Digital Twin-Assisted Task Offloading and Resource Allocation in ISAC-Enabled Internet of Vehicles

2025-11-08

AI Summary

To address challenges of low spectral efficiency, poor adaptability to dynamic environments, and long-term queue instability in task offloading and resource allocation for 6G-enabled vehicular networks, this paper proposes a digital twin (DT)-assisted joint optimization framework within an integrated sensing and communication (ISAC) architecture. We innovatively design an instruction-level transmission mode to compress data payloads and develop a Lyapunov-driven DT-enhanced multi-agent proximal policy optimization algorithm (Ly-DTMPPO), operating under a centralized-training-with-decentralized-execution paradigm to enable global state awareness and adaptive decision-making. Theoretical analysis guarantees queue stability. Experimental results demonstrate that the proposed method significantly outperforms existing baselines in system cost (weighted sum of latency and energy consumption), spectral efficiency, and long-term queue stability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Adversarial Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The convergence of the Internet of vehicles (IoV) and 6G networks is driving the evolution of next-generation intelligent transportation systems. However, IoV networks face persistent challenges, including low spectral efficiency in vehicular communications, difficulty in achieving dynamic and adaptive resource optimization, and the need for long-term stability under highly dynamic environments. In this paper, we study the problem of digital twin (DT)-assisted task offloading and resource allocation in integrated sensing and communication (ISAC)-enabled IoV networks. The objective is to minimize the long-term average system cost, defined as a weighted combination of delay and energy consumption, while ensuring queue stability over time. To address this, we employ an ISAC-enabled design and introduce two transmission modes (i.e., raw data transmission (DataT) and instruction transmission (InstrT)). The InstrT mode enables instruction-level transmission, thereby reducing data volume and improving spectral efficiency. We then employ Lyapunov optimization to decompose the long-term stochastic problem into per-slot deterministic problems, ensuring long-term queue stability. Building upon this, we propose a Lyapunov-driven DT-enhanced multi-agent proximal policy optimization (Ly-DTMPPO) algorithm, which leverages DT for global state awareness and intelligent decision-making within a centralized training and decentralized execution (CTDE) architecture. Extensive simulations verify that Ly-DTMPPO achieves superior performance compared with existing benchmarks.

Problem

• Minimizing system cost in IoV networks through task offloading optimization
• Enhancing spectral efficiency in vehicular communications using ISAC technology
• Ensuring long-term queue stability under highly dynamic vehicular environments

Innovation

• Digital twin-assisted task offloading and resource allocation
• Integrated sensing and communication with two transmission modes
• Lyapunov-driven multi-agent proximal policy optimization algorithm