Saturday 01 March 2025
The marriage of sensing and communication technologies has long been touted as a key enabler for future wireless systems, but researchers have struggled to find an optimal balance between data transmission rates and estimation performance. A new study sheds light on this conundrum by characterizing the fundamental tradeoff between information rate and mean-squared error (MSE) in integrated sensing and communication (ISAC) systems.
In traditional communication systems, the focus is typically on maximizing data transmission rates while minimizing errors. However, ISAC systems require a different approach, as they need to simultaneously transmit data and estimate environmental parameters, such as distance or velocity. This added complexity creates a tradeoff between the two goals: increasing the information rate can improve data transmission, but it may compromise estimation performance.
The researchers used an information-theoretic framework to analyze this tradeoff, developing a novel characterization of the optimal relationship between information rate and MSE. Their approach is based on the concept of asymptotically tight Bayesian Cramér-Rao bounds (ATBCRB), which provide a lower bound on the MSE for any estimator.
The study found that the ATBCRB can be used to derive a fundamental tradeoff curve, which represents the optimal balance between information rate and MSE. This curve is not only theoretically interesting but also has practical implications for ISAC system design. For instance, it suggests that increasing the information rate beyond a certain point may not necessarily improve estimation performance, as the added noise from transmission can overwhelm the benefits of increased data rates.
The researchers also demonstrated their theory using a numerical example involving spectrum sensing and communication. In this scenario, they found that the ATBCRB-based approach outperformed traditional methods that rely on the Bayesian Cramér-Rao bound (BCRB), which is commonly used in ISAC system design.
This work has important implications for the development of future wireless systems, particularly those that require simultaneous sensing and communication capabilities. By better understanding the fundamental tradeoff between information rate and MSE, engineers can design more effective ISAC systems that balance these competing goals. The study’s findings also highlight the importance of considering both data transmission rates and estimation performance when designing such systems.
The researchers’ approach offers a new perspective on the challenges faced by ISAC systems, and their results provide valuable insights for engineers working in this field. As wireless technologies continue to evolve, this work will play an important role in shaping the development of future sensing and communication systems.
Cite this article: “Balancing Act: Understanding the Tradeoff Between Data Transmission Rates and Estimation Performance in Integrated Sensing and Communication Systems”, The Science Archive, 2025.
Integrated Sensing And Communication, Information Rate, Mean-Squared Error, Wireless Systems, Estimation Performance, Data Transmission Rates, Cramér-Rao Bounds, Bayesian Estimation, Spectrum Sensing, Communication Systems.
Reference: Daewon Seo, “Integrated Communication and Bayesian Estimation of Fixed Channel States” (2025).
