MmWave Amplify-and-Forward MIMO Relay Networks with Hybrid Precoding/Combining Design

In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature. In addition, we also propose a robust joint transceiver design for imperfect channel state information

Deep learning enabled beam tracking for non-line of sight millimeter wave communications

To solve the complex beam alignment issue in non-line-of-sight (NLOS) millimeter wave communications, this paper presents a deep neural network (DNN) based procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in terms of azimuth and elevation, i.e., AAOA/AAOD and EAOA/EAOD. In order to evaluate the performance of the proposed procedure under practical assumptions, we employ a trajectory prediction method by considering dynamic window approach (DWA) to estimate the location information of the user equipment (UE), which is utilized as the input parameter of the trained DNN to generate the prediction of AAOA/AAOD and EAOA/EAOD. The robustness of the prediction procedure is analyzed in the presence of prediction errors, which proves that the proposed DNN is a promising tool to predict AOA and AOD in NLOS scenarios based on the estimated UE location. Simulation results shows that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2∘