低空无线网络中同时感知数据采集与共享：基本限制与最优信号设计
Simultaneous Sensing Data Acquisition and Sharing in Low-Altitude Wireless Networks: Fundamental Limits and Optimal Signaling
AI总结：
本文研究低空无线网络中同时感知数据采集与共享系统的信息理论框架、信道输入最优分配及高斯信号波形设计，提出改进的源信道分离定理，设计双功能信号最优输入及多输入多输出系统中的波形优化算法，并通过仿真验证算法性能。
摘要：
在低空无线网络中，通过ISAC信号策略实现的同时感知数据采集与共享（SDAS）成为典型应用场景。本文主要研究SDAS系统的三个主要方面，即信息理论框架、信道输入的最优分配以及高斯信号的最优波形设计。首先，我们建立了信息理论框架，并开发了一个适用于SDAS系统的改进源信道分离定理（MSST）。所提出的MSST阐明了在感知与通信（S&C）过程中的可分离失真度量下，可实现的失真度、编码速率和通信信道容量之间的关系。其次，我们提出了双功能信号的最优信道输入设计，旨在在MSST和资源预算约束下最小化SDAS失真。然后，我们构思了一个基于两步Blahut-Arimoto（BA）的最优搜索算法来数值求解功能优化问题。第三，为了提供实用设计见解，我们进一步提出了多输入多输出（MIMO）SDAS系统中的高斯信号最优波形设计。相关的协方差矩阵优化问题通过基于递推凸逼近（SCA）的波形设计算法解决。最后，我们提供了数值仿真结果，以证明所提算法的有效性，这些结果刻画了S&C过程之间的独特性能权衡。
英文摘要：
In the low-altitude wireless networks, the simultaneous sensing data acquisition and sharing (SDAS) through an ISAC signaling strategy becomes a typical application scenario. In this paper, we mainly investigate three primary aspects of the SDAS system, namely, the information-theoretic framework, the optimal distribution of channel input, and the optimal waveform design for Gaussian signaling. First, we establish the information-theoretic framework and develop a modified source-channel separation theorem (MSST) tailored for the SDAS 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 SDAS 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, to provide practical design insights, we further propose an optimal waveform design for Gaussian signaling in multi-input multi-output (MIMO) SDAS 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, which characterize the unique performance tradeoff between S&C processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
