Alamouti-Like Transmission Schemes in Distributed MIMO Networks

The purpose of the study is to investigate potential benefits of using Alamouti-like orthogonal space-time-frequency block codes (STFBC) in distributed multiple-input multiple-output (D-MIMO) systems to increase the diversity at the UE side when instantaneous channel state information (CSI) is not available at radio units (RUs). Most of the existing transmission techniques require instantaneous CSI to form precoders which can only be realized together with accurate and up-to-date channel knowledge. STFBC can increase the diversity at UE side without estimating the downlink channel. Under challenging channel conditions, the network can switch to a robust mode where a certain data rate is maintained for users even without knowing the channel coefficients by means of STFBC. In this study, it will be mainly focused on clustering of RUs and user equipment, where each cluster adopts a possibly different orthogonal code, so that overall spectral efficiency is optimized. Potential performance gains over known techniques that can be used when the channel is not known will be shown and performance gaps to sophisticated precoders making use of channel estimates will be identified.

Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility

Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher layer channel measurement reports in order to perform UE classification, thereby reducing the complexity of the classification process.