Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI (2306.06603v1)
Abstract: With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc. featuring massive data sensing, analyzing and inference as well critical latency in beyond 5G (B5G) networks, edge AI has been proposed to provide high-performance computation of a conventional cloud down to the network edge. Recently, convergence of wireless sensing, computation and communication (SC${}2$) for specific edge AI tasks, has aroused paradigm shift by enabling (partial) sharing of the radio-frequency (RF) transceivers and information processing pipelines among these three fundamental functionalities of IoT. However, most existing design frameworks separate these designs incurring unnecessary signaling overhead and waste of energy, and it is therefore of paramount importance to advance fully integrated sensing, computation and communication (ISCC) to achieve ultra-reliable and low-latency edge intelligence acquisition. In this article, we provide an overview of principles of enabling ISCC technologies followed by two concrete use cases of edge AI tasks demonstrating the advantage of task-oriented ISCC, and pointed out some practical challenges in edge AI design with advanced ISCC solutions.
- M. Chen, D. Gündüz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, “Distributed learning in wireless networks: Recent progress and future challenges,” IEEE J. Sel. Areas Commun., vol. 39, no. 12, pp. 3579–3605, Dec. 2021.
- Y. Wang, K. Wu, and L. M. Ni, “Wifall: Device-free fall detection by wireless networks,” IEEE Trans. Mob. Comput., vol. 16, no. 2, pp. 581–594, Feb. 2017.
- Y. Cui, F. Liu, X. Jing, and J. Mu, “Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges,” IEEE Netw., vol. 35, no. 5, pp. 158–167, Sept./Oct. 2021.
- X. Li, F. Liu, Z. Zhou, G. Zhu, S. Wang, K. Huang, and Y. Gong, “Integrated sensing, communication, and computation over-the-air: MIMO beamforming design,” to appear in IEEE Trans. Wireless Commun., Jan. 2023.
- P. Liu, G. Zhu, S. Wang, W. Jiang, W. Luo, H. V. Poor, and S. Cui, “Toward ambient intelligence: Federated edge learning with task-oriented sensing, computation, and communication integration,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 1, pp. 158–172, Dec. 2023.
- D. Wen, P. Liu, G. Zhu, Y. Shi, J. Xu, Y. C. Eldar, and S. Cui, “Task-oriented sensing, computation, and communication integration for multi-device edge AI,” 2022. [Online]. Available: https://arxiv.org/abs/2207.00969
- F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, “Joint radar and communication design: Applications, state-of-the-art, and the road ahead,” IEEE Trans. Commun., vol. 68, no. 6, pp. 3834–3862, Jun. 2020.
- G. Zhu, J. Xu, K. Huang, and S. Cui, “Over-the-air computing for wireless data aggregation in massive IoT,” IEEE Wireless Commun., vol. 28, no. 4, pp. 57–65, Aug. 2021.
- K. Roy, A. Jaiswal, and P. Panda, “Towards spike-based machine intelligence with neuromorphic computing,” Nature, vol. 575, no. 7784, pp. 607–617, Nov. 2019.
- X. Cao, G. Zhu, J. Xu, and S. Cui, “Transmission power control for over-the-air federated averaging at network edge,” IEEE J. Sel. Areas Commun., vol. 40, no. 5, pp. 1571–1586, May 2022.
- H. Xing, O. Simeone, and S. Bi, “Federated learning over wireless device-to-device networks: Algorithms and convergence analysis,” IEEE J. Sel. Areas Commun., vol. 39, no. 12, pp. 3723–3741, Dec. 2021.
- A. Koloskova, S. U. Stich, and M. Jaggi, “Decentralized stochastic optimization and gossip algorithms with compressed communication,” in International Conference on Machine Learning (ICML), Long Beach, CA, USA, Jun. 2019.
- D. Liu and O. Simeone, “Privacy for free: Wireless federated learning via uncoded transmission with adaptive power control,” IEEE J. Sel. Areas Commun., vol. 39, no. 1, pp. 170–185, Jan. 2021.
- D. Liu, G. Zhu, Q. Zeng, J. Zhang, and K. Huang, “Wireless data acquisition for edge learning: Data-importance aware retransmission,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 406–420, Jan. 2021.
- J. Chen, N. Skatchkovsky, and O. Simeone, “Neuromorphic wireless cognition: Event-driven semantic communications for remote inference,” IEEE Trans. Cogn. Commun. Netw., vol. 9, no. 2, pp. 252–265, Jan. 2023.
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