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.

翻译：本文提出了一种称为通信辅助感知（CAS）系统的双功能信令策略，通过该策略同时获取和共享感知数据，有望为用户提供超视距感知能力。我们主要关注三个核心方面：信息论框架、信道输入的最优分布以及高斯信号的最优波形设计。首先，我们建立了信息论框架，并针对CAS系统提出了改进的源信道分离定理（MSST）。所提出的MSST阐明了在感知与通信（S&C）过程的失真度量可分离的情况下，可达到的失真、编码速率与通信信道容量之间的关系。其次，我们提出了一种双功能信令的最优信道输入设计，其目标是在MSST和资源预算的约束下最小化CAS失真。随后，我们设计了一种基于两步Blahut-Arimoto（BA）的最优搜索算法，以数值方式求解该泛函优化问题。第三，基于现有信令策略，我们进一步提出了多输入多输出（MIMO）CAS系统中高斯信号的最优波形设计。相关的协方差矩阵优化问题通过一种基于逐次凸逼近（SCA）的波形设计算法得以解决。最后，我们提供了数值仿真结果，以验证所提算法的有效性，并展示S&C过程之间独特的性能权衡。