A Modified Smart Candidate Adding Algorithm with Fixed-complexity for Soft-output MIMO Detection

A multiple-input multiple-output (MIMO) system has been adopted in many modern wireless communication standards. The MIMO system can greatly improve the transmission capacity of the wireless communication. However, its computational complexity at the receiver also increases. The smart ordering and candidate adding (SOCA) algorithm that achieves near max-log optimal error-rate performance with low and fixed computational complexity was proposed recently. The proposed algorithm combines a smart-ordered QR decomposition with candidate adding and a parallel breadth-first search of the detection tree to achieve its desirable performance complexity trade-off.Nevertheless, it can provide soft-outputs counterhypotheses so it must try to find out not only the optimal solution but also the counterhypotheses of the optimal solution. In this study, we propose a method to reduce the complexity of searching for the counterhypotheses so generated. This technique can be reduce the search complexity while achieving a near-optimal performance.