I. J. of Electrical & Electronics Research
Research Article | 
Performance Analysis of MIMO System Using Fish Swarm Optimization Algorithm
Author(s) : M. Kasiselvanathan1, S. Lakshminarayanan2, J. Prasad3 , K.B. Gurumoorthy4 and S. Allwin Devaraj5
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2 , Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 30 May 2022
e-ISSN : 2347-470X
Page(s) : 167-170
Abstract
During the signal identification process, massive multiple-input multiple-output (MIMO) systems must manage a high quantity of matrix inversion operations. To prevent exact matrix inversion in huge MIMO systems, several strategies have been presented, which can be loosely classified into similarity measures and evolutionary computation. In the existing Neumann series expansion and Newton methods, the initial value will be taken as zero as a result wherein the closure speed will be slowed and the prediction of the channel state information is not done properly. In this paper, fish swarm optimization algorithm is proposed in which initial values are chosen optimally for ensuring the faster and accurate signal detection with reduced complexity. The optimal values are chosen between 0 to 1 value and the initial arbitrary values are chosen based on number of input signals. In the proposed work, Realistic condition based channel state information prediction is done by using machine learning algorithm. Simulation results demonstrate that the suggested receiver's bit error rate performance characteristics employing the Quadrature Amplitude Modulation (QAM) methodology outperform the existing Neumann series expansion and Newton methods.
Keywords: MMSE, BER, MIMO, Complexity, Signal Detection
M. Kasiselvanathan, Assistant Professor, Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, India; Email: kasiselvanathan@gmail.com
S. Lakshminarayanan, Assistant Professor, Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, India; Email: shrinarayanan20@gmail.com
J. Prasad, Assistant Professor, Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, India: prdece@gmail.com
K.B. Gurumoorthy, Assistant Professor (Ad hoc Faculty), Department of Electronics and Communication Engineering, National Institute of Technology, Calicut, India; Email: gurukb85@gmail.com
S. Allwin Devaraj, Assistant Professor, Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tamil Nadu, India; Email: babu.allwin@gmail.com
[1] Y. Zhu and H. Gao, “Improved binary artificial fish swarm algorithm and fast constraint processing for large scale unit commitment”, IEEE Access, vol. 8, pp. 152081-152092, 2020.[Cross Ref]
[2] C. Zhang, X. Liang, Z. Wu, F. Wang, S. Zhang, Z. Zhang and X. You. “On the low-complexity, hardware-friendly tridiagonal matrix inversion for correlated massive MIMO systems”, IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6272-6285, 2019. http://doi.org/10.1109/TVT.2019.2915171.[Cross Ref]
[3] F. Jin, Q. Liu, H. Liu and P. Wu, “A low complexity signal detection scheme based on improved Newton iteration for massive MIMO systems”, IEEE Communications Letters, vol. 23, no. 4, pp. 748-751, 2019.[Cross Ref]
[4] L. Zou, H. Li, W. Jiang and X. Yang, “An improved fish swarm algorithm for neighborhood rough set reduction and its application”. IEEE Access, vol. 7, pp. 90277-90288, 2019.[Cross Ref]
[5] K. Guo, R. Qiu and C. Cai, “A power allocation algorithm based on artificial fish swarm algorithm for cognitive radio networks based on interference alignment”, In 2017 4th International Conference on Systems and Informatics (ICSAI), pp. 1013-1017, 2017. http://doi.org/10.1109/ICSAI.2017.8248434.[Cross Ref]
[6] L. Fang, L. Xu and D.D. Huang, “Low complexity iterative MMSE-PIC detection for medium-size massive MIMO”, IEEE Wireless Communications Letters, vol. 5, no. 1, pp. 108-111, 2015.[Cross Ref]
[7] D. Zhu, B. Li and P. Liang, “On the matrix inversion approximation based on Neumann series in massive MIMO systems”, In 2015 IEEE international conference on communications (ICC), pp. 1763-1769, 2015.[Cross Ref]
[8] M. Wu, B. Yin, G. Wang, C. Dick, J.R. Cavallaro and C. Studer, “Large-scale MIMO detection for 3GPP LTE: Algorithms and FPGA implementations”, IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 916-929, 2014.[Cross Ref]
[9] H. Holma, and A. Toskala, “LTE advanced: 3GPP solution for IMT-Advanced”, John Wiley & Sons, pp. 1-14, 2012.[Cross Ref]
[10] H.Q. Ngo, E.G. Larsson, and T.L. Marzetta, “Energy and spectral efficiency of very large multiuser MIMO systems”, IEEE Transactions on Communications, vol. 61, no. 4, pp. 1436-1449, 2013.[Cross Ref]
[11] F. Rusek, D. Persson, B.K. Lau, E.G. Larsson, T.L. Marzetta, O. Edfors and Tufvesson, F, “Scaling up MIMO: Opportunities and challenges with very large arrays”, IEEE signal processing magazine, vol. 30, no. 1, pp. 40-60, 2012.[Cross Ref]
M. Kasiselvanathan, S. Lakshminarayanan, J. Prasad, K.B. Gurumoorthy and S. Allwin Devaraj (2022), Performance Analysis of MIMO System Using Fish Swarm Optimization Algorithm. IJEER 10(2), 167-170. DOI: 10.37391/IJEER.100220.