PSO-Optimized Power Allocation in NOMA-QAM for Beyond 5G: A CFD and MFD Analysis
Subject Areas : Wireless Network
1 -
Keywords: Non Orthogonal Multiple Access (NOMA), Matched Filter Detection (MFD), CFD, PSO, Cognitive Radio Networks (CRN), Next Generation Networks (NGN),
Abstract :
This paper proposes a power allocation method based on particle swarm optimization (PSO) to enhance spectrum sensing performance in downlink Non Orthogonal Multiple Access (NOMA) systems employing high-order Quadrature Amplitude modulation (QAM) modulation for beyond 5G networks. By intelligently adjusting user power levels, the proposed approach significantly improves detection reliability while maintaining stringent false alarm constraints, even under challenging low-SNR conditions. The goal is to enhance spectrum sensing performance by maximizing the probability of detection (Pd) while maintaining a constrained probability of false alarm (Pf). Cyclostationary Feature Detection (CFD) and Matched Filter Detection (MFD) techniques are applied to evaluate detection performance under varying Signal to noise ratio (SNR) conditions. Simulation results demonstrate that the optimized framework not only strengthens detection performance particularly for high order QAM but also enhances overall system responsiveness. Also CFD surpasses MFD in higher SNR scenarios due to its ability to exploit cyclic features of modulated signals, which are preserved even in moderately noisy environments. The integration of PSO further enhances system performance, offering a practical and scalable solution for next-generation Internet of Things (IoT)-enabled spectrum sharing environments.
[1] Kumar, A., Bharti, S., & Gupta, M. (2019). FBMC vs. OFDM: 5G mobile communication system. International Journal of Systems, Control and Communications, 10(3), 250-264. DOI:10.5815/ijwmt.2023.05.01.
[2] Haykin, S., Thomson, D. J., & Reed, J. H. (2009). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5), 849-877. DOI:10.1109/JPROC.2009.2015711.
[3] Kumar, Arun, et al. "NOMA based CR for qam-64 and qam-256." Egyptian Informatics Journal 21.2 (2020): 67-71. DOI:10.5815/ijwmt.2023.05.01.
[4] Kumar, Arun, and P. Nandha Kumar. "OFDM system with Cyclostationary feature detection spectrum sensing." ICTExpress 5.1 (2019): 21-25. DOI:10.1016/j.icte.2018.01.007.
[5] Liang, Y. C., Chen, K. C., Li, G. Y., & Mahonen, P. (2011). Cognitive radio networking and communications: An overview. IEEE transactions on vehicular technology, 60(7), 3386-3407. DOI: 10.1109/TVT.2011.2158673.
[6] Datla, D., Wyglinski, A. M., & Minden, G. J. (2009). A spectrum surveying framework for dynamic spectrum access networks. IEEE [7]Transactions on Vehicular Technology, 58(8), 4158-4168. DOI: 10.1109/TVT.2009.2025380.
[8]Goldsmith, A., Jafar, S. A., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894-914. DOI: 10.1109/JPROC.2009.2015717.
[9] Tumuluru, V. K., Wang, P., & Niyato, D. (2011). A novel spectrum-scheduling scheme for multichannel cognitive radio network and performance analysis. IEEE Transactions on Vehicular Technology, 60(4), 1849-1858. DOI: 10.1109/TVT.2011.2117951.
[10] Navrátil, P. A., Childs, H., Fussell, D. S., & Lin, C. (2013). Exploring the spectrum of dynamic scheduling algorithms for scalable distributed-memoryray tracing. IEEE transactions on visualization and computer graphics, 20(6), 893-906. DOI: 10.1109/TVCG.2013.261.
[11] Nandhakumar, P., & Kumar, A. (2016). Analysis of OFDM system with energy detection spectrum sensing. Indian J. Sci. Technol, 9(16), 1-6. DOI: 10.17485/ijst/2016/v9i16/92224.
[12] Saberali, S. A., & Beaulieu, N. C. (2014, October). Matched-filter detection of the presence of MPSK signals. In 2014 International Symposium on Information Theory and its Applications (pp. 85-89). IEEE.DOI:0.1109/ISITA.2014.7001426.
[13] Kim, K., Akbar, I. A., Bae, K. K., Um, J. S., Spooner, C. M., & Reed, J. H. (2007, April). Cyclostationary approaches to signal detection and classification in cognitive radio. In 2007 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks (pp. 212-215). IEEE. DOI: 10.1109/DYSPAN.2007.39.
[14] Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer networks, 50(13), 2127-2159. DOI: 10.1016/j.comnet.2006.05.001.
[15] Tu, C. C., & Champagne, B. (2009). Subspace-based blind channel estimation for MIMO-OFDM systems with reduced time averaging. IEEE Transactions on Vehicular Technology, 59(3), 1539-1544.DOI: 10.1109/TVT.2009.2036728.
[16] Zhou, Y., Wang, Y., Wang, T., Zhang, K., & Zhang, W. (2011, May). Iterative inter-cell interference coordination in MU-MIMO systems. In 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE. DOI: 10.1109/VETECS.2011.5956799.
[17] Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5G be?. IEEE Journal on selected areas in communications, 32(6), 1065-1082. DOI: 10.1109/JSAC.2014.2328098.
[18] Srinu, S., & Sabat, S. L. (2013). Cooperative wideband sensing based on cyclostationary features with multiple malicious user elimination. AEU-International Journal of Electronics and Communications, 67(8), 702-707. DOI: 10.1016/j.aeue.2013.01.002.
[19] Quan, Z., Cui, S., Sayed, A. H., & Poor, H. V. (2008). Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE transactions on signal processing, 57(3), 1128-1140. DOI: 10.1109/TSP.2008.2008540.
[20] Na, D., & Choi, K. (2019). DFT spreading-based low PAPR FBMC with embedded side information. IEEE Transactions on Communications, 68(3), 1731-1745. DOI: 10.1109/TCOMM.2019.2952549.
[21] He, Z., Zhou, L., Chen, Y., & Ling, X. (2018). Low-complexity PTS scheme for PAPR reduction in FBMC-OQAM systems. IEEE Communications Letters, 22(11), 2322-2325. DOI: 10.1109/LCOMM.2018.2863908.
[22] Kumar, A., & Gupta, M. (2018). A review on activities of fifth generation mobile communication system. Alexandria Engineering Journal, 57(2), 1125-1135. DOI: 10.1016/j.aej.2017.06.014.
[23] Bairagi, A. K., et al. (2020). Coexistence mechanism between eMBB and uRLLC in 5G wireless networks. IEEE transactions on communications, 69(3), 1736-1749. DOI: 10.1109/TCOMM.2020.3046635.
[24] Mounir, M., et al. (2021). A novel hybrid precoding-companding technique for peak-to-average power ratio reduction in 5G and beyond. Sensors, 21(4), 1410. DOI: 10.3390/s21041410.
[25] Kumar, A., et al. (2021). An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms. Computers, Materials & Continua, 69(3). DOI: 10.32604/cmc.2021.019092.
[26] Miah, M. S., Schukat, M., & Barrett, E. (2020). Sensing and throughput analysis of a MU-MIMO based cognitive radio scheme for the Internet of Things. Computer communications, 154, 442-454. DOI: 10.1016/j.comcom.2020.02.040.
[27] Ramamoorthy, R., et al. (2022). Analysis of cognitive radio for lte and 5g waveforms. Computer Systems Science & Engineering, 43(3).DOI: 10.32604/csse.2022.019943.
[28] Pham, Q. V., et al. (2021). Swarm intelligence for next-generation networks: Recent advances and applications. Journal of Network and Computer Applications, 191, 103141. DOI: 10.1016/j.jnca.2021.103141.
[29] Bala Kumar, D., & Nanda Kumar, S. (2024). Block chain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output non orthogonal multiple access. Results in Engineering, 24, 102840.DOI: 10.1016/j.rineng.2024.102840.
[30] Salameh, H. B., & Hussienat, A. (2025). ML-Driven Feature-Based Spectrum Sensing for NOMA Signal Detection in Spectrum-agile IoT Networks under Fading Channels. IEEE Sensors Journal. DOI: 10.1109/JSEN.2025.3524968.
[31] Zhai, Q., Dong, L., Cheng, W., Li, Y., & Liu, P. (2023). Joint optimization for active IRS-aided multicluster NOMA systems. IEEE Systems Journal, 17(4), 6691-6694. DOI: 10.1109/JSYST.2022.3228965.