The emergence of 6G native AI network offers new opportunities to support accuracy-critical and latency-sensitive AI inference tasks in Internet-of-Vehicles(IoV)scenarios.However,existing computing offloading schemes ...The emergence of 6G native AI network offers new opportunities to support accuracy-critical and latency-sensitive AI inference tasks in Internet-of-Vehicles(IoV)scenarios.However,existing computing offloading schemes often treat the network merely as a communication pipeline or edge computing node,lacking the joint scheduling of the communication resource and the computational resource in a native AI infrastructure.Besides,the deterioration of the Vehicle-to-Infrastructure(V2I)channel is likely to decrease the inference accuracy of AI tasks by decreasing the quality of data offloading.To this end,this paper considers a road lane detection task in IoV scenario and proposes a Quality of AI Service(QoAIS)-guaranteed AI service offloading architecture.Firstly,the functionality between inference accuracy and Signal-to-Noise Ratio(SNR)under different Modulation and Coding Schemes(MCS)is established by the numerical experiments.On this basis,a cross-layer optimization framework is introduced to maximize the number of QoAIS-guaranteed tasks by jointly optimizes MCS selection,uplink bandwidth allocation,and computing resource allocation,simultaneously.A Particle Swarm Optimization(PSO)algorithm is introduced to solve this problem.Numerical results show that the proposed PSO algorithm can significantly improve the number of QoAIS-guaranteed tasks compared with that of the baselines.展开更多
文摘The emergence of 6G native AI network offers new opportunities to support accuracy-critical and latency-sensitive AI inference tasks in Internet-of-Vehicles(IoV)scenarios.However,existing computing offloading schemes often treat the network merely as a communication pipeline or edge computing node,lacking the joint scheduling of the communication resource and the computational resource in a native AI infrastructure.Besides,the deterioration of the Vehicle-to-Infrastructure(V2I)channel is likely to decrease the inference accuracy of AI tasks by decreasing the quality of data offloading.To this end,this paper considers a road lane detection task in IoV scenario and proposes a Quality of AI Service(QoAIS)-guaranteed AI service offloading architecture.Firstly,the functionality between inference accuracy and Signal-to-Noise Ratio(SNR)under different Modulation and Coding Schemes(MCS)is established by the numerical experiments.On this basis,a cross-layer optimization framework is introduced to maximize the number of QoAIS-guaranteed tasks by jointly optimizes MCS selection,uplink bandwidth allocation,and computing resource allocation,simultaneously.A Particle Swarm Optimization(PSO)algorithm is introduced to solve this problem.Numerical results show that the proposed PSO algorithm can significantly improve the number of QoAIS-guaranteed tasks compared with that of the baselines.
