In the sixth-generation (6G) era, wireless networks need to support a large number of ultra-low latency and high-reliability applications. However, conventional bit-level communication paradigms fail to capture the intrinsic meaning of multi-modal data, leading to inefficiencies in both communication and computation for downstream tasks. To address this limitation, we propose a semantic-driven task offloading framework in low-altitude wireless networks (LAWNs), where multiple uncrewed aerial vehicles (UAVs) provide on-demand edge computing services to ground terminals (GTs). Specifically, we employ a vector quantized-variational autoencoder (VQ-VAE) to enable joint coding and modulation (JCM) of cross-modal data. Then, we formulate an optimization problem that simultaneously determines UAV deployment, task offloading decisions, transmit power allocation, and computational resource scheduling, with the objective of maximizing the quality of experience (QoE) for GTs. To solve this problem, we employ the Karush-Kuhn-Tucker (KKT) conditions to address the UAV deployment subproblem, and utilize a multi-agent proximal policy optimization (MAPPO) approach to tackle the task offloading and resource allocation subproblem. Simulation results demonstrate that the proposed method significantly enhances QoE, achieving more than 5.87% improvement over representative benchmarks, while reduces task latency by more than 4.89% and improves energy efficiency by more than 3.76%.
Fang-Fang Yin, Yue-Xin Liu, Wanli Ni et al.· IEEE Transactions on Communi...· 0 citations
A versatile DS2D system that supports cooperative task offloading and non-cooperative access monitoring, and Transformer-based models to enable blind signal detection and automatic modulation classification (AMC) are proposed.
Sai Huang, Wanli Ni, Ke Lv et al.· IEEE Vehicular Technology Ma...· 0 citations
Smart factories are evolving into agentic control systems powered by wireless connectivity, edge computing, and artificial intelligence. This evolution alleviates computational limits and enhances production efficiency. However, the heterogeneity of spatiotemporal control logic, coupled with indeterminate wireless conditions, makes it challenging to coordinate control tasks and radio resources. To overcome these challenges, this paper presents a mixed graph-driven model to characterize spatiotemporal dependencies among control tasks and proposes a semi-centralized multi-agent collaborative framework. This paper employs an improved heterogeneous twin delayed deep deterministic policy gradient algorithm to jointly optimize task scheduling and radio resource allocation, thereby minimizing the average processing delay of industrial control processes. Simulation results demonstrate that the proposed algorithm achieves outstanding performance compared to benchmarks, improving execution success rate and data processing rate, as well as reducing model training time.
Sha Li, Lei Sun, Wanli Ni et al.· IEEE Transactions on Cogniti...· 0 citations
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