Efficiently using extrinsic gain for candidate vectors selection in QR-LRL based IDD MIMO receiver

Various OSIC MIMO detectors are considered suboptimal in terms of performance and complexity, however suffers from error propagation. QR-LRL is considered to be most effective to mitigate error performance as it detects the least reliable layer(LRL) symbol first. It achieves hard ML performance, but suffers from empty vector set(EVS) problem for soft output generation. Some of the previous work mitigates this problem effectively and achieves soft ML performance at the cost of complexity. QR-LRL based IDD(Iterative Detection and Decoding) is an alternative solution, which exchanges the extrinsic information between detector and decoder. Based on feed back knowledge from decoder, decision is made to update candidate vector set for soft output generation. Simulation results shows that significant performance improvement is achieved while keeping the receiver design simple.