Unified performance theory for ISAC (information and estimation)
Establish a universal theoretical framework to jointly evaluate sensing and communication performance in integrated sensing and communication (ISAC) systems by rigorously integrating results from information theory (e.g., mutual information, capacity) and estimation theory (e.g., Fisher information, Cramér–Rao bounds), so that dual-function tradeoffs and resource allocations can be consistently analyzed within a single theory.
References
Note that it remains an open challenge to establish a universal theoretical framework to jointly evaluate the performance of dual-functions in ISAC systems, which can integrate the classical results derived from information theory and estimation theory, respectively.
Developing scalable cross-layer optimization frameworks that jointly integrate correlation modelling, performance characterization, UE grouping, resource allocation, and sensing fusion remains an important open research problem for future multi-UE sensing networks.