A Riemannian Manifold Approach to Constrained Resource Allocation in ISAC
arxiv(2024)
摘要
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
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.
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