Performance evaluation of linear constellation precoded OFDM with linear equalizers in overestimated channels

Linear constellation precoded orthogonal frequency division multiplexing (LCP-OFDM) requires a maximum likelihood (ML) decoder to fully collect multipath diversity gain. However, the complexity of ML increases exponentially with the channel memory, signal constellation size, and the number of transmit/ receive antennas. Therefore, linear equalizers are highly desirable. This paper mainly analyzes minimum-mean-square equalization for MMSE-LCP-OFDM systems. We further analyze the performance of linear zero-forcing ZF-LCP-OFDM equalizers. In particular, we show that MMSE-LCP-OFDM equalization provides a significant performance improvement in the so-called overestimated channels. Unlike MMSE-LCP-OFDM, our analysis demonstrates that the performance of ZF-LCP-OFDM is in fact worse in overestimated channels from low to medium SNR region. Simulation results are provided to corroborate the theoretical analysis.