An iterative soft decision based adaptive K-best decoder without SNR estimation

摘要

This paper presents an iterative soft decision based adaptive K-best multiple-input-multiple-output (MIMO) decoding algorithm. It has the flexibility of changing the list size, K with respect to the channel condition, although the accurate measurement of signal to noise ratio (SNR) is not required. Moreover, the concept of iterative soft decision based lattice reduction (LR)-aided minimum mean square error (MMSE) extended K-best decoder is applied instead of conventional hard decision based K-best algorithm to reduce computational complexity to a great extent It is found that the ratio of the minimum path metric to the second minimum can provide reliable estimation of channel condition. Hence, in the proposed algorithm, K is changed adaptively with respect to the ratio. Using this method with less number of K, we can obtain similar performance compared to the conventional LR-aided K-best algorithm operating with maximum list size of 64. Comparing to the fourth iteration of iterative soft decision based least sphere decoding (LSD), the proposed method with less K achieves 1.6 dB improvement at the bit error rate (BER) of 10. Therefore, similar performance can be obtained by the proposed adaptive K-best algorithm with less computational complexity of the tree search decoder.