Digital Twin-Assisted Resource Allocation in ISCC-Enabled IoV: When DRL Meets LLMs

Abstract

Integrated sensing, communication, and computation (ISCC) provides a critical enabling platform in supporting the diverse services in the Internet of Vehicles (IoV). However, effective heterogeneous IoV service provisioning relies on both communication-centric and beyond-communication performance metrics, making unified resource allocation challenging. Moreover, competition from concurrent services for limited multi-dimensional resources is intensified in dynamic vehicular environments. In this paper, we investigate the resource allocation problem for concurrent communication and target classification services in an ISCC-enabled IoV system. To solve the problem, we first introduce the value of service (VoS) to unify communication rate and classification accuracy into a common measure that captures the degree of heterogeneous service fulfillment. To reduce the complexity of dynamic problem optimization, we propose a digital twin-assisted proximal policy optimization (DTPPO) algorithm, in which the digital twin exploits both current and historical information to generate predictive information, thereby enhancing policy learning in dynamic environments. Furthermore, we develop a large language model (LLM)-enhanced DTPPO (LLM-DTPPO) algorithm, which leverages the contextual understanding and domain knowledge of LLMs to reshape the reward function and improve resource allocation performance under multi-dimensional resource competition. Simulation results based on real-world vehicle mobility traces demonstrate that the proposed algorithms outperform existing benchmark schemes.