Beamforming-based Spatial Precoding with Channel Estimation for Massive MIMO-OFDM system
Journal
IEEE Radio and Wireless Symposium, RWS
ISBN
9798350340457
Date Issued
2024-01-01
Author(s)
Chiu, Chen Hao
Abstract
Multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) is a dominant technology used in nowadays 4G/5G wireless communication systems. Applying massive number of antennas to MIMO-OFDM (massive MIMO-OFDM) exploits the merit of increasing spectral efficiency and enhancing the data rate through achieving spatial diversity. However, in a frequency-division duplex (FDD) system, the reduction of downlink training and channel state information (CSI) feedback is a critical issue. We adopt the beamforming-based spatial precoding (BBSP) method and further improve it by combining the grey wolf optimization (GWO) algorithm to solve this problem. Moreover, we consider the more realistic situation that imperfect CSI is given and introduce the channel estimation of MIMO-OFDM via orthogonal matching pursuit (OMP). Hence, a massive MIMO-OFDM BBSP system with channel estimation is proposed. The system preserves the advantage of BBSP that the overheads are reduced as well as a better performance in bit error rate (BER). Numerical results are provided to show that the proposed system possesses a competitive performance.
Subjects
beamforming | channel estimation | compressed sensing | MIMO-OFDM | spatial precoding
SDGs
Type
conference paper