The recent publications in the field of communication and information theory reveal a strong focus on enhancing the robustness and efficiency of data transmission and state estimation under challenging conditions. A significant trend is the exploration of joint message and state transmission strategies that aim to optimize the trade-off between communication rate and state estimation accuracy, even in the presence of adversarial jamming or unreliable channels. Innovative coding schemes and decoding approaches are being developed to achieve optimal performance bounds, with particular attention to scenarios involving partial state information and feedback. Additionally, there is a growing interest in understanding and mitigating the effects of information degradation and misinformation spread in networked systems, highlighting the importance of timely and accurate information dissemination. The integration of model predictive control and reinforcement learning techniques into remote estimation problems further underscores the field's move towards more adaptive and intelligent communication systems.

Noteworthy Papers

• Robust Joint Message and State Transmission under Arbitrarily Varying Jamming: Introduces an optimal inner bound for the robust capacity-distortion region, addressing worst-case scenarios in communication and state estimation.
• A Simultaneous Decoding Approach to Joint State and Message Communications: Proposes a novel coding scheme that achieves optimal capacity-distortion functions without the need for Wyner-Ziv random binning, enhancing performance in various channel models.
• Information Degradation and Misinformation in Gossip Networks: Demonstrates the relationship between information age and misinformation spread, offering insights into network design for minimizing degradation.
• Which Sensor to Observe? Timely Tracking of a Joint Markov Source with Model Predictive Control: Presents innovative MPC methods for minimizing the mean age of incorrect information in remote estimation tasks, showcasing the potential of adaptive control in communication systems.
• Remote State Estimation over Unreliable Channels with Unreliable Feedback: Fundamental Limits: Characterizes optimal coding policies for networked estimation, providing a foundational understanding of performance limits in unreliable communication environments.