Study of polarization spectrum sensing based on stochastic resonance in partial polarized noise

Wireless communications is one of the most rapidly developing segments of the telecommunications industry. A large amount of intelligent terminals occupy the radio spectrum, resulting in the reduction of radio spectrum resources. Cognitive radio, which requires rapid and exact spectrum sensing, is considered the most effective method to resolve this problem. This paper applies bistable stochastic resonance to polarization antenna spectrum sensing. To adhere to its requirements, frequency re-scaling is adopted to degrade high frequency to low frequency. Differential computing is used to wipe the direction current component from the output signal. Three algorithms including differential energy detection, generalized likelihood ratio test, generalized Hadamard ratio test, are then employed for spectrum sensing. The simulation experiment compares the three algorithms above in various channel conditions including additional white Gaussian noise, Rayleigh-fading, and partial polarized noise. The results indicate that bistable stochastic resonance can drastically enhance detection probability in low signal-to-noise-ratio, and partial polarized noise degenerates the accuracy of spectrum sensing.

[1]  Jingjing Yang,et al.  A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization , 2017, Wirel. Pers. Commun..

[2]  Shaoqian Li,et al.  Adaptive Bistable Stochastic Resonance Aided Spectrum Sensing , 2014, IEEE Trans. Wirel. Commun..

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

[4]  Shiwen Mao,et al.  Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey , 2018, IEEE Communications Surveys & Tutorials.

[5]  J. J. Yang,et al.  State of the Art and Challenges of Radio Spectrum Monitoring in China , 2017 .

[6]  Min Sheng,et al.  Throughput and Fairness Analysis of Wi-Fi and LTE-U in Unlicensed Band , 2017, IEEE Journal on Selected Areas in Communications.

[7]  Fangfang Liu,et al.  Polarization-Based Spectrum Sensing Algorithms for Cognitive Radios: Upper and Practical Bounds and Experimental Assessment , 2016, IEEE Transactions on Vehicular Technology.

[8]  Weidang Lu,et al.  5G-based green broadband communication system design with simultaneous wireless information and power transfer , 2018, Phys. Commun..

[9]  Zhu Han,et al.  Network Association Strategies for an Energy Harvesting Aided Super-WiFi Network Relying on Measured Solar Activity , 2016, IEEE Journal on Selected Areas in Communications.

[10]  Michel C. Jeruchim,et al.  Simulation of Communication Systems: Modeling, Methodology and Techniques , 2000 .

[11]  Marina Ruggieri,et al.  Special Issue on “Future Tele-Infrastructure for Multi-sensory Devices (FIND)” , 2017, Wirel. Pers. Commun..

[12]  Seung Joo Kim,et al.  Analysis and Security Evaluation of Security Threat on Broadcasting Service , 2017, Wirel. Pers. Commun..

[13]  Chunyan Feng,et al.  Spectrum Sensing for Cognitive Radios Based on Directional Statistics of Polarization Vectors , 2013, IEEE Journal on Selected Areas in Communications.

[14]  An He,et al.  A Survey of Artificial Intelligence for Cognitive Radios , 2010, IEEE Transactions on Vehicular Technology.

[15]  Hanyang Li,et al.  Study of spectrum sensing exploiting polarization: From optimal LRT to practical detectors , 2016, Digit. Signal Process..

[16]  Zhenyu Na,et al.  Multi-Modal Cooperative Spectrum Sensing Based on Dempster-Shafer Fusion in 5G-Based Cognitive Radio , 2018, IEEE Access.

[17]  Symeon Chatzinotas,et al.  Exploiting polarization for spectrum sensing in cognitive SatComs , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[18]  Jun Li,et al.  THash: A Practical Network Optimization Scheme for DHT-based P2P Applications , 2013, IEEE Journal on Selected Areas in Communications.

[19]  Lishuang Feng,et al.  Analysis of polarization noise in transmissive single-beam-splitter resonator optic gyro based on hollow-core photonic-crystal fiber. , 2017, Optics express.

[20]  Yanyang Zi,et al.  Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis , 2009 .

[21]  Hai Jiang,et al.  Relay Based Cooperative Spectrum Sensing in Cognitive Radio Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[22]  Enrico Zio,et al.  A Bayesian Optimal Design for Accelerated Degradation Testing Based on the Inverse Gaussian Process , 2017, IEEE Access.

[23]  Di He,et al.  A Novel Spectrum-Sensing Technique in Cognitive Radio Based on Stochastic Resonance , 2010, IEEE Transactions on Vehicular Technology.

[24]  Zhenyu Na,et al.  Optimal Resource Allocation in Simultaneous Cooperative Spectrum Sensing and Energy Harvesting for Multichannel Cognitive Radio , 2017, IEEE Access.

[25]  Luigi Paura,et al.  Widely Linear Cooperative Spectrum Sensing for Cognitive Radio Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[26]  Luigi Paura,et al.  Cooperative Spectrum Sensing Techniques with Temporal Dispersive Reporting Channels , 2011, IEEE Transactions on Wireless Communications.

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

[28]  Yihu Xu,et al.  Spectrum sensing using dual polarized multiple antennas in cognitive radio systems , 2012, 2012 18th Asia-Pacific Conference on Communications (APCC).