Joint Task Offloading and Trajectory Optimization for Cooperative Multi-Branch DNN Inference in CR-SAGIN-Enhanced IoV

摘要

The Space-Air-Ground Integrated Network (SAGIN) has emerged as a pivotal architecture for sixth-generation (6G) mobile communication systems, offering extensive multi-domain and multi-dimensional coverage. To support intelligent services within SAGIN, deep neural network (DNN) inference tasks are often distributed across multiple devices due to the increasing scale of DNN parameters and constrained communication and computation resources. In this paper, to enhance intelligent services in the Internet of Vehicles (IoV), a cognitive-radio (CR)-enhanced SAGIN framework is proposed, where unmanned aerial vehicles (UAVs) are responsible for data collection and cooperate with vehicles to complete complex DNN inference tasks. A joint optimization problem that coordinates device association, model splitting with early exiting, resource allocation, and UAV trajectory planning is proposed to improve the average inference efficiency over a finite multi-round mission, defined as the average of the per-round ratios between aggregate inference accuracy and aggregate inference latency. Since the problem is highly coupled and non-convex, we decompose it into several subproblems and solve them iteratively. A quadratic unconstrained binary optimization (QUBO)-based graph attention network (GAT) approach with independent branch selection is proposed to solve the joint device association and model splitting problem, where a differentiable loss function is derived through Hamiltonian relaxation. In addition, we obtain a convex approximation of the transmit-power subproblem, while computation-resource allocation is solved as a convex optimization problem. For multi-round operations, we design a multi-agent reinforcement learning (MARL)-based trajectory optimization algorithm under a centralized training and decentralized execution paradigm. Simulation results on widely used DNN models, including AlexNet and VGG16, demonstrate that the proposed approach can improve the efficiency of collaborative DNN inference.