In time-varying fading environments, the performance of multiple-input multiple-output (MIMO) systems applying an eigenbeam-space division multiplexing (E-SDM) technique may be degraded due to a channel change during the time interval between the transmit weight matrix determination and the actual data transmission. To compensate for the channel change, we have proposed some channel prediction methods. Simulation results based on computer-generated channel data showed that better performance can be obtained when using the prediction methods in Rayleigh fading environments assuming the Jakes model with rich scatterers. However, actual MIMO systems may be used in line-of-sight (LOS) environments, and even in a non-LOS case, scatterers may not be uniformly distributed around a receiver and/or a transmitter. In addition, mutual coupling between antennas at both the transmitter and the receiver cannot be ignored as it affects the system performance in actual implementation. We conducted MIMO channel measurement campaigns at a 5.2 GHz frequency band to evaluate the channel prediction techniques. In this paper, we present the experiment and simulation results using the measured channel data. The results show that robust bit-error rate performance is obtained when using the channel prediction methods and that the methods can be used in both Rayleigh and Rician fading environments, and do not need to know the maximum Doppler frequency.

We evaluated the behavior of a multi-user multiple-input multiple-output (MIMO) system in time-varying channels using measured data. A base station for downlink or broadcast transmission requires downlink channel state information (CSI), which is outdated in time-varying environments and we encounter degraded performance due to interference. One of the countermeasures against time-variant environments is predicting channels with an autoregressive (AR) model-based method. We modified the AR prediction for a time division duplex system. We conducted measurement campaigns in indoor environments to verify the performance of the scheme of channel prediction in an actual environment and measured channel data. We obtained the bit-error rate (BER) using these data. The AR-model-based technique of prediction assuming the Jakes' model was found to reduce BER. Also, the optimum AR-model order was investigated by using the channel data we measured.

Multiple-input multiple-output (MIMO) systems using eigenbeam space division multiplexing (E-SDM) perform well and have increased capacities compared with those using conventional space division multiplexing (SDM). However, channel state information (CSI) is required at a transmitter, and the performance of E-SDM systems depends much on the accuracy of the CSI at a transmitter and a receiver. In time-varying fading environments, the channel change between the transmit weight determination time and the actual data transmission time causes the system performance to degrade. To compensate for the channel error, a linear extrapolation method has been proposed for a time division duplexing system. Unfortunately, the system performance still deteriorates as the maximum Doppler frequency increases. Here, two new techniques of channel extrapolation are proposed. One is second order extrapolation, and the other is exponential extrapolation. Also, we propose maximum Doppler frequency estimation methods for exponential extrapolation. Simulation results for 4tx 4rx MIMO systems showed that using the proposed techniques, E-SDM system performs better in a higher Doppler frequency region.

In this paper, the performance of multiuser MIMO E-SDM systems in downlink transmission is evaluated in both uncorrelated and correlated time-varying fading environments. In the ideal case, using the block diagonalization scheme, inter-user interference can be completely eliminated at each user; and using the E-SDM technique for each user, optimal resource allocation can be achieved, and spatially orthogonal substreams can be obtained. Therefore, a combination of the block diagonalization scheme and the E-SDM technique applied to multiuser MIMO systems gives very good results. In realistic environments, however, due to the dynamic nature of the channel and processing delay at both the transmitter and the receiver, the channel change during the delay may cause inter-user interference even if the BD scheme is used. In addition, the change may also result in large inter-substream interference and prevent optimal resource allocation from being achieved. As a result, system performance may be degraded seriously. To overcome the problem, we propose a method of channel extrapolation to compensate for the channel change. Applying our proposed method, simulation results show that much better system performance can be obtained than the conventional case. Moreover, it also shows that the system performance in the correlated fading environments is much dependent on the antenna configuration and the angle spread from the base station to scatterers.

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.