A complexity-constrained particle filtering algorithm for MAP equalization of frequency-selective MIMO channels

Sequential Monte Carlo (SMC) schemes have recently been proposed in order to perform optimal equalization of multiple input multiple output (MIMO) wireless channels. Unfortunately, for each simulated data sample, the complexity of existing algorithms grows exponentially with the number of input data streams. We propose a novel SMC MIMO channel equalizer that avoids this limitation. An adequate design of the data sampling scheme leads to a reduction of the computational load per sample, which becomes linear in the number of channel inputs. Computer simulations that illustrate the nearly optimal bit error rate of the proposed SMC equalizer are presented.