An improved multiple feedback successive interference cancellation algorithm for MIMO detection

Symbol vector detection in multiple-input multiple-output (MIMO) spatial multiplexing systems is gaining a lot of research attention. The optimal (minimum) bit error rate (BER) performance in spatially multiplexed MIMO systems can be achieved by employing maximum likelihood detection (MLD) at the receiver end. However, MLD performs an exhaustive search over all possible transmit vectors which is computationally impractical when number of antennas or the modulation order increases. With the motivation of detecting symbol vector in MIMO systems with less computational complexity, we propose an improved multiple feedback successive interference cancellation (IMF-SIC) algorithm in this paper. The multiple feedback (MF) strategy in successive interference cancellation (SIC) is based on the concept of shadow area constraint (SAC) where multiple neighboring constellation points are used in the decision feedback loop if the decision falls in the shadow region. In improved MF strategy, the SAC criteria is checked recursively which results in a better BER performance. Further, to achieve a higher detection diversity, we also propose a multiple branch IMF-SIC (MB-IMF-SIC) algorithm where we incorporate the concept of multiple branch (MB) processing. Simulation results show that the proposed algorithms outperform the existing SIC and MF-SIC based MIMO detectors, and achieves a near optimal BER performance.