over short, modified periodogram," IEEE
Trans. On Audio and Electroacoustics, vol. 15, no. 2,
pp. 70 – 73, 1967.
[20] P. Stoica and R. Moses, Spectral Analysis of Signals,
Prentice Hall,2005.
[21] Prashob R Nair, A. P. Vinod and Anoop Kumar
Krishna,” An Adaptive Threshold Based Energy
Detector for Spectrum Sensing in Cognitive Radios at
Low SNR,” in The 7th IEEE VTS Asia Pacific
Wireless Communication,2010.
[22] Prashob R Nair, A. P. Vinod and Anoop Kumar
Krishna,” An Energy Detector for Cognitive Radios in
Channels at Low SNR Using Adaptive Threshold, “in
8th International Conference on Information,
Communications and Signal Processing (ICICS) ,2011.
[23] Mrs. R. S. Kale (Sandikar), Dr. Vijay M. Wadhai and
Dr. Jagdish B. Helonde,” Efficient Spectrum Sensing
In Cognitive Radio Using Energy Detection Method
with New Threshold Formulation,” in International
Conference on Microelectronics, Communication and
Renewable Energy (ICMiCR-2013).
[24] Hossam M. Farag and Ehab M. “An Efficient Dynamic
Thresholds Energy Detection Technique for Cognitive
Radio Spectrum Sensing,” in 10th International
Computer Engineering Conference (ICENCO),
IEEE,2014
[25] ShujingXie and LianfengShen,”Double-threshold
Energy Detection of Spectrum Sensing for Cognitive
Radio Under Noise Uncertainty Environment” in
International Conference on Wireless Communications
& Signal Processing,2012.
