AI 中文总结
研究用于ISAC的mMIMO-OFDM框架在稀疏ISAR成像中的应用，提出自适应加权二维ADMM算法及联合资源分配框架，推导感知基准并开发SAC方法，实现高分辨率图像恢复，提升频谱效率，揭示重建精度与通信效率的权衡。
AI 中文摘要
本文研究了一种用于集成感知与通信（ISAC）并支持物联网（IoT）等应用的大规模多输入多输出（mMIMO）正交频分复用（OFDM）框架，其结合通信预编码和专用感知波束成形以实现下行链路通信和ISAR成像同时进行。由于间歇性导频传输和稀疏感知子载波激活，接收回波测量不完整，导致稀疏孔径ISAR重建问题。为此开发了自适应加权二维交替方向乘子法（ADMM）算法用于从稀疏观测中进行高分辨率图像恢复。还提出了联合资源分配框架，在通信服务质量和感知约束下优化通信子载波分配、感知子载波选择和发射功率分配。利用信道硬化，基于统计信道状态信息（CSI）推导了最大比（MR）和迫零（ZF）预编码的解析全频段感知基准，同时开发了基于软演员评论家（SAC）的稀疏感知资源分配方法。数值结果表明，所提出的自适应ADMM算法比传统方法改进了稀疏ISAR重建。基于SAC的设计在满足通信和感知约束的同时，在和频谱效率上比全频段基准有显著提升，揭示了ISAR重建精度与通信频谱效率之间的权衡。
英文摘要
This paper investigates a massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) framework for integrated sensing and communication (ISAC) with inverse synthetic aperture radar (ISAR) imaging, supporting applications such as the Internet of Things (IoT). A dual-function architecture combines communication precoding and dedicated sensing beamforming to enable simultaneous downlink communication and ISAR imaging. Due to intermittent pilot transmission and sparse sensing-subcarrier activation, the received echoes provide incomplete measurements, resulting in a sparse-aperture ISAR reconstruction problem. To address this issue, an adaptive reweighted two-dimensional alternating direction method of multipliers (ADMM) algorithm is developed for high-resolution image recovery from sparse observations. A joint resource-allocation framework is also proposed to optimize communication-subcarrier assignment, sensing-subcarrier selection, and transmit power allocation subject to communication quality-of-service and sensing constraints. Exploiting channel hardening, analytical full-band sensing benchmarks based solely on statistical channel state information (CSI) are derived for maximum-ratio (MR) and zero-forcing (ZF) precoding, while a soft actor-critic (SAC)-based method is developed for sparse-sensing resource allocation. Numerical results show that the proposed adaptive ADMM algorithm improves sparse ISAR reconstruction over conventional methods. The SAC-based design also achieves substantial gains in sum spectral efficiency over the full-band benchmarks while satisfying communication and sensing constraints, thereby revealing the tradeoff between ISAR reconstruction accuracy and communication spectral efficiency.
