Novel Two-Stage Detection for MIMO OFDM Systems with Reduced Complexity

Two new fixed effort two-stage detection algorithms for Multiple Input Multiple Output (MIMO) systems using orthogonal frequency division multiplexing (OFDM) are proposed, reaching near maximum likelihood (ML) performance with a significantly reduced complexity. For enhancing the data rates in wireless communication systems a higher bandwidth is needed, resulting in a frequency selective channel with multiple reflecting transmission paths. The resulting higher complexity at the receiver side can be reduced by performing the equalization in the frequency domain, where each subchannel can be considered as a flat MIMO channel. The implementation of an efficient MIMO detector with low effort based on an analytic criterion is strongly needed to achieve the optimum hardware exploitation in concrete practical systems. Therefore our reduced search algorithms (RSML and IRS-ML), performing the search over a reduced set of candidates based on pre-computed search set missing probabilities and distance ratios for flat MIMO channels, are adapted to the OFDM system model. Furthermore our new OFDM-RS-ML and OFDM-IRS-ML algorithms are proposed reaching ML performance with only 1/8 of the needed computational effort compared to the RS-ML and IRS-ML algorithms by additionally considering the conditions of the different subchannel matrices during the selection of the candidates. Therefore the OFDM- (I)RS-ML algorithms offer a higher flexibility reaching near ML performance with significantly reduced computational effort.