Deep Learning for Large-Scale MIMO: An Intelligent Wireless Communications Approach

Recent years have seen machine learning makes growing presence in several areas inwireless communications, and specifically in large-scale Multiple-Input Multiple-Output (MIMO) systems. This comes as a result of its ability to offer innovative solutions to some of the most daunting problems that haunt current and future large-scale MIMO systems, such as downlink channel-training and sensitivity to line-of-sight (LOS) blockages to name two examples. Machine learning, in general, provides wireless systems with data-driven capabilities, with which they could realize much needed agility for decision-making and adaptability to their surroundings. Bearing the potential of machine learning in mind, this dissertation takes a close look at what deep learning can bring to the table of large-scale MIMO systems. It proposes three novel frameworks based on deep learning that tackle challenges rooted in the need to acquire channel state information. Framework 1, namely deterministic channel prediction, recognizes that some channels are easier to acquire than others (e.g., uplink are easier to acquire than downlink), and, as such, it learns a function that predicts some channels (target channels) from others (observed channels). Framework 2, namely statistical channel prediction, aims to do the same thing as Framework 1, but it takes a more statistical approach; it learns a large-scale statistic for target channels (i.e., per-user channel covariance) from observed channels. Differently from frameworks 1 and 2, framework 3, namely vision-aided wireless communications, presents an unorthodox perspective on dealing with large-scale MIMO challenges specific to high-frequency communications. It relies on the fact that high-frequency communications are reliant on LOS much like computer vision. Therefore, it recognizes that parallel and utilizes multimodal deep learning to address LOS-related challenges, such as downlink beam training and LOSlink blockages. All three frameworks are studied and discussed using datasets representing various large-scale MIMO settings. Overall, they show promising results that cement the value of machine learning, especially deep learning, to large-scale MIMO systems.