Sensory Data Sharing in ISAC System: Fundamental Limits and Waveform Design

The simultaneous acquisition and sharing of sensory data through a dual-functional signaling strategy termed the communication-assisted sensing (CAS) system in this paper, has the potential to provide users with beyond-line-of-sight sensing capabilities. We mainly focus on three primary aspects, namely, the information-theoretic framework, the optimal distribution of channel input, and the optimal waveform design for Gaussian signals. First, we establish the information-theoretic framework and develop a modified source-channel separation theorem (MSST) tailored for CAS systems. The proposed MSST elucidates the relationship between achievable distortion, coding rate, and communication channel capacity in cases where the distortion metric is separable for sensing and communication (S&C) processes. Second, we present an optimal channel input design for dual-functional signaling, which aims to minimize CAS distortion under the constraints of the MSST and resource budget. We then conceive a two-step Blahut-Arimoto (BA)-based optimal search algorithm to numerically solve the functional optimization problem. Third, in light of the current signaling strategy, we further propose an optimal waveform design for Gaussian signaling in multi-input multi-output (MIMO) CAS systems. The associated covariance matrix optimization problem is addressed using a successive convex approximation (SCA)-based waveform design algorithm. Finally, we provide numerical simulation results to demonstrate the effectiveness of the proposed algorithms and to show the unique performance tradeoff between S&C processes.