Practical equalization of multiple input multiple output (MIMO) channels poses several difficulties. Namely, it is well known that the complexity of maximum a posteriori (MAP) data detection grows exponentially with the number of inputs and the channel order, i.e., the length of the channel impulse response (CIR). Moreover, knowledge of the latter parameter is needed for reliable data detection, but its estimation is often a hard task and very few papers have tackled the problem. In this article, we propose the use of the sequential Monte Carlo (SMC) methodology to build quasi-MAP MIMO equalizers with polynomial complexity, that admit a parallel implementation and can handle the uncertainty in the channel order. In particular, we derive both optimal and complexity-constrained SMC algorithms for joint data detection, channel order and CIR estimation in frequency and time-selective MIMO channels. Computer simulation results are presented to illustrate the performance of the proposed techniques.
展开▼
