A Monte Carlo MIMO detection scheme via random noise generation

In this paper, a MIMO detection scheme is proposed based on a combination of Monte Carlo technique and list detection. Specifically, a list of Gaussian samples are first generated to determine the search range of constellation points in which the transmitted symbol is most likely to locate. Linear equalizations are then applied to equalize the effect caused by the channel mixing, and a list detector is used to search within the determined search range. By varying the parameters in the Monte Carlo method, different symbol error rate (SER) versus complexity tradeoff can be obtained to account for different system design requirements. Simulation results also show that near-ML SER performance with considerably less computational complexity can be achieved by the proposed scheme compared to the exhaustive search.