MMSE-based local ML detection of linearly precoded OFDM signals

Abstract

Linear precoding is a well known effective technique to boost the performance of orthogonal frequency-division multiplexing (OFDM) systems. A drawback of linearly precoded OFDM (LP-OFDM) systems is the high computational complexity required by maximum-likelihood (ML) detection, which is mandatory to capture all the channel diversity. Conversely, low-complexity techniques, such as the linear minimum mean-squared error (MMSE) detection, suffer from nonnegligible performance loss with respect to the ML performance. This paper proposes a detection technique that performs a local ML (LML) search in the neighborhood of the output provided by the MMSE detector. The trade-off between performance and complexity of the proposed LML-MMSE detector, which fall between the ones of the MMSE and ML detectors, can be nicely adjusted by appropriately setting the neighborhood size. Simulation results show that the LML-MMSE detector with minimum neighborhood size outperforms a block decision-feedback equalization (DFE) approach, while preserving a similar complexity.