This paper investigates the improved decoder performance offered by incorporating dynamic linear modelling techniques when applied to particle filters for use in tracking the MIMO wireless channel. Conventional Bayesian-based receivers that perform channel tracking necessarily require a wireless channel model, typified by the use of a low order auto-regressive (AR) model. Normally, the model parameters are static in nature and are estimated a priori of any transmission; thus if the channel conditions change, a model mismatch occurs and system performance is degraded. Our method allows for time-varying channel statistics by modelling the channel fading rate as a Markov random walk. This new procedure allows the channel model to assume a time-varying behavior. As shown through simulations, the incorporation of dynamic modelling of time-dispersive channels not only offers superior performance, but at high SNR eliminates the error-rate floor commonly seen in systems using the static AR models

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.

The implementation of current space-time codes is often performed under the assumption that the additive channel noise is white and Gaussian, and that the receiver has precise knowledge of the realization of the fading process. Here we study the application of particle filters to MIMO systems in order to reduce the uncertainty in the estimation of the channel fading coefficients. Using known orthogonal training sequences for channel estimation, an analysis on the estimated fading gains reveals that they are stochastic in nature with mean equal to the true channel gain, and variance proportional to the inverse of the transmit power. Furthermore, this estimation uncertainty is shown to incur a penalty in the signal-to-noise ratio, thus reducing overall system efficiency. In order to mitigate the effects of estimation error suffered by current MIMO systems, we use particle filters for channel tracking. Modeling the wireless fading channel as an AR process, the particle filter is shown to be superior to conventional estimation techniques by providing a significant decrease in the mean-squared error (MSE) of the channel estimate. Simulations illustrate the robust nature of this new scheme.

Narrowband multiple-input-multiple-output (MIMO) measurements using 16 transmitters and 16 receivers at 2.11 GHz were carried out in Manhattan. High capacities were found for full, as well as smaller array configurations, all within 80% of the fully scattering channel capacity. Correlation model parameters are derived from data. Spatial MIMO channel capacity statistics are found to be well represented by the separate transmitter and receiver correlation matrices, with a median relative error in capacity of 3%, in contrast with the 18% median relative error observed by assuming the antennas to be uncorrelated. A reduced parameter model, consisting of 4 parameters, has been developed to statistically represent the channel correlation matrices. These correlation matrices are, in turn, used to generate H matrices with capacities that are consistent within a few percent of those measured in New York. The spatial channel model reported allows simulations of H matrices for arbitrary antenna configurations. These channel matrices may be used to test receiver algorithms in system performance studies. These results may also be used for antenna array design, as the decay of mobile antenna correlation with antenna separation has been reported here. An important finding for the base transmitter array was that the antennas were largely uncorrelated even at antenna separations as small as two wavelengths.

Narrowband MIMO measurements using 16 transmitters and 16 receivers at 2.11 GHz were carried out in Manhattan. High capacities were found for full as well as smaller array configurations, all within 80% of the fully scattering channel capacity. It was found that two streets which might be considered near LOS, had different capacities, one about 45% of and the other about 75% of the fully scattering channel capacity.