Turbo-Coded MIMO-OFDM Channel Estimation Using the Chaotic Grey Wolf Optimizer and Genetic Algorithm

Abstract

Turbo-Coded multiple-input multiple-output orthogonal frequency division multiplexing Channel Estimation, using Chaotic Grey Wolf Optimizer and the Genetic Algorithm (TC-MIMO-OFDM-Hybrid-CGWO-GA), is proposed in this manuscript. To enhance the performance of the bit error rate (BER), the proposed turbo code lessens the maximal correlation of the channel in the frequency domain. Initially, the channel is estimated using LS-MMSE individually using Chaotic Grey Wolf Optimizer (CGWO) and the LS-MMSE is combined with a genetic algorithm to scale the best channel for reducing the error. The encoding and decoding methods are done with the help of Turbo-codes for LS channel estimation. Then, the performance of the proposed method is analyzed with different performance metrics, such as the bit error rate, mean square error, channel estimation, computational cost, overhead, and symbol error rate. From the analysis, the proposed method attains lower computational costs of 99.67%, 98.38%, 92.34% and 97.45%, lower bit error rates of 98.33%, 89.34%, 83.12% and 88.96% and lower mean square errors of 93.15%, 91.25%, 79.90% and 92.88% analyzed to the existing methods, such as TSO-CE-TC-MIMO-OFDM, MFPA-CE-MIMO-OFDM, OSBS-CEA-MIMO-OFDM, and IAMO-CE-MIMO-OFDM.

KEYWORDS:

• Channel estimation
• Chaotic grey wolf optimization
• Genetic algorithm
• Multiple-input multiple-output orthogonal frequency division multiplexing
• Turbo code

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Chennapragada Padmaja

C Padmaja is currently pursuing the PhD degree at the Faculty of Electronics and Communication Engineering, G. Narayanamma Institute of Technology and Science (for women), Hyderabad, India. Her research interests include MIMO OFDM channel estimation techniques, channel coding techniques and diversity techniques and optimization schemes using machine learning algorithms. Corresponding author. Email: [email protected]

Boleti Lakshmi Malleswari

B L Malleswari is currently working as a principal at Sridevi Women's Engineering College, Hyderabad, India. Her research interests include modelling of GPS errors based on Kalman filters, implementation on FPGA for WCDMA systems and OFDM with diversity techniques. Email: [email protected]