Reducing the Complexity of Quasi-ML Detectors for MIMO Systems Through Simplified Branch Metric and Accumulated Branch Metric Based Detection

A detection scheme built on a combination of a simplified branch metric based search and refined calculation is proposed in this work to reduce the computational complexity of the quasi-maximum-likelihood (QML) detectors, such as the QR-decomposition combined with M-algorithm (QRD-M), list sequential sphere decoder (LISS) and list sphere decoding (LSD), for multiple-input multiple-output (MIMO) multiplexing systems, while maintaining similar performance to the original methods. More specifically, the proposed method utilizes a multiplication-free accumulated branch metric to first identify the potential candidates with significantly reduced search complexity. The optimal solution is then determined by the standard squared Euclidean distance metric from the initial candidates. The key idea of the proposed method lies in the fact that the best candidate identified by the simplified branch metric coincides with that of which is based on the conventional one with a high probability for a properly designed parameter. Numerical results demonstrates that the QRD-M detector using the proposed method achieves significantly better performance than the conventional one with comparable (or even less) computational complexity.