Project Description

Data Augmentation and Adaptive Learning for Next Generation Wireless Spectrum Systems

Deep learning has shown great promise in solving many open challenges in wireless networking research and applications. Deep learning is data hungry, and one of the critical obstacles towards fulfilling its promise is facilitating the acquisition of sufficient amounts of data to train and validate deep learning models. The primary goal of this project is to devise innovative approaches that enable wireless researchers and practitioners to acquire data more efficiently at reduced cost and to utilize existing data more effectively. Findings from this project are expected to fuel future breakthroughs in wireless research by making deep learning models more widely applicable.

Oct. 1, 2021 ~ Sept. 30, 2024

Project Team

• Shiwen Mao

• Slobodan Vucetic

• Jie Wu

• Xuyu Wang

• Graduate Students: Ziqi Wang (Auburn), Yubin Duan (Temple Univ.), Abdalaziz Sawwan (Temple Univ.), Sai Shi (Temple Univ.), Hanzi Xu (Temple Univ.), Tianya Zhao (Florida Internationan University), Steven Mackey (CSUS), Amirush Ramdas Javare (CSUS), and Mansi Patel (CSUS)

Related Publications (journal & magazine)

• G. Burduli and J. Wu, “Time management in a chess game through machine learning,” International Journal of Parallel, Emergent and Distributed Systems, to appear. DOI: 10.1080/17445760.2022.2088746.

• Y. Duan, N. Wang, and J. Wu, “Minimizing training time of distributed machine learning by reducing data communication,” IEEE Transactions on Network Science and Engineering, Vol. 8, No. 2, pp.1802-1814, Apr.-June, 2021.

• L. Wang, S. Mao, B. Wilamowski, and R. M. Nelms, “Pre-trained models for non-intrusive appliance load monitoring,” IEEE Transactions on Green Communications and Networking, to appear. DOI: 10.1109/TGCN.2021.3087702.

• Z. Bao, Y. Lin, S. Zhang, Z. Li, and S. Mao, “Threat of adversarial attacks on DL-based IoT device identification,” IEEE Internet of Things Journal, to appear. DOI: 10.1109/JIOT.2021.3120197.

• C. Yang, X. Wang, and S. Mao, “RFID based 3D human pose tracking: A subject generalization approach,” Elsevier/KeAi Digital Communications and Networks, to appear. DOI: 10.1016/j.dcan.2021.09.002.

• S. Duan, W. Yang, X. Wang, S. Mao, and Y. Zhang, “Temperature forecasting for stored grain: A deep spatio-temporal attention approach,” IEEE Internet of Things Journal, to appear. DOI: 10.1109/JIOT.2021.3078332.

• Y. Tu, Y. Lin, H. Zha, J. Zhang, Y. Wang, Guan Gui, Ali Kashif Bashir, and Shiwen Mao, “Large-scale real-world radio signal recognition with deep learning,” Chinese Journal of Aeronautics, to appear.

• C. Yang, X. Wang, and S. Mao, “RFID-Pose: Vision-aided 3D human pose estimation with RFID,” IEEE Transactions on Reliability, vol.70, no.3, pp.1218-1231, Sept. 2021. DOI: 10.1109/TR.2020.3030952.

• T. Zhang and S. Mao, “An introduction to the federated learning standard,” ACM GetMobile, vol.25, no.3, pp.18-22, Sept. 2021.

• S. Shen, T. Zhang, S. Mao, and G.-K. Chang, “DRL-based channel and latency aware radio resource allocation for 5G service-oriented RoF-mmWave RAN,” IEEE/OSA Journal of Lightwave Technology, vol.39, no.18, pp.5706-5714, Sept. 2021. DOI: 10.1109/JLT.2021.3093760.

• P. Tang, Y. Dong, Y. Chen, S. Mao, and S. Halgamuge, “QoE-aware traffic aggregation using preference logic for edge intelligence,” IEEE Transactions on Wireless Communications, vol.20, no.9, pp.6093-6106, Sept. 2021. DOI: 10.1109/TWC.2021.3071745.

• X. Wang, X. Wang, and S. Mao, “Indoor fingerprinting with bimodal CSI tensors: A deep residual sharing learning approach,” IEEE Internet of Things Journal, vol.8, no.6, pp.4498-4513, Mar. 2021.

Related Publications (conference)

• Z. Yu, J. Zhang, S. Mao, S. CG Periaswamy, and J. Patton, “RIRL: A recurrent imitation and reinforcement learning method for long-horizon robotic tasks,” in Proc. IEEE CCNC 2022, Las Vegas, NV, Jan. 2022.

• C. Yang, L. Wang, X. Wang, and S. Mao, “Meta-Pose: Environment-adaptive human skele-ton tracking with RFID,” in Proc. IEEE GLOBECOM 2021, Madrid, Spain, Dec. 2021.

• N. Tang, S. Mao, and R. M. Nelms, “Adversarial attacks to solar power forecast,” in Proc. IEEE GLOBECOM 2021, Madrid, Spain, Dec. 2021.

• H. Ambalkar, X. Wang, and S. Mao, “Adversarial human activity recognition using Wi-Fi CSI,” invited paper, in Proc. 2021 Annual IEEE Canadian Conference of Electrical and Computer Engineering (CCECE'21), Virtual Conference, Sept. 2021.

• Y. Duan and J. Wu, “Joint optimization of DNN partition and scheduling for mobile cloud computing,” in Proc. of the 50th International Conference on Parallel Processing (ICPP 2021), Lemont, IL, Aug., 2021, pp.1-10.

• Mohini Patil, Xuyu Wang, Xiangyu Wang, and Shiwen Mao, “Adversarial attacks on deep learning-based floor classification and indoor localization,” in Proc. 2001 ACM Workshop on Wireless Security and Machine Learning (WiseML'21), Abu Dhabi, United Arab Emirates, June-July 2021.

• Mansi Patel, Xuyu Wang, and Shiwen Mao, “Data augmentation with Conditional GAN for automatic modulation classification,” in Proc. 2020 ACM Workshop on Wireless Security and Machine Learning (WiseML 2020), in conjunction with the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2020), Linz, Austria, July 2020, pp.31-36.

We acknowledge the generous support from our sponsor

• National Science Foundation

This work is supported in part by the U.S. National Science Foundation (NSF) under Grant CNS-2107190. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation.