Reduced K-best sphere decoding algorithm based on minimum route distance and noise variance

This paper focuses on reducing the complexity of K-best sphere decoding (SD) algorithm for the detection of uncoded multiple input multiple output (MIMO) systems. The proposed algorithm utilizes the threshold-pruning method to cut nodes with partial Euclidean distances (PEDs) larger than the threshold. Both the known noise value and the unknown noise value are considered to generate the threshold, which is the sum of the two values. The known noise value is the smallest PED of signals in the detected layers. The unknown noise value is generated by the noise power, the quality of service (QoS) and the signal-to-noise ratio (SNR) bound. Simulation results show that by considering both two noise values, the proposed algorithm makes an efficient reduction while the performance drops little.