| Abstract |
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. © 2023 IEEE. |
