Massive MIMO-OFDM ISAC for Sparse ISAR Imaging: Joint Power and Subcarrier Allocation

Abstract

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.