Deep Adaptive Learning-Based Beam Combining Framework for 5G Millimeter-Wave Massive 3D-MIMO Uplink Systems

Abstract

In today's wireless communication systems, the integration of 5G millimeter-wave (mmWave) Massive Multiple Input–Multiple Output (M-MIMO) technology offers significant advancements and capabilities that address the growing demand for higher data rates, increased capacity, and improved user experiences. In three-dimensional (3D) environments, the design of beamforming for uplink multi-user M-MIMO relies on accurate uplink channel state information (CSI) at the transmitter/receiver. In fact, it is difficult for the Base Station (BS) and the User Equipment (UE) to obtain beam patterns due to computational complexity, multiple 3D beams, and regulating the weight of antenna elements, which leads to significantly low sum-rate. Hence, a robust, deep adaptive learning framework in the case of 3D beamforming is needed. This paper proposes a deep adaptive learning-based beam combining framework using User-Wise Attention-Assisted Deep Adaptive Neural Network (UWA-DANN) for mmWave-3DM-MIMO systems. In this, a UWA mechanism learns a set of beam features and corresponding weights for each user. This mechanism allows the system to focus on different users independently and adapt the beamforming process accordingly. Also, it allows the network to dynamically focus on relevant information from the input channels and user constraints. To this end, a dynamic beam pattern is adapted using the DANN model to learn user positions, channel measurements, and beamforming weights. This approach learns to map input parameters such as user positions, channel measurements, and corresponding beam patterns to extract relevant features for beam pattern adaptation. Thus, the UWA-DANN approach provides higher data rates, low complexity, and improved link stability for users. Experimental results show that the proposed UWA-DANN model obtains robust performance over existing schemes in terms of achievable rate and sum-rate under field trial sites in urban scenarios.