Data-driven Channel Learning for Next-generation Communication Systems

University of Minnesota Ph.D. dissertation. October 2019. Major: Electrical/Computer Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); x, 116 pages.The turn of the decade has trademarked the `global society' as an information society, where the creation, distribution, integration, and manipulation of information have significant political, economic, technological, academic, and cultural implications. Its main drivers are digital information and communication technologies, which have resulted in a "data deluge", as the number of smart and Internet-capable devices increases rapidly. Unfortunately, establishing information infrastructure to collect data becomes more challenging particularly as communication networks for those devices become larger, denser, and more heterogeneous to meet the quality-of-service (QoS) for the users. Furthermore, scarcity in spectral resources due to an increased demand for mobile devices urges the development of a new methodology for wireless communications possibly facing unprecedented constraints both on hardware and software. At the same time, recent advances in machine learning tools enable statistical inference with efficiency as well as scalability in par with the volume and dimensionality of the data. These considerations justify the pressing need for machine learning tools that are amenable to new hardware and software constraints, and can scale with the size of networks, to facilitate the advanced operation of next-generation communication systems. The present thesis is centered on analytical and algorithmic foundations enabling statistical inference of critical information under practical hardware/software constraints to design and operate wireless communication networks. The vision is to establish a unified and comprehensive framework based on state-of-the-art data-driven learning and Bayesian inference tools to learn the channel-state information that is accurate yet efficient and non-demanding in terms of resources. The central goal is to theoretically, algorithmically, and experimentally demonstrate how valuable insights from data-driven learning can lead to solutions that markedly advance the state-of-the-art performance on inference of channel-state information. To this end, the present thesis investigates two main research thrusts: i) channel-gain cartography leveraging low-rank and sparsity; and ii) Bayesian approaches to channel-gain cartography for spatially heterogeneous environment. The aforementioned research thrusts introduce novel algorithms that aim to tackle the issues of next-generation communication networks. Potential of the proposed algorithms is showcased by rigorous theoretical results and extensive numerical tests

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals

We propose a new form of plausible counterfactual explanation designed to explain the behaviour of computer vision systems used in urban analytics that make predictions based on properties across the entire image, rather than specific regions of it. We illustrate the merits of our approach by explaining computer vision models used to analyse street imagery, which are now widely used in GeoAI and urban analytics. Such explanations are important in urban analytics as researchers and practioners are increasingly reliant on it for decision making. Finally, we perform a user study that demonstrate our approach can be used by non-expert users, who might not be machine learning experts, to be more confident and to better understand the behaviour of image-based classifiers/regressors for street view analysis. Furthermore, the method can potentially be used as an engagement tool to visualise how public spaces can plausibly look like. The limited realism of the counterfactuals is a concern which we hope to improve in the future

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Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

Applications in Monocular Computer Vision using Geometry and Learning : Map Merging, 3D Reconstruction and Detection of Geometric Primitives

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