Iterative LS/MMSE Channel Estimation for OFDM Systems with Turbo Receiver Architectures

Abstract

Accurate channel state information (CSI) is essential for reliable orthogonal frequency division multiplexing (OFDM) transmissions, especially when training resources are limited and iterative receiver processing is employed. This paper revisits least squares (LS) and minimum mean square error (MMSE) channel estimation based on training sequences and analyzes their impact on an iterative turbo receiver framework. The initial channel estimate is obtained from an OFDM training transmission, while subsequent refinement is performed using soft information generated by a soft-input soft-output (SISO) equalizer and decoder. Unlike conventional approaches that keep the channel estimate fixed after the training phase, the proposed architecture enables decision-directed channel refinement using reconstructed transmit symbols. The OFDM stage is employed for channel estimation, whereas BER performance is evaluated using independently generated turbo-coded BPSK sequences transmitted through the analyzed channel. The performance analysis investigates the influence of training sequence length and signal-to-noise ratio (SNR) on iterative estimation gains. Simulation results show that, for short training sequences, the proposed iterative strategies can significantly improve BER performance compared with conventional non-iterative LS and MMSE estimators. These results provide practical insights for the design of training-efficient OFDM-based communication systems.