variable-breadth k-best detector for mimo systems
Tree search detection algorithms can provide Maximum-Likelihood detection over Gaussian MIMO channels with lower complexity than the exhaustive search. Furthermore, the performance of MIMO detectors is highly influenced by the channel matrix condition number. In this paper, the impact of the 2-norm condition number in data detection is exploited in order to decrease the complexity of already proposed algorithms. A suboptimal tree search method called K-Best is combined with a channel matrix condition number estimator and a threshold selection method. This approach leads to a variable-breadth K-Best detector with predictable average performance and suitable for hardware implementation. The results show that the proposed scheme has lower complexity, i.e. it is less power consuming, than a fixed K-Best detector of similar performance.
k-best condition number mimo detection