51ÂÜÀò

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 paradigm is referred to as goal or task-oriented communication, and is relevant also in part of the emerging area of semantic communications.

Sensing has emerged as a key feature of modern wireless communication systems and networks. Traditionally, advances in the design, optimization and deployment of sensing and communication systems have evolved somewhat independently of each other.

This special issue of the 51ÂÜÀò Journal on Selected Areas in Information Theory is dedicated to the memory of Alexander Vardy, a pioneer in the theory and practice of channel coding. His ground-breaking contributions ranged from unexpected solutions of long-standing theoretical conjectures to ingenious decoding algorithms that broke seemingly insurmountable barriers to code performance.

In the problem of quickest change detection, a change occurs at some unknown time in the distribution of a sequence of random vectors that are monitored in real time, and the goal is to detect this change as quickly as possible subject to a certain false alarm constraint. In this work we consider this problem in the presence of parametric uncertainty in the post-change regime and controlled sensing.

We study the distributed mean estimation (DME) problem under privacy and communication constraints in the local differential privacy (LDP) and multi-message shuffled (MMS) privacy frameworks. The DME has wide applications in both federated learning and analytics. We propose a communication-efficient and differentially private algorithm for DME of bounded $\ell _{2}$ -norm and $\ell _{\infty }$ -norm vectors. We analyze our proposed DME schemes showing that our algorithms have order-optimal privacy-communication-performance trade-offs.

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made.

In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirements. However, predictions are inherently uncertain, and managing prediction uncertainty presents significant challenges in environments characterized by complex dynamics and forking trajectories. In this work, we assume access to a pre-designed probabilistic implicit or explicit sequence model, which may have been obtained using model-based or model-free methods.