Experience-Centric Resource Management in ISAC Networks: A Digital Agent-Assisted Approach

TL;DR

This paper introduces a digital agent-assisted resource management framework for ISAC networks that optimizes user QoE by integrating predictive modeling, clustering, and advanced optimization techniques, leading to improved performance.

Contribution

It presents a novel digital agent module with user-specific QoE modeling and a two-layer optimization algorithm combining deep reinforcement learning and convex optimization.

Findings

Outperforms benchmark schemes in user QoE metrics

Effectively models user behavior and environmental factors

Enhances sensing accuracy through resource allocation

Abstract

In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) networks. Particularly, user QoE is a comprehensive metric that integrates quality of service (QoS), user behavioral dynamics, and environmental complexity. The novel DA module includes a user status prediction model, a QoS factor selection model, and a QoE fitting model, which analyzes historical user status data to construct and update user-specific QoE models. Users are clustered into different groups based on their QoE models. A Cram\'er-Rao bound (CRB) model is utilized to quantify the impact of allocated communication resources on sensing accuracy. A joint optimization problem of communication and computing resource management is formulated to maximize long-term user QoE while satisfying CRB and resource constraints. A two-layer data-model-driven algorithm is developed to solve the formulated problem, where the top layer utilizes an advanced deep reinforcement learning algorithm to make group-level decisions, and the bottom layer uses convex optimization techniques to make user-level decisions. Simulation results based on a real-world dataset demonstrate that the proposed DA-assisted resource management scheme outperforms benchmark schemes in terms of user QoE.