Volume 9 • Issue 1 • PP: 34-45 • 2025
Cooperative Spectrum Sensing Architecture for Energy-Efficient Data-Fusion-Based Cognitive Radio Network
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This demand can be satisfied by cognitive radio (CR) technology thanks to the growing desire to utilize existing radio frequency bands more effectively. This paper suggests a hardware-efficient, very-large spectrum sensor. In cooperative cognitive radio networks, data fusion is not provided by a new-scale integration (VLSI) architecture. The cooperative method to spectrum sensing and management as a proposed concept uses approaches for data fusion to address the difficulties. Our VLSI system delivers high throughput with exceptional performance by combining the latest sensing algorithms with an effective hardware architecture. The overall performance of the spectrum and spectrum awareness are enhanced by the cooperative theoretical radio communication system. In order to enable the network to make judgements that can be modified utilizing combined data from scattered spectrum sensors, the study examines the integration of network fusion techniques. The suggested scheme's primary characteristics are its hardware efficiency, low power consumption, and real-time flexibility for changing spectrum conditions. Through simulations and comparison with existing methods, it is assessed. System performance is tracked, and the results indicate that faster and more accurate spectrum sensing is required in order to apply notions of spectrum sharing that make sense.
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References
[1] Rohit B. Chaurasiya and R. Shrestha, "Hardware-Efficient and Fast Sensing-Time Maximum-Minimum-Eigenvalue-Based Spectrum Sensor for Cognitive Radio Network," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 11, pp. 4448-4461, Nov. 2019.
[2] Rahul Shrestha and R. B. Chaurasiya, "Fast Sensing-Time and Hardware-Efficient Eigenvalue-Based Blind Spectrum Sensors for Cognitive Radio Network," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 4, pp. 1296-1308, April 2020.
[3] M. S. Murty and R. Shrestha, "Reconfigurable and Memory-Efficient Cyclostationary Spectrum Sensor for Cognitive-Radio Wireless Networks," in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 8, pp. 1039-1043, Aug. 2018.
[4] K. Banović and A. C. Carusone, "A Sub-mW Integrating Mixer SAR Spectrum Sensor for Portable Cognitive Radio Applications," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 3, pp. 1110-1119, March 2018.
[5] R. B. Chaurasiya and R. Shrestha, "Area-Efficient and Scalable Data-Fusion Based Cooperative Spectrum Sensor for Cognitive Radio," in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 4, pp. 1198-1202, April 2021.
[6] Q. Xiao, L. Lu, J. Xie and Y. Liang, "FCNNLib: An Efficient and Flexible Convolution Algorithm Library on FPGAs," 2020 57th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, pp. 1-6, 2020.
[7] N. -S. Kim and J. M. Rabaey, "A Dual-Resolution Wavelet-Based Energy Detection Spectrum Sensing for UWB-Based Cognitive Radios," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 7, pp. 2279-2292, July 2018.
[8] V. Khatri and G. Banerjee, "A 0.25–3.25-GHz Wideband CMOS-RF Spectrum Sensor for Narrowband Energy Detection," in IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, vol. 24, no. 9, pp. 2887-2898, Sept. 2016.
[9] T.-H. Yu, O. Sekkat, S. Rodriguez-Parera, D. Markovic, and D. Cabric, “A wideband spectrum-sensing processor with adaptive detection threshold and sensing time,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 58, no. 11, pp. 2765–2775, Nov. 2011.
[10] S. Mishra, A. Sahai, and R. Brodersen, “Cooperative sensing among cognitive radios,” in Proc. IEEE Int. Conf. Commun., vol. 4, pp. 1658–1663 Jun. 2006.
a. Nevat, G. W. Peters and I. B. Collings, "Distributed Detection in Sensor Networks Over Fading Channels with Multiple Antennas at the Fusion Centre," in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 671-683, Feb.1, 2014.
[11] R. C. Dias Vilela Bomfin and R. A. A. de Souza, "Performance of centralized data-fusion cooperative eigenvalue-based spectrum sensing under correlated shadowed fading," 2015 International Workshop on Telecommunications (IWT), Santa Rita do Sapucai, Brazil, pp. 1-6, 2015.
[12] K. Baskar, K. Muthumanickam, P. Vijayalakshmi & S. Kumarganesh, (2024), “A Strong Password Manager Using Multiple Encryption Techniques”. Journal of The Institution of Engineers (India): Series B, pp. 1-8, DOI: https://doi.org/10.1007/s40031-024-01144-6.
[13] K. Saravanan, S. Anthoniraj, S. Kumarganesh, T. Senthil Kumar, Martin Sagayam. K “Power Adjustment Algorithm for Higher Throughput in Mobile Ad hoc Networks” International Conference of Computer Sciences and Renewable Energies 2021 (ICCSRE 2021), July 23- 24, at Agadir, Morocco. https://doi.org/10.1051/e3sconf/202129701064.
[14] N. Sugirtham, R. Sherine Jenny, B. Thiyaneswaran, S. Kumarganesh, et al., (2024), “Modified Playfair for Text File Encryption and Meticulous Decryption with Arbitrary Fillers by Septenary Quadrate Pattern” International Journal of Networked and Distributed Computing, Vol. 12, pp. 108–118, https://doi.org/10.1007/s44227-023-00019-4.
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