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A Novel Spectrum Sensing Technique for Cognitive Radios under Shadow Fading Environment
Background/Objectives: Cognitive radios are future communication devices which will have the ability to bring revolution in communication systems. The major role of cognitive radios to sense its surrounding environment to use white spaces in spectrum for complete utilization of spectrum band. Methods: This paper presents a lightweight solution to mitigate the shadow fading using the one-bit padding factor by using data packets or acknowledgements exchanged between the two cognitive ends. Findings: The proposed model called Multiple Frequencies Sensing − One-Bit Parent Frequency Management (MFS-OBPFM) works on the principle of exchange of bit information which is processed and analyzed on the receiver side, and analyzed for the spectrum availability pattern. The shadow fading patterns are found and analyzed in time domain and data is scheduled according to these shadow fading patterns. The frequency domain is designed according to the spectrum availability factor. The proposed model is expected to give the performance parameters of throughput, time complexity, total energy consumed and number of reports by the primary and secondary user. The proposed model has been tested on the basis of various performance parameters of energy, time, etc. The proposed model has shown efficient results in the terms of performance parameters than the existing models. Improvements: The proposed model is consuming less energy in different data volume transmission as compared to the energy efficient model in same data volume.
Cognitive Radios, Communication Systems, Shadow Fading, Spectrum Sensing.
- Mousavifar AS, Leung C. Energy Efficient Collaborative Spectrum Sensing Based on Trust Management in Cognitive Radio Networks, Wireless Communications, IEEE Transactions. 2015; 14(4):1927−39.
- Molisch A, Greenstein LJ, Shafi M. Propagation Issues for Cognitive Radio. Proceedings of the IEEE. 2009 May; 97(5):787−804.
- Zhao N, Fei RY, Hongjian S, Nallanathan A. EnergyEfficient Cooperative Spectrum Sensing Schemes for Cognitive Radio Networks. EURASIP Journal on Wireless Communications and Networking. 2013; 120(1):1−13.
- Umar R, Sheikh AUH. A Comparative Study of Spectrum Awareness Techniques for Cognitive Radio Oriented Wireless Networks, Physical Communication. 2013; 9:148−70.
- Ren W, Zhao Q, Swami A. Temporal Traffic Dynamics Improve the Connectivity of Ad Hoc Cognitive Radio Networks, IEEE/ACM Transactions on Networking. 2014; 22(1):124−36.
- Rebeiz E, Yuan FL, Urriza P, Markovic D, Cabric D. Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks, 2014, p. 1−13.
- Incebacak D, Zilan R, Tavli B, Barcelo-Ordinas JM, Garcia-Vidal J. Optimal Data Compression for Lifetime Maximization in Wireless Sensor Networks Operating in Stealth Mode, Ad Hoc Networks. 2014; 24:134−47.
- Spasojevic Z, Burns J. Performance Comparison of Frequency Hopping and Direct Sequence Spread Spectrum Systems in the 2.4 GHz Range”. In: Personal, Indoor and Mobile Radio Communications, 2000, 1, p. 426−30.
- RuiMin LU, GanHua LE, JinLing MA, Yong Chao LI, Wei H. A Numerical Comparison between FHSS and DSSS in Satellite Communication Systems with On-Board Processing. In: 2nd International Congress on Image and Signal Processing, 2009, p. 1−4.
- Li J, Zhang J, Tan Z. Bit Error Rate of the Primary user in a Cognitive Relay Network over Rayleigh-Fading Channels. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, 2009, p. 1−4.
- Raut RD, Kulat KD. BER Performance Maintenance at High Data Rates in Cognitive Radio. In: 20th International Conference on Electronics, Communications and Computer, IEEE, 2010, p. 62−67.
- Mitola III J. Cognitive Radio: Making Software Radios More Personal, IEEE Personal Communications. 1999; 6(4):13−18.
- Akyildiz IF, Lo BL, Balakrishnan R. Cooperative Spectrum Sensing in Cognitive Radio Networks: A survey, Elsevier, Physical Communication. 2011; 4(1):40−62
- Kumar R. Analysis of Spectrum Sensing Techniques in Cognitive Radio, IJICT. 2014; 4(1):437−44.
- Vijayakumar S, Malarvizhi M. Reconfigurable Filter Bank Multicarrier Modulation for Cognitive Radio Spectrum Sharing - A SDR Implementation, Indian Journal of Science and Technology. 2016 Feb; 9(6):1−6.
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