Performance of the EP-MBCJR algorithm in time dispersive MIMO office environments

This work investigates the performance of a new reduced-complexity trellis decoding algorithm (termed the EP-MBCJR algorithm) when employed for the task of equalization in an indoor multiple input multiple output wireless environment. The algorithm is a generic approximate reduced-state variation of the BCJR algorithm modeled after the conventional M-BCJR algorithm. Instead of choosing the active states based on the filtered distribution of states in the forward recursion, the EP-MBCJR algorithm selects the active states based on "beliefs" on the states. This can be seen as an application of the concept of "expectation propagation" and leads to identical forward and backward recursions which can be iterated to improve system performance. A receiver architecture comprising of channel estimation, sampling phase selection and turbo equalization is proposed and its performance evaluated through computer simulations. For the simulation of channels closely resembling the physical environment, we have used channels generated in accordance with the IEEE 802.11n TGn channel models