Maximum-minimum energy based spectrum sensing under frequency selectivity for Cognitive Radios

Cognitive Radio (CR) technology which uses intelligent signal processing at the physical layer of a wireless system has been considered for coordinating better the spectral resources. In this study, we investigate spectrum sensing methods which utilize the frequency variability of the energy spectral density which is introduced by the primary transmissions. The variability is partly due to the transmitted spectrum shape and party introduced by the frequency selective multipath channel. The spectral variability is observed by dividing the sensing frequency band into relatively narrow subbands and comparing the subband energies. We compare the subband energy based methods against the eigenvalue based sensing method, which exploits the signal correlations introduced by the primary transmission. Eigenvalue based sensing is known to be robust against the inevitable noise uncertainty, which severely limits the usability of energy detection for primaries with low SNR. Our results demonstrate for the subband energy detection methods, similar robustness against noise uncertainty as the eigenvalue based methods have.

[1]  Volkan Cevher,et al.  Gaussian Approximations for Energy-Based Detection and Localization in Sensor Networks , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[2]  Manjunath V. Joshi,et al.  Energy detection based spectrum sensing over η-λ-μ fading channel , 2016, 2016 8th International Conference on Communication Systems and Networks (COMSNETS).

[3]  Giorgio Taricco,et al.  Optimization of Linear Cooperative Spectrum Sensing for Cognitive Radio Networks , 2011, IEEE Journal of Selected Topics in Signal Processing.

[4]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[5]  R.W. Brodersen,et al.  Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[6]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[7]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[8]  Anant Sahai,et al.  SNR Walls for Signal Detection , 2008, IEEE Journal of Selected Topics in Signal Processing.

[9]  R. M. A. P. Rajatheva,et al.  Energy Detection of Unknown Signals in Fading and Diversity Reception , 2011, IEEE Transactions on Communications.

[10]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[11]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[12]  Rahim Tafazolli,et al.  Frequency-Domain Differential Energy Detection Based on Extreme Statistics for OFDM Source Sensing , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[13]  Mikko Valkama,et al.  Reduced Complexity Spectrum Sensing Based on Maximum Eigenvalue and Energy , 2013, ISWCS.

[14]  Yonghong Zeng,et al.  Eigenvalue-based spectrum sensing algorithms for cognitive radio , 2008, IEEE Transactions on Communications.

[15]  Giorgio Taricco.  On the Accuracy of the Gaussian Approximation With Linear Cooperative Spectrum Sensing Over Rician Fading Channels , 2010, IEEE Signal Processing Letters.

[16]  Rahim Tafazolli,et al.  Cluster-Based Differential Energy Detection for Spectrum Sensing in Multi-Carrier Systems , 2012, IEEE Transactions on Signal Processing.

[17]  Kalle Ruttik,et al.  Detection of Unknown Signals in a Fading Environment , 2009, IEEE Communications Letters.

[18]  S. Haykin,et al.  Modern Wireless Communications , 1939, Nature.

[19]  Hai Jiang,et al.  MGF Based Analysis of Area under the ROC Curve in Energy Detection , 2011, IEEE Communications Letters.

[20]  Rahim Tafazolli,et al.  A novel low complexity differential energy detection for sensing OFDM sources in low SNR environment , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[21]  Vladimir I. Kostylev,et al.  Energy detection of a signal with random amplitude , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[22]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .