Fractional K-best sphere decoding algorithm over rayleigh fading MIMO channels

K-best sphere decoding algorithm (KBA) is used to approach near-maximum-likelihood (ML) performance for multiple-input-multiple-output (MIMO) detection with lower complexity than maximum-likelihood (ML) method. In KBA, the value of survivor paths K, can be fixed values only in all tree levels. These fixed values of K's give a certain performances at a certain complexities. In this paper, a new fractional K-best algorithm (FKBA) is proposed which gives a performance and complexity between these discreet performances and complexities for ordinary KBA, acts as if the values of K's are fractions (not integers). This can be achieved by increasing the number of survivor paths into K+Δ in some tree levels and stays K paths in other tree levels. The value of Δ in a specific tree level is resulted from the number of branches have distance metrics lower than or equal the value of average distance metric for all branches in the same tree level. The simulation results show that the performance and complexity of FKBA are approximately in the middle of performances and complexities of two successive values of K (K and K + 1) for different MIMO models of 16 - QAM over Rayleigh fading MIMO Channels for all values of SNR.