An improved data aided channel estimation technique using genetic algorithm for massive MIMO

Document Type

Conference Proceeding



Place of Publication

Sydney, Australia


School of Science




Noman, M. K., Shamsul, S. M., Hassan, S., & Pervin, R. (2018, December). An Improved Data Aided Channel Estimation Technique Using Genetic Algorithm for Massive MIMO. In 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (pp. 61-66). IEEE. Available here


With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowded and messy situation, massive MIMO is designed to work with hundreds of low-cost serving antennas at a time as well as improve the spectral efficiency at the same time. Time Division Duplexing (TDD) has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. Least Square (LS), Minimum Mean Square Error (MMSE) and Linear Minimum Mean Square Error (LMMSE) are the estimators commonly used for estimating channel matrices. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of Genetic Algorithm (GA) worked well on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms. GA provides optimal result in some few iterations in terms of Mean Square Error (MSE) with respect to SNRand computational complexity.