Frequency-selective fading due to multipath propagation is serious hindrance in high-speed TDMA mobile communications. An adaptive antenna has been proposed to reduce the frequency-selective fading and realize path-diversity. This paper presents a criterion which selects multipath signals and weighting factors for combining them. First, we describe a selection criterion which chooses the multipath signals for the path-diversity. We propose a ratio of signal power to error power for the criterion. Furthermore, we propose weighting factors which realize approximately the maximal ratio combining. Computer simulation results show that the proposed selection criterion and weighting factors reveal excellent performance.

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.

Direction of arrival (DOA) estimation of wireless signals has a long history but is still being investigated to improve the estimation accuracy. Non-linear algorithms such as compressed sensing are now applied to DOA estimation and achieve very high performance. If the large computational loads of compressed sensing algorithms are acceptable, it may be possible to apply a deep neural network (DNN) to DOA estimation. In this paper, we verify on-grid DOA estimation capability of the DNN under a simple estimation situation and discuss the effect of training data on DNN design. Simulations show that SNR of the training data strongly affects the performance and that the random SNR data is suitable for configuring the general-purpose DNN. The obtained DNN provides reasonably high performance, and it is shown that the DNN trained using the training data restricted to close DOA situations provides very high performance for the close DOA cases.