An Adaptive K-best Algorithm without SNR Estimation for MIMO Systems

This paper proposes a new adaptive K-best algorithm for MIMO systems. The proposed scheme controls the number of survivor paths, K based on the degree of the reliability of zero-forcing (ZF) estimates at each K-best step. The critical drawback of the fixed K-best detection is that the correct path´s metric may be temporarily larger than K minimum paths metrics due to imperfect interference cancellation by the incorrect ZF estimates. So, the conventional variable K-best schemes control K according to measured SNR value. However, these schemes still have the problem that needs to accurately and dynamically measure SNR for optimal setting of K. In the proposed variable scheme, we accomplish adaptation of K without necessity of SNR measurement. It is found that the ratio of the minimum path metric to the second minimum is a good reliability indicator for the channel condition. By adaptively changing K based on this ratio, the proposed scheme effectively achieves the performance of large K-best system while maintaining the overall average computation complexity much smaller than that of large K-best system without the necessity of SNR value estimation.