Improved Energy Detection With Laplacian Noise in Cognitive Radio

Contaminated by Laplacian noise, the performance of energy detection (ED) degrades. To address this problem, an improved energy detection (i-ED) is proposed. Instead of the square of the received signal amplitude in the ED, an arbitrary exponent varying from 0 to 2 is adopted in the proposed algorithm. By deriving the expressions of the false alarm probability and detection probability, we propose an approach to determine the optimal exponent for a given cognitive radio system scenario. Both theoretical analysis and simulation results demonstrate that, by selecting the optimal exponent, the proposed i-ED outperforms ED and the absolute-value cumulating detection in terms of detection performance and robustness to the noise uncertainty.

[1]  Dhaval K. Patel,et al.  Non-parametric Blind Spectrum Sensing Based on Censored Observations for Cognitive radio , 2015, J. Signal Process. Syst..

[2]  Jiangtao Xi,et al.  Spectrum Sensing Using Weighted Covariance Matrix in Rayleigh Fading Channels , 2015, IEEE Transactions on Vehicular Technology.

[3]  Derrick Wing Kwan Ng,et al.  Power Efficient Resource Allocation for Full-Duplex Radio Distributed Antenna Networks , 2015, IEEE Transactions on Wireless Communications.

[4]  Zan Li,et al.  A novel sequential spectrum sensing method in cognitive radio using suprathreshold stochastic resonance , 2014, 2013 IEEE Global Communications Conference (GLOBECOM).

[5]  Luis Vergara,et al.  An extended energy detector for non-Gaussian and non-independent noise , 2009, Signal Process..

[6]  Wei-Ping Zhu,et al.  Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise , 2014, Signal Process..

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

[8]  Arnold Neumaier,et al.  Introduction to Numerical Analysis , 2001 .

[9]  Norman C. Beaulieu,et al.  UWB receiver designs based on a gaussian-laplacian noise-plus-MAI model , 2010, IEEE Transactions on Communications.

[10]  Jian Shen,et al.  A novel spectrum sensing method in cognitive radio based on suprathreshold stochastic resonance , 2012, 2012 IEEE International Conference on Communications (ICC).

[11]  Z. Liu,et al.  Spectrum sensing in cognitive radios based on enhanced energy detector , 2012, IET Commun..

[12]  Julian Cheng,et al.  Collaborative Spectrum Sensing in a Cognitive Radio System with Laplacian Noise , 2012, IEEE Communications Letters.

[13]  Norman C. Beaulieu,et al.  Energy-Efficient Optimal Power Allocation for Fading Cognitive Radio Channels: Ergodic Capacity, Outage Capacity, and Minimum-Rate Capacity , 2016, IEEE Transactions on Wireless Communications.

[14]  Norman C. Beaulieu,et al.  Designing Time-Hopping Ultrawide Bandwidth Receivers for Multiuser Interference Environments , 2009, Proceedings of the IEEE.

[15]  Bo Ai,et al.  Absolute Value Cumulating Based Spectrum Sensing with Laplacian Noise in Cognitive Radio Networks , 2015, Wirel. Pers. Commun..

[16]  Norman C. Beaulieu,et al.  Feasibility of maximum eigenvalue cooperative spectrum sensing based on Cholesky factorisation , 2016, IET Commun..

[17]  Shi Jin,et al.  A Full-Space Spectrum-Sharing Strategy for Massive MIMO Cognitive Radio Systems , 2016, IEEE Journal on Selected Areas in Communications.

[18]  Yunfei Chen,et al.  Improved energy detector for random signals in gaussian noise , 2010, IEEE Transactions on Wireless Communications.

[19]  Pramod K. Varshney,et al.  Polarity-Coincidence-Array Based Spectrum Sensing for Multiple Antenna Cognitive Radios in the Presence of Non-Gaussian Noise , 2011, IEEE Transactions on Wireless Communications.

[20]  Panayiotis G. Georgiou,et al.  Alpha-Stable Modeling of Noise and Robust Time-Delay Estimation in the Presence of Impulsive Noise , 1999, IEEE Trans. Multim..

[21]  Konstantinos N. Plataniotis,et al.  An Accurate Kernelized Energy Detection in Gaussian and non-Gaussian/Impulsive Noises , 2015, IEEE Transactions on Signal Processing.

[22]  Ranjan K. Mallik,et al.  Performance of an Improved Energy Detector in Multihop Cognitive Radio Networks , 2016, IEEE Transactions on Vehicular Technology.

[23]  Donald R. Ucci,et al.  Microwave Oven Signal Modelling , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[24]  Z. Hasan,et al.  Some research issues in cognitive radio networks , 2007, AFRICON 2007.

[25]  S. Kassam.  Signal Detection in Non-Gaussian Noise , 1987 .

[26]  Wei-Ping Zhu,et al.  Spectrum sensing based on fractional lower order moments for cognitive radios in α-stable distributed noise , 2015, Signal Process..

[27]  Zhijin Qin,et al.  Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes , 2016, IEEE Transactions on Signal Processing.

[28]  Yinghui Ye,et al.  Performance of Spectrum Sensing Based on Absolute Value Cumulation in Laplacian Noise , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[29]  Feifei Gao,et al.  Enhanced multi-parameter cognitive architecture for future wireless communications , 2015, IEEE Communications Magazine.

[30]  Ali Olfat,et al.  Soft-Limited Polarity-Coincidence-Array Spectrum Sensing in the Presence of Non-Gaussian Noise , 2017, IEEE Transactions on Vehicular Technology.

[31]  Konstantinos N. Plataniotis,et al.  Accurate kernel-based spectrum sensing for Gaussian and non-Gaussian noise models , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[32]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[33]  Lei Shen,et al.  Blind Spectrum Sensing for Cognitive Radio Channels with Noise Uncertainty , 2011, IEEE Transactions on Wireless Communications.