Edge Learning and Communication

OUTLINE

Edge Learning and Communication

Journal: Edge Learning and Communication

Journal: Mobile Edge Computing

Journal: 5G Technologies

Journal: Stochastic Geometry

J. Huang, K. Yuan, C. Huang, K. Huang, "D^2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications," submitted to IEEE for possible publication (Available: ArXiv)
X. Chen, K. B. Letaief, K. Huang, "On the View-and-Channel Aggregation Gain in Integrated Sensing and Edge AI," to appear in IEEE J. Sel. Areas in Commun. (Available: ArXiv)
H. Wu, X. Chen, K. Huang, "Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Service," to appear in IEEE Wireless Commun. (Available: ArXiv)
Y. Cang, M. Chen, K. Huang, "Joint Batching and Scheduling for High-Throughput Multiuser Edge AI with Asynchronous Task Arrivals," to appear in IEEE Trans. on Wireless Commun. (Available: ArXiv)
H. Wu, Q. Zeng, K. Huang, "Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting" to appear in IEEE Trans. on Wireless Commun. (Available: ArXiv)
Z. Wang, K. Huang, Y C. Eldar "Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference," to appear in IEEE Trans. on Wireless Commun. (Available: ArXiv)
Z. Liu, Q. Lan, A E. Kalør, P. Popovski, K. Huang, "Over-the-Air Multi-View Pooling for Distributed Sensing," IEEE Trans. on Wireless Commun. vol. 61, no. 12, pp. 70-76, December 2023 (Available: ArXiv)
K. Huang, Q. Lan, Z. Liu. L. Yang, "Semantic Data Sourcing for 6G Edge Intelligence," IEEE Commun. Mag. vol. 61, no. 12, pp. 70-76, December 2023 (Available: ArXiv)
H. Wu, H. Tan, R. He, X. Qi, K. Huang, "Vertical Layering of Quantized Neural Networks for Heterogeneous Inference," IEEE Trans. Pattern Anal. Mach. Intell. vol. 45, no. 12, pp. 15964-15978, Dec. 2023 (Available: ArXiv)
K. Huang, H. Wu, Z. Liu, X. Qi, "In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks," IEEE Wireless Commun. vol. 30, no. 3, pp. 96-102, June 2023 (Available: ArXiv)
Z. Liu, Q. Lan, and K. Huang, "Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting," IEEE J. Sel. Areas in Commun. vol. 41, no. 4, pp. 1186-1200, April 2023 (Available: ArXiv)
Z. Lin, Y. Gong, and K. Huang, "Distributed Over-the-air Computing for Fast Distributed Optimization: Beamforming Design and Convergence Analysis," IEEE J. Sel. Areas in Commun. vol. 41, no. 1, pp. 274-287, Jan. 2023 (Available: ArXiv)
X. Li, G. Zhu, K. Han, Y. Gong, K. Huang, "Energy Efficient Wireless Crowd Labelling: Joint Annotator Clustering and Power Control," IEEE Trans. on Wireless Commun. vol. 22, no. 3, pp. 2022-2035, March 2023 (IEEE Explore).
P. Popovski, F. Chiariotti, K. Huang, A. E. Kalør, M. Kountouris, N. Pappas, B. Soret, "A Perspective on Time towards Wireless 6G," in Proc. of the IEEE, vol. 10, no. 8, pp. 116-1146, Aug. 2022. (Available: ArXiv)
Q. Zeng, Y. Du, K. Huang, "Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer" IEEE Trans. on Wireless Commun. . vol. 21, no. 1, pp. 680-695, Jan. 2022. (IEEE Xplore)
S. Huang, Z. Zhang, S. Wang, R. Wang, K. Huang, "Accelerating Federated Edge Learning via Topology Optimization," IEEE Internet Things J. vol. 10, no. 3, pp. 2056-2070, 1 Feb.1, 2023 (Available: ArXiv)
X. Chen, E. G. Larsson, K. Huang, "Analog MIMO Communication for One-shot Distributed Principal Component Analysis," IEEE Trans. Signal Process. vol. 70, pp. 3328-3342, 2022 (Available: ArXiv)
Q. Lan, Q. Zeng, P. Popovski, D. Gündüz, and K. Huang, "Progressive Feature Transmission for Split Inference at the Wireless Edge," IEEE Trans. on Wireless Commun. vol. 22, no. 6, pp. 3837-3852, June 2023 (Available: ArXiv)
Q. Lan, D. Wen, Z. Zhang, Q. Zeng, X. Chen, P. Popovski, K. Huang "What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence," invited for J. Commun. Inf. Netw., 2021. (Available: ArXiv)
D. Wen, K. Jeon, K. Huang, "Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices," IEEE Wireless Communication Letters. vol. 11, no. 5, pp. 923-927, May 2022. (Available: ArXiv)
Z. Lin, X. Li, V. Lau, Y. Gong and K. Huang, "Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis," IEEE Trans. on Wireless Comm. vol. 21, no. 3, pp. 1542-1556, March 2022. (Available: ArXiv)
Q. Zeng, Y. Du and K. Huang, "Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer," IEEE Trans. on Wireless Comm. vol. 21, no. 1, pp. 680-695, Jan. 2022. (Available: ArXiv)
Z. Zhang, G. Zhu, R. Wang, V. K. N. Lau, and K. Huang, "Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis", IEEE Trans. on Wireless Comm. vol. 21, no. 10, pp. 7926-7941, Oct. 2022. (ArXiv)
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", submitted to IEEE J. Sel. Area on Commun. . (ArXiv)
D. Wen, K.-J. Jeon, M. Bennis, K. Huang, "Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels", submitted to IEEE for possible publication. (ArXiv)
G. Zhu, J. Xu and K. Huang, "Over-the-Air Computing for 6G -- Turning Air into a Computer", submitted to IEEE for possible publication. (ArXiv)
Q. Lan, Y. Du, P. Popovski and K. Huang, "Capacity of Remote Classification over Wireless Channels", to appear in IEEE Trans. Commun. . (ArXiv)
J. Wen, M. Sheng, J.g Li, and K. Huang, "Assisting for Intelligent Wireless Networks with Traffic Prediction: Exploring and Exploiting Predictive Causality in Wireless Traffic", to appear in IEEE Commun. Magazine, 2020. (IEEE Xplore)
X. Li, G. Zhu, K. Shen, W. Yu, Y. Gong, and K. Huang, “Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling”, IEEE Trans. Wireless Commun. , vol. 19, no. 9, pp. 6116-6129, Sept. 2020. (ArXiv)
Q. Lan, B. Lv, R. Wang, K. Huang and Y. Gong, "Adaptive Video Streaming in Massive MIMO Networks via Approximate MDP and Reinforcement Learning", IEEE Trans. Wireless Commun. , vol. 19, no. 9, pp. 5716-5731, Sept. 2020. (IEEE Xplore)
J. Ren, Y. He, D. Wen, G. Yu, K. Huang, and D. Guo, "Scheduling in Cellular Federated Edge Learning with Importance and Channel Awareness”, IEEE Trans. Wireless Commun., vol. 19, no. 11, pp. 7690-7703, Nov. 2020. (ArXiv)
D. Wen, M. Bennis, K. Huang, “Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning”, IEEE Trans. Wireless Commun., vol. 19, no. 12, pp. 8272-8286, Nov. 2020. (ArXiv)
G. Zhu, Y. Du, D. Gunduz, and K. Huang, “One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis”, to appear in IEEE Trans. Wireless Commun., 2020. (ArXiv)
D. Wen, X. Li, Q. Zeng, J. Ren and K. Huang, “An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning”, an invited paper in Journal of Communications and Information Networks, 2020. (ArXiv)
D. Liu, G. Zhu, J. Zhang, and K. Huang, “Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning”, to appear in IEEE Trans. Cognitive Comm. and Networking, 2020. (ArXiv)
Y. Du, S. Yang, and K. Huang, “High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning”, to appear in IEEE Trans. Signal Process. , 2020. (ArXiv)
Q. Zeng, Y. Du, K. Leung and K. Huang, "Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing”, submitted to IEEE for possible publication. (ArXiv)
G. Zhu, Y. Wang, and K. Huang, "Broadband Analog Aggregation for Low-Latency Federated Edge Learning”, accepted to IEEE Trans. Wireless Commun.. (ArXiv)
D. Liu, G. Zhu, Q. Zeng, J. Zhang, and K. Huang, "Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission”, to appear in IEEE Trans. Wireless Commun., 2020. (ArXiv)
G. Zhu, D. Liu, Y, Du, C. You, J. Zhang, and K. Huang, "Towards an Intelligent Edge: Wireless Communication Meets Machine Learning”, to appear in IEEE Commun. Magazine . (ArXiv)
J. Zhang, G. Zhu, R. Heath Jr., and K. Huang, “Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning”, submitted to IEEE for possible publication. (ArXiv)
Y. Du and K. Huang, "Fast Analog Transmission for High-Mobility Wireless Data Acquisition in Edge Learning", IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 468 - 471, April 2019. (ArXiv)
Y. Du, G. Zhu, J. Zhang, and K. Huang, "Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold", IEEE Trans. on Signal Process. , vol. 66, no. 22, pp. 6031-6046, Nov. 2018. (ArXiv)
G. Zhu, S.-W. Ko and K. Huang, "Inference from Randomized Transmissions by Many Backscatter Sensors", IEEE Trans. on Wireless Commun., vol. 17, no. 5, pp 3111-3127, May 2018. (ArXiv)

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OUTLINE

Edge Learning and Communication

Z. Zhang, G. Zhu, R. Wang, V. K. N. Lau, and K. Huang, "Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis", IEEE Trans. on Wireless Comm. vol. 21, no. 10, pp. 7926-7941, Oct. 2022. (ArXiv)

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", submitted to IEEE J. Sel. Area on Commun. . (ArXiv)

D. Wen, K.-J. Jeon, M. Bennis, K. Huang, "Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels", submitted to IEEE for possible publication. (ArXiv)

G. Zhu, J. Xu and K. Huang, "Over-the-Air Computing for 6G -- Turning Air into a Computer", submitted to IEEE for possible publication. (ArXiv)

Q. Lan, Y. Du, P. Popovski and K. Huang, "Capacity of Remote Classification over Wireless Channels", to appear in IEEE Trans. Commun. . (ArXiv)

J. Wen, M. Sheng, J.g Li, and K. Huang, "Assisting for Intelligent Wireless Networks with Traffic Prediction: Exploring and Exploiting Predictive Causality in Wireless Traffic", to appear in IEEE Commun. Magazine, 2020. (IEEE Xplore)

X. Li, G. Zhu, K. Shen, W. Yu, Y. Gong, and K. Huang, “Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling”, IEEE Trans. Wireless Commun. , vol. 19, no. 9, pp. 6116-6129, Sept. 2020. (ArXiv)

Q. Lan, B. Lv, R. Wang, K. Huang and Y. Gong, "Adaptive Video Streaming in Massive MIMO Networks via Approximate MDP and Reinforcement Learning", IEEE Trans. Wireless Commun. , vol. 19, no. 9, pp. 5716-5731, Sept. 2020. (IEEE Xplore)

J. Ren, Y. He, D. Wen, G. Yu, K. Huang, and D. Guo, "Scheduling in Cellular Federated Edge Learning with Importance and Channel Awareness”, IEEE Trans. Wireless Commun., vol. 19, no. 11, pp. 7690-7703, Nov. 2020. (ArXiv)

D. Wen, M. Bennis, K. Huang, “Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning”, IEEE Trans. Wireless Commun., vol. 19, no. 12, pp. 8272-8286, Nov. 2020. (ArXiv)

G. Zhu, Y. Du, D. Gunduz, and K. Huang, “One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis”, to appear in IEEE Trans. Wireless Commun., 2020. (ArXiv)

D. Wen, X. Li, Q. Zeng, J. Ren and K. Huang, “An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning”, an invited paper in Journal of Communications and Information Networks, 2020. (ArXiv)

D. Liu, G. Zhu, J. Zhang, and K. Huang, “Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning”, to appear in IEEE Trans. Cognitive Comm. and Networking, 2020. (ArXiv)

Y. Du, S. Yang, and K. Huang, “High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning”, to appear in IEEE Trans. Signal Process. , 2020. (ArXiv)

Q. Zeng, Y. Du, K. Leung and K. Huang, "Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing”, submitted to IEEE for possible publication. (ArXiv)

G. Zhu, Y. Wang, and K. Huang, "Broadband Analog Aggregation for Low-Latency Federated Edge Learning”, accepted to IEEE Trans. Wireless Commun.. (ArXiv)

D. Liu, G. Zhu, Q. Zeng, J. Zhang, and K. Huang, "Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission”, to appear in IEEE Trans. Wireless Commun., 2020. (ArXiv)

G. Zhu, D. Liu, Y, Du, C. You, J. Zhang, and K. Huang, "Towards an Intelligent Edge: Wireless Communication Meets Machine Learning”, to appear in IEEE Commun. Magazine . (ArXiv)

J. Zhang, G. Zhu, R. Heath Jr., and K. Huang, “Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning”, submitted to IEEE for possible publication. (ArXiv)

Y. Du and K. Huang, "Fast Analog Transmission for High-Mobility Wireless Data Acquisition in Edge Learning", IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 468 - 471, April 2019. (ArXiv)

Y. Du, G. Zhu, J. Zhang, and K. Huang, "Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold", IEEE Trans. on Signal Process. , vol. 66, no. 22, pp. 6031-6046, Nov. 2018. (ArXiv)

G. Zhu, S.-W. Ko and K. Huang, "Inference from Randomized Transmissions by Many Backscatter Sensors", IEEE Trans. on Wireless Commun., vol. 17, no. 5, pp 3111-3127, May 2018. (ArXiv)