Group-wise blind OFDM ML detection for complexity reduction

This paper presents a low-complexity blind Maximum-Likelihood (ML) detector for Orthogonal Frequency Division Multiplexing (OFDM) systems in block fading channels. The receiver complexity is reduced by subcarrier grouping (SG) for which the OFDM block is partitioned into smaller groups, and then the data are detected on a group-by-group basis. An identifiability analysis is also provided. We show that the data in each group can be identified under a more relaxed condition than that in [1], therefore enabling us to use smaller group size for implementation efficiency. Our simulation results show that the proposed detector can provide good symbol error performance even when the group size is much smaller than the discrete Fourier transform size.