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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, 2025

Project Team

  • Shiwen Mao

  • Slobodan Vucetic

  • Jie Wu

  • Hongchang Gao

  • Xuyu Wang

  • Graduate Students: Ziqi Wang (Auburn), Junwei Ma (Auburn), Bernard Amoah (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. Shen, J. Zhang, X. Wang, and S. Mao, “Federated radio frequency fingerprint identification powered by unsupervised contrastive learning,” IEEE Transactions on Information Forensics & Security, to appear. DOI: 10.1109/TIFS.2024.3428367.

  • P. Liao, X. Wang, L. An, S. Mao, T. Zhao, and C. Yang, “TFSemantic: A time-frequency semantic GAN framework for imbalanced classification using radio signals,” ACM Transactions on Sensor Networks, Special Issue on Contact-free Smart Sensing in AIoT, vol.20, no.4, Article No. 79, pp.1-22, May 2024. DOI: DOI: 10.1145/3614096.

  • X. Wang, Z. Yu, S. Mao, J. Zhang, S. C.G. Periaswamy, and J. Patton, “MapLoc: LSTM-based location estimation using uncertainty radio maps,” IEEE Internet of Things Journal, vol.10, no.15, pp.13474-13488, Aug. 2023. DOI: 10.1109/JIOT.2023.3262619.

  • A. Chara, T. Zhao, X. Wang, and S. Mao, “Respiratory biofeedback using acoustic sensing with smartphones,” Elsevier Smart Health Journal, vol. 28, pp.100387, June 2023. DOI: 10.1016/j.smhl.2023.100387.

  • Y. Sun, S. Chen, Z. Wang, and S. Mao, “A joint learning and game-theoretic approach to multi-dimensional resource management in fog radio access networks,” IEEE Transactions on Vehicular Technology, vol.72, no.2, pp.2550-2563, Feb. 2023. DOI: 10.1109/TVT.2022.3214075.

  • C. Yang, X. Wang, and S. Mao, “TARF: Technology-agnostic RF sensing for human activity recognition,” IEEE Journal of Biomedical and Health Informatics, Special Issue on Cognitive Cyber-Physical Systems with AI based Solutions in Medical Informatics, vol.27, no.2, pp.636-647, Feb. 2023. DOI: 10.1109/JBHI.2022.3175912.

  • G. Burduli and J. Wu, “Time management in a chess game through machine learning,” International Journal of Parallel, Emergent and Distributed Systems, vol.38, pp.14-34, Jan. 2023. DOI: 10.1080/17445760.2022.2088746.

  • W. Zhang, W. Fan, G. Zhang, and S. Mao, “Learning-based joint service caching and load balancing for MEC blockchain networks,” IEEE/CIC China Communications, vol.20, no.1, pp.125-139, Jan. 2023. DOI: 10.23919/JCC.2023.01.011. (Featured Cover Article)

  • Y. Sun, J. Chen, Z. Wang, M. Peng, and S. Mao, “Enabling mobile virtual reality with Open 5G, fog computing and reinforcement learning,” IEEE Network, vol.36, no.6, pp.142-149, Nov./Dec. 2022. DOI: 10.1109/MNET.010.2100481.

  • L. Wang, S. Mao, and R. M. Nelms, “Transformer for non-intrusive load monitoring: Complexity reduction and transferability,” IEEE Internet of Things Journal, vol.9, no.19, pp.18987-18997, Oct. 2022. DOI: 10.1109/JIOT.2022.3163347.

  • X. Wang, X. Wang*, S. Mao, J. Zhang, S. C.G. Periaswamy, and J. Patton, “Adversarial deep learning for indoor localization with Channel State Information Tensors,” IEEE Internet of Things Journal, vol.9, no.19, pp.18182-18194, Oct. 2022. DOI: 10.1109/JIOT.2022.3155562

  • Z. Yu, J. Zhang, S. Mao, S. CG Periaswamy, and J. Patton, “Multi-state-space reason-ing reinforcement learning for long-horizon RFID-based robotic searching and planning tasks,” Journal of Communications and Information Networks, vol.7, no.3, pp.239-251, Sept. 2022. DOI: 10.23919/JCIN.2022.9906938.

  • 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, vol.35, no.9, pp.35-48, Sept. 2022. DOI: 10.1016/j.cja.2021.08.016.

  • C. Yang, X. Wang, and S. Mao, “RFID based 3D human pose tracking: A subject generalization approach,” Elsevier/KeAi Digital Communications and Networks, Special Issue on Edge computation and intelligence, vol.8, no.3, pp.278-288, Aug. 2022. DOI: 10.1016/j.dcan.2021.09.002.

  • C. Yang, X. Wang, and S. Mao, “RFID based 3D human pose tracking: A subject generalization approach,” Elsevier/KeAi Digital Communications and Networks, Special Issue on Edge computation and intelligence, vol.8, no.3, pp.278-288, Aug. 2022. DOI: 10.1016/j.dcan.2021.09.002. DOI: 10.1016/j.dcan.2021.09.002.

  • Z. Liu, L. Hou, K. Zheng, Q. Zhou, and S. Mao, “A DQN-based consensus mechanism for block-chain in IoT networks,” IEEE Internet of Things Journal, vol.9, no.14, pp.11962-11973, July 2022. DOI: 10.1109/JIOT.2021.3132420.

  • P. Hu, W. Yang, X. Wang, and S. Mao, “Contract-free wheat mildew detection using commodity WiFi,” Elsevier/KeAi International Journal of Cognitive Computing in Engineering, vol.3, no.1, pp.9-23, June 2022. DOI: 10.1016/j.ijcce.2022.01.001.

  • 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, vol.9, no.11, pp.9012-9024, June 2022. DOI: 10.1109/JIOT.2021.3120197. DOI: 10.1109/JIOT.2021.3120197.

  • 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, Special Issue on Communications and Computing for Green Industrial IoT and Smart Grids, vol.6, no.1, pp.56-68, Mar. 2022. DOI: 10.1109/TGCN.2021.3087702. DOI: 10.1109/TGCN.2021.3087702.

  • C. Fan, H. Liu, B. Li, C. Zhao, and S. Mao, “Adversarial game against hybrid attacks in UAV communications with partial information,” IEEE Transactions on Vehicular Technology, vol.71, no.2, pp.2204-2208, Feb. 2022. DOI: 10.1109/TVT.2021.3132934.

  • C. Yang, L. Wang, X. Wang, and S. Mao, “Environment adaptive RFID based 3D human pose tracking with a meta-learning approach,” IEEE Journal of Radio Frequency Identification, Special Issue on Wireless Motion Capture and Fine-Scale Localization, vol.6, no.1, pp.413-425, Jan. 2022. DOI: 10.1109/JRFID.2022.3140256.

  • 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, vol.8, no.23, pp.17147-17160, Dec. 2021. DOI: 10.1109/JIOT.2021.3078332.

  • 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.

  • T. Zhang and S. Mao, “An introduction to the federated learning standard,” ACM GetMobile, vol.25, no.3, pp.18-22, Sept. 2021. DOI: 10.1145/3511285.3511291.

  • 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.

  • 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.

  • 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)

  • T. Zhao, N. Wang, S. Mao, and X. Wang, “Few-shot learning and data augmentation for cross-domain UAV fingerprinting,” in ACM Mobicom 2024 Workshop on Machine Learning for NextG Networks (MLNextG24), Washington, D.C., Nov. 2024.

  • D. Li, X. Gong, S. Mao, and Y. Zhou, “Anarchic federated bilevel optimization,” in Proc. The 22nd International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt 2024), Seoul, South Korea, Oct. 2024

  • N. Wang, T. Zhao, S. Mao, and X. Wang, “AI generated wireless data for enhanced satellite device fingerprinting,” in Proc. IEEE ICC 2024 Workshop on Machine Learning and Deep Learning for Wireless Security (MLDLWiSec), Denver, CO, June 2024, pp.88-93.

  • Z. Wang and S. Mao, “AIGC for wireless data: The case of RFID-based human activity recognition,” in Proc. IEEE ICC 2024, Denver, CO, June 2024, pp.4060-4065.

  • Z. Wang and S. Mao, “Demo Abstract: AIGC for RFID-based human activity recognition,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024. (Best Demo Award of IEEE INFOCOM 2024)

  • T. Zhao, X. Wang, and S. Mao, “Cross-domain, scalable, and interpretable RF device fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.

  • Ziqi Wang* and Shiwen Mao, “AIGC for RF sensing: The case of RFID-based human activity recognition,” invited paper, in Proc. 2024 International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, Feb. 2024, pp.1092-1097.

  • W. O’Quinn* and S. Mao, “Technology agnostic anomaly detection using multi-modal sensory data in industrial IoT,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023, pp.4516-4521.

  • T. Zhao, X. Wang, and S. Mao, “Backdoor attacks against deep learning-based massive MIMO localization,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023, pp.2796-2801.

  • X. Chen, C. Wu, Z. Zhao, Y. Xiao, S. Mao, Y. Ji, “Hierarchical meta-reinforcement learning for resource-efficiency slicing in O-RAN,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023, pp.2729-2735.

  • A. Chara, T. Zhao, X. Wang, and S. Mao, “Respiratory biofeedback using acoustic sensing with smartphones,” presented at IEEE/ACM Chase 2023, Orlando, FL, June 2023.

  • X. Li, M. Chen, Z. Zhang, D. Liu, Y. Liu, and S. Mao, “Joint optimization of sensing and communications in vehicular networks: A graph neural network based approach,” in Proc. IEEE ICC 2023, Rome, Italy, May/June 2023, pp.5781-5786.

  • R. Boutahala, H. Fouchal, M. Ayaida, and S. Mao, “Light and efficient authentication mechanism for connected vehicles using unsupervised detection,” in Proc. IEEE ICC 2023, Rome, Italy, May/June 2023, pp.329-333.

  • Y. Zhao, X. Gong, and S. Mao, “Truthful incentive mechanism for federated learning with crowdsourced data labeling,” in Proc. IEEE INFOCOM 2023, Hoboken, NJ, May 2023.

  • Harshit Ambalkar, Tianya Zhao, Xuyu Wang*, and Shiwen Mao, “Adversarial Attack and Defense for WiFi-based Apnea Detection System,” in Proc. IEEE INFOCOM 2023 Posters, Hoboken, NJ, May 2023.

  • P. Hu, W. Yang, X. Wang, S. Mao, and E. Shen, “WiWm-EP: Wi-Fi CSI-based wheat moisture detection using equivalent permittivity,” in Proc. IEEE INFOCOM WKSHPS: DeepWireless 2023: Deep Learning for Wireless Communications, Sensing, and Security, Hoboken, NJ, May 2023.

  • S. Mackey, T. Zhao, X. Wang, and S. Mao, “Poster Abstract: Cross-domain adaptation for RF fingerprinting using prototypical networks,” in Proc. ACM SenSys 2022, Boston, MA, Nov. 2022, pp.812-813.

  • Z. Wang, C. Yang, and S. Mao, “Data augmentation for RFID-based 3D human pose tracking,” in Proc. IEEE VTC-Fall 2022, London, UK, Sept. 2022.

  • X. Wang, J. Zhang, S. Mao, S. CG Periaswamy, and J. Patton, “Locating multiple RFID tags with Swin Transformer-based RF hologram tensor filtering,” in Proc. IEEE VTC-Fall 2022, London, UK, Sept. 2022.

  • C. Yang, Z. Wang, and S. Mao, “RFPose-GAN: Data augmentation for RFID based 3D human pose tracking,” invited paper, Special Session on RFID for Healthcare and Wearable Applications, in Proc. The 12th IEEE International Conference on RFID Technology and Applications (IEEE RFID-TA 2022), Cagliari, Italy, Sept. 2022, pp.138-141.

  • S. Parmar, X. Wang, C. Yang, and S. Mao, “Voice fingerprinting for indoor localization with a single microphone array and deep learning,” in Proc. the Fourth ACM Wireless Security and Machine Learning Workshop (WiseML'22) in conjunction with ACM WiSec 2022, San Antonio, TX, May 2022, pp.21-26.

  • J. Ma, C. Yang, S. Mao, J. Zhang, S. Periaswamy, and J. Patton, “Human trajec-tory completion with transformers,” in Proc. IEEE ICC 2022, Seoul, South Korea, May 2022, pp.3346-3351.

  • U. Boora, X. Wang, and S. Mao, “Robust massive MIMO localization using neural ODE in adversarial environments,” in Proc. IEEE ICC 2022, Seoul, South Korea, May 2022, pp.4866-4871.

  • C. Yang, L. Wang, X. Wang, and S. Mao, “Demo Abstract: Environment-adaptive 3D human pose tracking with RFID,” in Proc. IEEE INFOCOM 2022, Virtual Conference, May 2022. (Best Demo Award of IEEE INFOCOM'22)

  • C. Yang, X. Wang, and S. Mao, “Demo Abstract: Technology-agnostic approach to RF based human activity recognition,” in Proc. IEEE INFOCOM 2022, Virtual Conference, May 2022.

  • 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.

Related Publications (others)

  • T. Zhao, X. Wang, S. Mao, S. Vucetic, and J. Wu, “Adversarial machine learning for wireless localization,” Chapter 8 in Network Security Empowered by Artificial Intelligence, Y. Chen, J. Wu, P. L. Yu, and C. Wang (Editors), Berlin, Germany: Springer Nature, 2024. DOI: 10.1007/978-3-031-53510-9_8.

Broadening Participation in Computing Workshops

  • The BPC Workshop at Florida International University, summer 2024 (13 K-12 teachers and 8 students, 7 talks, one wireless spectrum project)

  • The BPC Workshop at Temple University, summer 2023 (10 students, 4 talks, one wireless spectrum project)

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.

 Department of Electrical and Computer Engineering | Auburn University | Auburn, Alabama 36849-5201 | (334) 844-1845 | smao@auburn.edu
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