CPPU: Policy space diversity for informative path planning and GAI-enabled updating CSI in ISAC

Abstract

Integrated Sensing and Communication (ISAC) is a core component of 6G technology, driving advancements in networking and communication in combination with Generative Artificial Intelligence (GAI). By utilizing GAI’s predictive functions, indoor positioning achieves higher efficiency through optimized fingerprint localization methods. Traditional approaches to Channel State Information (CSI) collection remain costly and lack adaptability in dynamic environments. To address these challenges, this paper proposes an algorithm that combines multi-agent learning with a GAI model for CSI acquisition, Path Planning, and Updating, namely CPPU. Initially, the terrain is partitioned into multiple regions, each corresponding to a specific agent, ensuring comprehensive path coverage and eliminating backtracking solutions. Subsequently, the dynamic programming strategy of the policy space response oracle is employed …