Partial Noise Value Aided Reduced K-Best Sphere Decoding

This article focuses on reducing the complexity of K-best sphere decoding (K-best SD) algorithm for the detection of multiple-input multiple-output (MIMO) systems. One common reduction method is that one or more selected thresholds are set to cut excess nodes with partial Euclidean Distance (PED) larger than them. For a long time, statistical characteristic of noise has been well explored to generate thresholds. But the known noise in a certain specific transmission process is always overlooked. In this article, not only the statistical characteristic of noise is calculated, but also the known value of noise is considered. By adding a parameter determined by both noise and quality of service (QoS) to the smallest PED in each searching layer, a tighter and more suitable threshold can be calculated for this layer. Simulation results show that the proposed algorithm makes an efficient complexity reduction while the performance drops little. Specially, the proposed algorithm reduces the computational complexity about 90% while the bit error ratio (BER) performance drops around 10% in 4-by-4 MIMO systems employing 16-QAM or 64-QAM modulation. A new parameter, half complexity point, is proposed to evaluate the reduction effect, and half complexity points of the proposed algorithm are better than one selected original algorithm.