This paper introduces a new resource allocation framework for integrated sensing and communication (ISAC) systems, which are expected to be fundamental aspects of sixth-generation networks. In particular, we develop an augmented Lagrangian manifold optimization (ALMO) framework designed to maximize communication sum rate while satisfying sensing beampattern gain targets and base station (BS) transmit power limits. ALMO applies the principles of Riemannian manifold optimization (MO) to navigate the complex, non-convex landscape of the resource allocation problem. It efficiently leverages the augmented Lagrangian method to ensure adherence to constraints. We present comprehensive numerical results to validate our framework, which illustrates the ALMO method's superior capability to enhance the dual functionalities of communication and sensing in ISAC systems. For instance, with 12 antennas and 30 dBm BS transmit power, our proposed ALMO algorithm delivers a 10.1% sum rate gain over a benchmark optimization-based algorithm. This work demonstrates significant improvements in system performance and contributes a new algorithmic perspective to ISAC resource management.

翻译：本文提出了一种面向集成感知与通信（ISAC）系统的新型资源分配框架，此类系统被认为是第六代网络的基础要素。具体而言，我们开发了一种增广拉格朗日流形优化（ALMO）框架，旨在在满足感知波束模式增益目标与基站（BS）发射功率限制的同时，最大化通信总速率。ALMO应用黎曼流形优化（MO）的原理来导航资源分配问题的复杂非凸景观，并高效利用增广拉格朗日方法确保约束条件的满足。我们通过全面的数值结果验证了所提框架，展示了ALMO方法在增强ISAC系统通信与感知双重功能方面的卓越能力。例如，在12天线和30 dBm BS发射功率条件下，我们提出的ALMO算法相比基于基准优化的算法实现了10.1%的总速率增益。这项工作证明了系统性能的显著提升，并为ISAC资源管理贡献了新的算法视角。