Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Graph learning is significant due to its ability to model complex, non-Euclidean relationships that traditional machine learning struggles to capture, thus better supporting real-world applications ranging from drug discovery and fraud detection to recommender systems and scientific reasoning. However, challenges like scalability, generalization, heterogeneity, interpretability, and trustworthiness must be addressed to unlock its full potential. This survey provides a comprehensive introduction to graph learning, focusing on key dimensions including scalable, temporal, multimodal, generative, explainable, and responsible graph learning. We review state-of-the-art techniques for efficiently handling large-scale graphs, capturing dynamic temporal dependencies, integrating heterogeneous data modalities, generating novel graph samples, and enhancing interpretability to foster trust and transparency. We also explore ethical considerations, such as privacy and fairness, to ensure responsible deployment of graph learning models. Additionally, we identify and discuss emerging topics, highlighting recent integration of graph learning and other AI paradigms and offering insights into future directions. This survey serves as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of graph learning.

In the current noisy intermediate-scale quantum (NISQ)era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parameterized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parameterized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a back groundin probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then,it covers parameterized quantum circuits, the variation alquantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.

As data generation increasingly takes place on devices without a wired connection, Machine Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. In this monograph, we give a comprehensive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives an introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.

Imagine a coverage area where each mobile device is communicating with a preferred set of wireless access points (among many) that are selected based on its needs and cooperate to jointly serve it, instead of creating autonomous cells. This effectively leads to a user-centric post-cellular network architecture, which can resolve many of the interference issues and service-quality variations that appear in cellular networks. This concept is called User-centric Cellfree Massive MIMO (multiple-input multiple-output) and has its roots in the intersection between three technology components: Massive MIMO, coordinated multipoint processing, and ultra-dense networks. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to enable massively large networks with many mobile devices. This monograph covers the foundations of User-centric Cell-free Massive MIMO, starting from the motivation and mathematical definition. It continues by describing the state-of-the-art signal processing algorithms for channel estimation, uplink data reception, and downlink data transmission with either centralized or distributed implementation. The achievable spectral efficiency is mathematically derived and evaluated numerically using a running example that exposes the impact of various system parameters and algorithmic choices. The fundamental tradeoffs between communication performance, computational complexity, and fronthaul signaling requirements are thoroughly analyzed. Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster formation, and power optimization are provided, while open problems related to these and other resource allocation problems are reviewed. All the numerical examples can be reproduced using the accompanying Matlab code.

Clock synchronization is a mechanism for providing a standard time to various devices across a network. This monograph provides a comprehensive overview of recent developments for clock synchronization protocols built on two-way message exchanges. Several clock synchronization protocols are available in the literature for distributing time from high-cost, high-stability clocks (termed masters) to low-cost, low-stability clocks (termed slaves) via an interconnecting network. A number of clock synchronization protocols are built on two-way message exchanges. These include the timing protocol for sensor networks (TPSN), lightweight time synchronization (LTS) protocol, tiny-sync and mini-sync, network time protocol (NTP) and the IEEE 1588 precision time protocol (PTP). The messages traveling between the master and slave nodes can encounter several intermediate switches and routers, accumulating delays at each node. The main factors contributing to the overall delay are the fixed propagation and processing delays at the intermediate nodes along the network path between the master and slave, as well as the stochastic queuing delays at each such node. Popular probability density function (pdf) models for modeling the stochastic delays include Gaussian, exponential, gamma, Weibull, and log-normal. Although these pdf models for the stochastic queuing delays apply to several scenarios, they might not be suitable in specific scenarios such as cellular base station synchronization using mobile backhaul networks and IEEE 1588 in 4G Long Term Evolution (LTE) networks. Further, there could be possibly unknown asymmetries between the fixed path delays in the forward master-to-slave path and the reverse slave-to-master path. These unknown asymmetries could arise from various sources, including delay attacks or incorrect modeling. In this monograph, we present recent developments for clock synchronization protocols built on the two-way message exchange. After an introduction to the basic concepts and mathematical models, the optimum estimators are presented for estimating the clock skew and offset that are applicable for any pdf model of the stochastic delays. Robust algorithms that can also handle unknown path asymmetries are presented next. The focus is on techniques that consider practical, relevant measurement models in order to guide the reader from physical observations to the actual synchronization of the clocks at the slave and master.

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most imaging systems necessarily use an image reconstruction algorithm to create an image; the design of these algorithms has been a topic of research since at least the 1960’s. In the last few years, machine learning-based approaches have shown impressive performance on image reconstruction problems, triggering a wave of enthusiasm and creativity around the paradigm of learning. Our goal is to unify this body of research, identifying common principles and reusable building blocks across decades and among diverse imaging modalities. We first describe system modeling, emphasizing how a few building blocks can be used to describe a broad range of imaging modalities. We then discuss reconstruction algorithms, grouping them into three broad generations. The first are the classical direct methods, including Tikhonov regularization; the second are the variational methods based on sparsity and the theory of compressive sensing; and the third are the learning-based (also called data-driven) methods, especially those using deep convolutional neural networks. There are strong links between these generations: classical (first-generation) methods appear as modules inside the latter two, and the former two are used to inspire new designs for learning-based (third-generation) methods. As a result, a solid understanding of all three generations is necessary for the design of state-of-the-art algorithms.

Pattern Matching in Compressed Texts and Images surveys and appraises techniques for pattern matching in compressed text and images. Normally compressed data needs to be decompressed before it is processed. If however the compression has been done in the right way, it is often possible to search the data without having to decompress it, or, at least, only partially decompress it. The problem can be divided into lossless and lossy compression methods, and then in each of these cases the pattern matching can be either exact or inexact. Much work has been reported in the literature on techniques for all of these cases. It includes algorithms that are suitable for pattern matching for various compression methods, and compression methods designed specifically for pattern matching. This monograph provides a survey of this work while also identifying the important relationship between pattern matching and compression, and proposing some performance measures for compressed pattern matching algorithms. Pattern Matching in Compressed Texts and Images is an excellent reference text for anyone who has an interest in the problem of searching compressed text and images. It concludes with a particularly insightful section on the ideas and research directions that are likely to occupy researchers in this field in the short and long term.

This survey gives an introduction to redundant signal representations called frames. These representations have recently emerged as yet another powerful tool in the signal processing toolbox and have become popular through use in numerous applications. Our aim is to familiarize a general audience with the area, while at the same time giving a snapshot of the current state-of-the-art.