Probabilistic tree pruning for fixed-complexity sphere decoder in MIMO systems

The fixed-complexity sphere decoder (FSD) has been proposed to attain the near-optimal performance achieving the same diversity order as the maximum-likelihood decoder (MLD). However, it suffers great redundant computations resulting in very high power consumption. In this paper, we combine the probabilistic tree pruning (PTP) method proposed recently by Shim and Kang with the original FSD. This algorithm (dubbed as PTP-FSD) preserves the advantages of sphere decoder (SD) such as branch pruning using an adaptively updated PTP threshold. We compare the PTP-FSD with the original FSD and a lately developed statistical threshold (ST) based FSD (ST-FSD). Simulation results demonstrate that the PTP-FSD attains the near-optimal performance with a much lower cost than that of the FSD, and is much more efficient than the ST-FSD in practice.