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 The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI.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.

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 A base station for downlink or broadcast transmission requires downlink channel state information (CSI), which is outdated in time-varying environments and we encounter degraded performance due to interference.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.

Multi-user MIMO (Multiple Input Multiple Output) systems, in which multiple Mobile Stations (MSs) equipped with multiple antennas simultaneously communicate with a Base Station (BS) equipped with multiple antennas, at the same frequency, are attracting attention because of their potential for improved transmission performance in wireless communications. In the uplink of Space Division Multiplexing based multi-user MIMO (multi-user MIMO/SDM) systems that do not require full Channel State Information (CSI) at the transmitters, selecting active MS antennas, which corresponds to scheduling transmit antennas, is an effective technique. The Full search Selection Algorithm based on exhaustive search (FSA) has been studied as an optimal active MS antenna selection algorithm for multi-user MIMO systems. Unfortunately, FSA suffers from extreme computational complexity given large numbers of MSs. To solve this problem, this paper introduces the Gram-Schmidt orthogonalization based Selection Algorithm (GSSA) to uplink multi-user MIMO/SDM systems. GSSA is a suboptimal active MS antenna selection algorithm that offers lower computational complexity than the optimal algorithm. This paper evaluates the transmission performance improvement of GSSA in uplink multi-user MIMO/SDM systems under realistic propagation conditions such as spatially correlated BS antennas and clarifies the effectiveness of GSSA.