The goal of this special issue is to invite previously unpublished work in the broad areas of quantum error correction and fault tolerance with connections to classical and quantum information theory.
For future wireless communications, higher data rate, reliability, and traffic demands will lead to the development of novel communication frameworks that fully exploit the physics of electromagnetic waves. These emerging technologies include holographic MIMO, super-directive antenna array, extremely large antenna arrays, reconfigurable intelligent surfaces, orbital angular momentum (OAM) multiplexing, etc. To explore both potentials and limitations of these technologies, research into electromagnetic and information theory (EIT) is actively underway in both academia and industry. EIT is an interdisciplinary framework integrating electromagnetic wave (EM) theory and information theory (IT) for the analysis of physical systems for the communication, processing, and storage of information. It has been shown that physically large antenna arrays, large intelligent surfaces, RF lens antenna arrays, holographic MIMO, and/or continuous-aperture MIMO can be analyzed more effectively within an EIT framework. Furthermore, it is expected that the physical properties of the OAM, the non-diffraction properties of the Bessel beam, and/or the acceleration properties of the Airy beam will open new opportunities under the EIT framework.
This special issue of the 鶹ýAV Journal on Selected Areas in Information Theory is dedicated to the memory of Toby Berger, one of the most important information theorists of our time, who passed away in 2022 at the age of 81. He made foundational contributions to a wide range of areas in information theory, including rate-distortion theory, network information theory, quantum information theory, and bio-information theory. He also left a deep imprint on diverse fields in applied mathematics and theoretical engineering, such as Markov random fields, group testing, multiple access theory, and detection and estimation. Well known for his technical brilliance, he tackled many challenging problems, but above all, it is his pursuit of elegance in research and writing that shines throughout his work. The goal of this special issue is to celebrate Toby Berger’s lasting legacy and his impact on information theory and beyond. Original research papers on topics within the realm of his scientific investigations and their “offspring”, as well as expository articles that survey his pioneering contributions and their modern developments, are invited.
Over the past decade, machine learning (ML), that is the process of enabling computing systems to take data and churn out decisions, has been enabling tremendously exciting technologies. Such technologies can assist humans in making a variety of decisions by processing complex data to identify patterns, detect anomalies, and make inferences. At the same time, these automated decision-making systems raise questions about security and privacy of user data that drive ML, fairness of the decisions, and reliability of automated systems to make complex decisions that can affect humans in significant ways. In short, how can ML models be deployed in a responsible and trustworthy manner that ensures fair and reliable decision-making? This requires ensuring that the entire ML pipeline assures security, reliability, robustness, fairness, and privacy. Information theory can shed light on each of these challenges by providing a rigorous framework to not only quantify these desirata but also rigorously evaluate and provide assurances. From its beginnings, information theory has been devoted to a theoretical understanding of the limits of engineered systems. As such, it is a vital tool in guiding machine learning advances. We invite previously unpublished papers that contribute to the fundamentals, as well as the applications of information- and learning-theoretic methods for secure, robust, reliable, fair, private, and trustworthy machine learning. Exploration of such techniques to practical systems is also relevant.
To support the fast growth of IoT and cyber physical systems, as well as the advent of 6G, there is a need for communication and networking models that enable more efficient modes for machine-type communications. This calls for a departure from the assumptions of classical communication theoretic problem formulations as well as the traditional network layers. This new communication paradigm is referred to as goal or task oriented communication, or in a broader sense, is part of the emerging area of semantic communications.
Over the past decade, there have been a number of approaches towards novel performance metrics, starting from measures of timeliness such as the Age of Information (AoI), Query Age of Information (QAoI), to those that capture goal oriented nature, tracking or control performance such as Quality of Information (QoI), Value of Information (VoI) and Age of Incorrect Information (AoII), moving toward to more sophisticated end-to-end distortion metrics (e.g. MSE), ML performance, or human perception of the reproduced data, and the application of finite-blocklength information theory in the context of the remote monitoring of stochastic processes, and real-time control.
We invite original papers that contribute to the fundamentals, as well as the applications of semantic metrics, and protocols that use them, in IoT or automation scenarios.