Publication Type : Conference Paper
Publisher : IEEE Xplore
Source : 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2022, pp. 406-411
Url : https://ieeexplore.ieee.org/document/9668456
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : Yes
Year : 2022
Abstract : Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system is a promising technology that provides high capacity and high data rate transmission in 5G and beyond. Presence of a large number of antennas in a MIMO-OFDM system increases the overhead on pilot symbols used for channel estimation. Our proposed work exploits the channel sparsity to design a pilot based compressed sensing method for channel estimation and integrates a Bi-LSTM approach for symbol detection for improved performance. Additionally, we optimize the pilot symbols to minimize the error value to be effectively within the total power constraint. We evaluate the performance of our proposed system using mean square error (MSE) with least minimum mean square estimate (LMMSE) as the benchmark. We demonstrate the design and evaluation of a scalable and efficient approach to joint channel estimation and symbol detection in a MIMO-OFDM system using a fewer number of pilot symbols.
Cite this Research Publication : Aswathy K. Nair and Vivek Menon, "Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM," 2022 14th International Conference on Communication Systems & NETworkS (COMSNETS), 2022, pp. 406-411, doi:10.1109/COMSNETS53615.2022.9668456