Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels

In this paper, we investigate the performance of energy detection-based spectrum sensing over F composite fading channels. To this end, an analytical expression for the average detection probability is firstly derived. This expression is then extended to account for collaborative spectrum sensing, squarelaw selection diversity reception and noise power uncertainty. The corresponding receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to-noise ratio (SNR), noise power uncertainty, time-bandwidth product, multipath fading, shadowing, number of diversity branches and number of collaborating users. It is shown that the energy detection performance is sensitive to the severity of the multipath fading and amount of shadowing, whereby even small variations in either of these physical phenomena can significantly impact the detection probability. As a figure of merit to evaluate the detection performance, the area under the ROC curve (AUC) is derived and evaluated for different multipath fading and shadowing conditions. Closed-form expressions for the differential entropy and cross entropy are also formulated and assessed for different average SNR, multipath fading and shadowing conditions. Then the relationship between the differential entropy of F composite fading channels and the corresponding ROC/AUC is examined where it is found that the average number of bits required for encoding a signal becomes small (i.e., low differential entropy) when the detection probability is high or when the AUC is large. The difference between composite fading and traditional smallscale fading is emphasized by comparing the cross entropy for Rayleigh and Nakagami-m fading. A validation of the analytical results is provided through a careful comparison with the results of some simulations.

[1]  Amir Ghasemi,et al.  Impact of User Collaboration on the Performance of Sensing-Based Opportunistic Spectrum Access , 2006, IEEE Vehicular Technology Conference.

[2]  Mikko Valkama,et al.  Subband Energy Based Reduced Complexity Spectrum Sensing Under Noise Uncertainty and Frequency-Selective Spectral Characteristics , 2016, IEEE Transactions on Signal Processing.

[3]  Anil Vohra,et al.  Analysis of different Spectrum Sensing techniques , 2017, 2017 International Conference on Computer, Communications and Electronics (Comptelix).

[4]  Mikko Valkama,et al.  Efficient Energy Detection Methods for Spectrum Sensing Under Non-Flat Spectral Characteristics , 2015, IEEE Journal on Selected Areas in Communications.

[5]  George K. Karagiannidis,et al.  Entropy and Channel Capacity under Optimum Power and Rate Adaptation over Generalized Fading Conditions , 2015, IEEE Signal Processing Letters.

[6]  Marcelo S. Alencar,et al.  Performance of Cognitive Spectrum Sensing Based on Energy Detector in Fading Channels , 2015 .

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

[8]  Pierce E. Cantrell,et al.  Comparison of generalized Q- function algorithms , 1987, IEEE Trans. Inf. Theory.

[9]  Neelam Srivastava,et al.  A survey on energy detection schemes in cognitive radios , 2016, 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES).

[10]  Miguel López-Benítez,et al.  Signal Uncertainty in Spectrum Sensing for Cognitive Radio , 2013, IEEE Transactions on Communications.

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

[12]  Sattar Hussain,et al.  Closed-Form Analysis of Relay-Based Cognitive Radio Networks Over Nakagami- $m$ Fading Channels , 2014, IEEE Transactions on Vehicular Technology.

[13]  Saeed Gazor,et al.  Distributed Cooperative Spectrum Sensing in Mixture of Large and Small Scale Fading Channels , 2013, IEEE Transactions on Wireless Communications.

[14]  Liuqing Yang,et al.  Cooperative Diversity of Spectrum Sensing for Cognitive Radio Systems , 2010, IEEE Transactions on Signal Processing.

[15]  Amir Ghasemi,et al.  Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs , 2008, IEEE Communications Magazine.

[16]  Haroon Rasheed,et al.  Performance analysis of Rice-Lognormal channel model for spectrum sensing , 2010, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[17]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[18]  Hai Jiang,et al.  Performance of an Energy Detector over Channels with Both Multipath Fading and Shadowing , 2010, IEEE Transactions on Wireless Communications.

[19]  Valentine A. Aalo,et al.  Energy detection of unknown signals in Gamma-shadowed Rician fading environments with diversity reception , 2015, IET Commun..

[20]  Geoffrey Ye Li,et al.  Cognitive radio networking and communications: an overview , 2011, IEEE Transactions on Vehicular Technology.

[21]  Oluwatobi Olabiyi,et al.  A performance study of energy detection for dual-hop transmission with fixed gain relays: area under ROC curve (AUC) approach , 2011, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications.

[22]  Yong-Gang Zhu,et al.  The simulation study of entropy-based signal detector over fading channel , 2012, 2012 International Conference on Wireless Communications and Signal Processing (WCSP).

[23]  Norman C. Beaulieu,et al.  New results on selection diversity , 1998, IEEE Trans. Commun..

[24]  Zhetao Li,et al.  Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[25]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[26]  Hussien Al-Hmood Performance of Cognitive Radio Systems over κ-μ Shadowed with Integer μ and Fisher-Snedecor F Fading Channels , 2018, ArXiv.

[27]  Linda Doyle,et al.  Cyclostationary Signatures in Practical Cognitive Radio Applications , 2008, IEEE Journal on Selected Areas in Communications.

[28]  Mikko Valkama,et al.  A Comprehensive Framework for Spectrum Sensing in Non-Linear and Generalized Fading Conditions , 2017, IEEE Transactions on Vehicular Technology.

[29]  T. Wickens Elementary Signal Detection Theory , 2001 .

[30]  Incremental-Precision Based Feature Computation and Multi-Level Classification for Low-Energy Internet-of-Things , 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[31]  Hai Jiang,et al.  Analysis of area under the ROC curve of energy detection , 2010, IEEE Transactions on Wireless Communications.

[32]  Pushpa N. Rathie,et al.  On the entropy of continuous probability distributions (Corresp.) , 1978, IEEE Trans. Inf. Theory.

[33]  Xianjun Deng,et al.  Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring , 2017, IEEE Access.

[34]  Mikko Valkama,et al.  Sparse Frequency Domain Spectrum Sensing and Sharing Based on Cyclic Prefix Autocorrelation , 2017, IEEE Journal on Selected Areas in Communications.

[35]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[36]  Colin C. Murphy,et al.  Fast and Accurate Approximations for the Analysis of Energy Detection in Nakagami-m Channels , 2013, IEEE Communications Letters.

[37]  Seong Ki Yoo,et al.  The Fisher-Snedecor F distribution: A Simple and Accurate Composite Fading Model , 2017 .

[38]  George K. Karagiannidis,et al.  On the Monotonicity of the Generalized Marcum and Nuttall ${Q}$ -Functions , 2007, IEEE Transactions on Information Theory.

[39]  Hai Jiang,et al.  Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks , 2011, IEEE Transactions on Wireless Communications.