邹 炜

论文及专利

• [1] Zou, W.; Yu, H.; Yang, B.; Ren, A.; Liu, W. An Improved Soft Actor-Critic Task Offloading and Edge Computing Resource Allocation Algorithm for Image Segmentation Tasks in the Internet of Vehicles. World Electr. Veh. J. 2025, 16, 353.
  URL：点击查看论文
  摘要：This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computing resource allocation system. The core of this system is an improved model-free soft actor-critic (iSAC) algorithm, which is enhanced by incorporating prioritized experience replay (PER). This PER-iSAC algorithm is designed to accelerate the learning process, maintain stability, and improve the efficiency and accuracy of computation offloading. Furthermore, an integrated computing and networking scheduling framework is employed to minimize overall task completion time. Simulation experiments were conducted to compare the PER-iSAC algorithm against baseline algorithms (Standard SAC and PPO). The results demonstrate that the proposed PER-iSAC significantly reduces task allocation error rates and optimizes task completion times. This research offers a practical engineering solution for enhancing the computational capabilities of IoV systems, thereby contributing to the development of more responsive and reliable autonomous driving applications.
• [2] Zou, W.; Yu, H.; Yang, B.; Ren, A.; Liu, W. Enabling Multimodal Logistics Communications with BDS-LEO Collaborations: A Reinforcement Learning Approach. Digital Communications and Networks(DCN). 2025.
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  摘要：This paper proposes a dynamic decision-making mechanism for multimodal logistics communication based on the integrated BeiDou Navigation Satellite System (BDS) and Low Earth Orbit (LEO) dual-system architecture. Aimed at overcoming communication gaps in cross-regional multimodal smart logistics, this paper designs a cloud-edge-terminal collaborative framework integrating BDS high-precision positioning and LEO broadband capabilities. A reinforcement learning-driven Communication Mode Decision System (CMDS) dynamically optimizes link selection among terrestrial (4G/5G) and non-terrestrial networks (5G NTN) by balancing the signal-to-interference-plus-noise ratio (SINR), latency, and cost. Mathematical models formalize multi-objective communication performance across heterogeneous networks and transport modes. Simulation results demonstrate that the Discrete Soft Actor-Critic (DSAC) algorithm achieves a 0.05s average latency, 42 dB SINR, and balanced cost, out performing TRGPPO and D3QN in adaptive network selection across shipping, rail, and road scenarios. The proposed DSAC mechanism enables reliable, cost-efficient global logistics operations.