Modified Multiple Feedback QR Aided Successive Interference Cancellation Algorithm for Large MIMO Detection

Multiple-input multiple-output (MIMO) systems have attracted increased interest due to their capability to achieve higher multiplexing and diversity gains. In MIMO systems, reliable symbol detection in one of the major challenges. Maximum likelihood (ML) detection is one such technique which achieves minimum error rate performance for MIMO systems, however due to its exponential complexity ML detection is practically infeasible for large number of antennas. Therefore, in this contribution, we propose a low-complexity modified multiple feedback QR aided successive interference cancellation (MMF-SIC) algorithm which is capable of achieving near optimal performance. The effect of error propagation in SIC can be reduced by using multiple constellation points in decision feedback loops based on the reliability criteria. In MMF-SIC, an enhanced detection diversity is achieved by considering the decision feedback loops in multiple layers of QR aided SIC algorithm. Furthermore, we employ two different ordering schemes in parallel for QR decomposition in MMF-SIC algorithm. Through simulations, it is observed that the MMF-SIC algorithm performs superior over the conventional SIC and other SIC based techniques for detection in MIMO systems, and approach near optimal performance.