The current era of artificial intelligence (AI) is characterized by the scaling of data and computational resources as the primary driver of emergent capabilities in AI models. However, this trend faces fundamental constraints on data availability and computing power. Recent breakthroughs suggest an alternative path forward—leveraging novel statistical and information-theoretic tools to enhance reliability and data efficiency, while enhancing intelligence per joule and per data point/token via hardware-software co-design principles. Understanding the fundamental limits of AI through the lens of information and physical principles, such as Landauer's principle, is crucial for developing sustainable and efficient learning systems. This special issue aims to advance theoretical and algorithmically motivated approaches to optimizing AI performance while reducing reliance on extensive data and energy resources.
Information theory has been a mainstay of wireless communications ever since these became digital in nature, directly or indirectly influencing most of the constituent technologies. Not only has information theory provided fundamental bounds that enable gauging the performance of specific techniques, but it has yielded insights on transmitter/receiver structures and the air interface at large, revealed essential tradeoffs, and delineated regimes where operating conditions and channel mechanisms are fundamentally different. This special issue aims to investigate the role of information theory moving forward, in the age of satellites, drones, self-driving vehicles, robots, and AI.
This Special Issue targets original work pushing the boundary of DNA-based data storage and emphasizes the consideration of data privacy and security therein. Researchers from all the scientific communities investigating the topic are encouraged to submit their work.
We will follow a continuous publication model.
The goal of this special issue is to invite previously unpublished work in the broad areas of quantum error correction and fault tolerance with connections to classical and quantum information theory.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.