PSO-Optimized Power Allocation in NOMA-QAM for Beyond 5G: A CFD and MFD Analysis
الموضوعات : Wireless Network
1 - IET Lucknow
الکلمات المفتاحية: NOMA, MFD, CFD, PSO, 5G, NGN,
ملخص المقالة :
Cognitive Radio (CR) and NOMA are two techniques that are showing out to be the most promising in the current context for improving 5G spectrum usage. New gadgets, IOT and expanding wireless applications has created a tremendous demand of spectrum in the ISM groups. With CR allowing secondary users (SU) to determine and make use of idle spectrum components the FCC permits unlicensed users to use spectrum without any interference to primary users. To address spectrum scarcity, Cognitive Radio (CR) techniques have been developed. However, the lack of standardization has led to numerous variations of CR algorithms. Through this paper we aim to offer a thorough summary of CR techniques, including recent and emerging methods. Unlike traditional methods that limit spectrum use when it's idle, recent NOMA advancements enhance spectrum efficiency. Our research suggests a method that utilizes Particle Swarm Optimization (PSO) to enhance the detection probability of Cyclostationary Feature (CFD) and Match Filter Detection. Also we have optimized power allocation for NOMA users using PSO algorithm. Iterative simulation and optimization is carried out for various Pfa values, and findings are presented to demonstrate the relationship between Pd and Pf. Based upon the results of the simulations the optimization of CFD technique has a shorter sensing time and achieves detection at the lowest SNR possible for a fixed threshold configuration.
[1] J. Mitola and G. Q. Maguire, “Cognitive radio: Making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13–18, 1999.
[2] N. Muchandi and R. Khanai, “Cognitive radio spectrum sensing: A survey,” in 2016 Int. Conf. on Electrical, Electronics, and Optimization Techniques (ICEEOT), Lahore, Pakistan, pp. 3233–3323, 2016.
[3] A. Kumar, S.Bharti and M.Gupta, “FBM Cvs. OFDM:5g mobile communication system, ”International Journal of Systems, Control and Communications, vol. 10, no. 3, pp. 250–264, 2019.
[4] J. E. Kasser, “Amateur radio: Past, present and future,” in Proc. of the 1995 Int. Conf. on 100 Years of Radio, London, UK, pp. 120–127, 1995.
[5] S. Haykin, D. J. Thomson and J. H. Reed, “Spectrum sensing for cognitive radio,” Proc. of the IEEE, vol. 97, no. 5, pp. 849–877, 2009.
[6] A. Kumar, M. K. Sharma, K. Sengar and S. Kumar, “NOMA based CR for QAM-64 and QAM-256,” Egyptian Informatics Journal, vol. 21, no. 2, pp. 67–71, 2020.
[7] A. Kumar and P. N. Kumar, “OFDM system with Cyclostationary feature detection spectrum sensing,” ICT Express, vol. 5, no. 1, pp. 21–25, 2019.
[8] Y. Liang, K. Chen, G. Y. Li and P. Mahonen, “Cognitive radio networking and communications: An overview, ”IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386–3407, 2011.1216 CSSE, 2022, vol.43, no.3
[9] M. H. Islam, C. L. Koh, S. W. Oh, X. Qing, Y. Y. Lai et al., “Spectrum survey in Singapore: Occupancy measurements and analysis,” in 3rd Int. Conf. on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), Singapore, pp. 1–7, 2008.
[10] D. Datla, A. M. Wyglinski and G. J. Minden, “A spectrum surveying framework for dynamic spectrum access networks,” IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4158–4168, 2009.
[11] A. Goldsmith, S. A. Jafar, I. Maric and S. Srinivasa, “Breaking spectrum gridlock with cognitive radios: An information-theoretic perspective: An information theoretic perspective,” Proc. of the IEEE, vol. 97, no. 5, pp. 894–914, 2009.
[12] K. Tumuluru, P. Wang and D. Niyato, “A Novel spectrum-scheduling scheme for multichannel cognitive radio network and performance analysis,” IEEE Transactions on Vehicular Technology, vol. 60, no. 4, pp. 1849 1858, 2011.
[13] P. A. Navrátil, H. Childs, D. S. Fussell and C. Lin, “Exploring the spectrum of dynamic scheduling algorithms for scalable distributed-memory ray tracing,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 6, pp. 893–906, 2014.
[14] P. Nandhakumar and A. Kumar, “Analysis of OFDM system with energy detection spectrum sensing,” IndianJournal of Science and Technology, vol. 9, no. 16, pp. 1–6, 2016.
[15] S. A. Saberali and N. C. Beaulieu, “Matched-filter detection of the presence of MPSK signals,” in 2014 Int. Symp. on Information Theory and its Applications, Victoria, BC, Canada, pp. 85–89, 2014.
[16] K. Kim, I. A. Akbar, K. K. Bae, J. Um, C. M. Spooner et al., “Cyclostationary approaches to signal detection and classification in cognitive radio,” in 2007 2nd IEEE Int. Symp. on New Frontiers in Dynamic Spectrum Access Networks, Dublin, Ireland, pp. 212–215, 2007.
[17] R. S. Roberts, W. A. Brown and H.H.Loomis, “Computationally efficient algorithms for cyclic spectral analysis,”IEEE Signal Processing Magazine, vol. 8, no. 2, pp. 38–49, 1991.
[18] D. Noguera, L. Biard and M. Laugeois, “Cyclostationary detectors for cognitive radio: Architectural tradeoffs, ”Journal of Wireless Communication Network, Vol. 2010, no. 1, pp. 1–8, 2010.
[19] M. Ghozzi, M. Dohler, F. Marx and J. Palicot, “Cognitive radio: Methods for detection of free bands,” Comptes Rendus Physique, vol. 7, no. 7, pp. 794–804, 2006.
[21] I. F. Akyildiz, W. Y. Lee, M. C. Vuran and S. Mohanty, “Next generation/dynamic spectrum access cognitive radio wireless network-A survey,” Computer Networks, vol. 5, no. 13, pp. 2127–2159, 2006.
[22] C. Tu and B. Champagne, “Subspace-Based blind channel estimation for MIMO-OFDM systems with reduced time averaging,” IEEE Transactions on Vehicular Technology, vol. 59, no. 3, pp. 1539–1544, 2010.
[23] J. Xuehua and C. Peijiang, “Research and Simulation of MIMO-OFDM Wireless Transmission System,” in2009 Int. Forum on Information Technology and Applications, China, pp. 83–86, 2009.
[24] Y. Zhou, Y. Wang, T. Wang and K. Zhang, “Iterative inter-cell interference coordination in MU-MIMO systems, “in Vehicular Technology and advancements Conf. or abbreviated as VTC Spring 2011 IEEE 73rd, Budapest, Hungary, pp. 1–5, 2011.
[25] J. G. Andrews, S. Buzzi, W. Cho, V. S. Hanly, A. Lozano et al., “What will 5G be,” IEEE Journal on Selected Areas in Communication, vol. 32, no. 6, pp. 1065–1082, 2014.
[28] S. Srinu and S. Sab, “Co-operative wideband sensing based on Cyclostationary features with multiple malicious user elimination,” International Journal of Electronics and Communication, vol. 68, no. 7, pp. 702–709, 2013.CSSE, 2022, vol.43, no.3 1217
[29] Z. Quan, S. Cui, A. H. Sayed and H.V.Poor, “Optimal multiband joint detection for spectrum sensing in cognitive radio networks,” Transactions on Signal Processing, vol. 57, no. 3, pp. 1128–1140, 2009.
[30] J. E. Salt and H. H. Nguyen, “Performance prediction for energy detection of unknown signals,” IEEE Transactions on Vehicular Technology, vol. 57, no. 6, pp. 3900–3904, 2008.
[31] D. Na and K. Choi, “DFT spreading-based low PAPR FBMC with embedded side information,” IEEE Transactions on Communications, vol. 68, no. 3, pp. 1731–1745, 2020.
[32] Z. He, L. Zhou, Y. Chen and X. Ling, “Low-complexity PTS scheme for PAPR reduction in FBMC-OQAM systems,” IEEE Communication Letter, vol. 22, no. 11, pp. 2322–2325, 2018.
[33] A. Kumar and M. Gupta, “A review on activities of fifth generation mobile communication system,” Alexandria Engineering Journal, vol. 57, no. 2, pp. 1125–1135, 2018.
[34] A. K. Bairagi, Md. S. Munir, M. Alsenwi, N. H. Tran, S. S. Alshamrani et al., “Coexistence mechanism between eMBB and uRLLC in 5g wireless networks,” IEEE Transactions on Communications, vol. 69, no. 3, pp. 1736 ,1749, 2021.
[35] M.Mounir, Mb.M.El,S.Berra, G. S. GabaandM. Masud,“A novel hybrid precoding-companding technique for peak-to-average power ratio reduction in 5G and beyond,” Sensors, vol. 21, no. 4, pp. 1410, 2021.
[36] A. Kumar, S. Ambigapathy, M. Masud, E. S. Jaha, S. Chakravarty et al., “An efficient hybrid PAPR reduction for 5G NOMA-FBMC waveforms,” Computers, Materials & Continua, vol. 69, no. 3, pp. 2967–2981, 2021
[37] Xu, Wenbo, and Jun Sun. "Adaptive parameter selection of quantum-behaved particle swarm optimization on global level." Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I 1. Springer Berlin Heidelberg, 2005.
[38].Miah, Md Sipon, Michael Schukat, and Enda Barrett. "Sensing and throughput analysis of a MU-MIMO based cognitive radio scheme for the Internet of Things." Computer Communications 154 (2020): 442-454.
[39].Ramamurthy, Ramesh, et al. "Analysis of Cognitive Radio for LTE and 5G Waveforms." Computer. Syst. Sci. Eng. 43.3 (2022): 1207-1217.
[40]. Pham, Quoc-Viet, et al. "Swarm intelligence for next-generation networks: Recent advances and applications." Journal of Network and Computer Applications 191 (2021): 103141.
