Decentralized Over-the-Air Computation for Edge AI Inference with Integrated Sensing and Communication
IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM（2023）
摘要
Collaborative artificial intelligent (AI) inference has been an effective approach to deploying well-trained AI models at the network edge for empowering immersive intelligent services such as autonomous driving and smart cities. In this paper, we propose an integrated sensing-computation-communication (ISCC) scheme for decentralized collaborative inference systems. In the proposed scheme, multiple devices connect to each other via device-to-device (D2D) links. Each device first extracts a homogeneous feature vector from the raw sensory data obtained from the same wide view of the source target and then aggregates all local feature vectors using the over-the-air computation technique. To further enhance the spectrum efficiency, the full-duplex technology is utilized to allow all devices to transmit and receive in the same frequency band. This, however, introduces significant self-interference and coupling among different tasks. To address these challenges, a multi-objective optimization-based ISCC approach is proposed.
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关键词
Local Features,Sensor Data,Local Vector,Artificial Intelligence Models,Wider View,Energy Consumption,Training Dataset,Support Vector Machine,Additive Noise,Feature Dimension,Distribution Of Elements,Singular Value Decomposition,Task Accuracy,Characteristics Of Elements,Feature Aggregation,Inference Accuracy,Communication Overhead,Inference Task,Multicast,Received Signal Power,Minimum Gain,Beamforming Vector,Frequency Modulated Continuous Wave,Baseline Schemes,Inference Performance,Central Server,Multilayer Perceptron,Angle Of Arrival,Dimensional Feature Vector,Feature Space
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