Bayesian sequential state estimation for MIMO wireless communications

This paper explores the use of particle filters, rooted in Bayesian estimation, as a device for tracking statistical variations in the channel matrix of a narrowband multiple-input, multiple-output (MIMO) wireless channel. The motivation is to permit the receiver to acquire channel state information through a semiblind strategy and thereby improve the receiver performance of the wireless communication system. To that end, the paper compares the particle filter as well as an improved version of the particle filter using gradient information, to the conventional Kalman filter and mixture Kalman filter with two metrics in mind: receiver performance curves and computational complexity. The comparisons, also including differential phase modulation, are carried out using real-life recorded MIMO wireless data.