NSF Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT) Project

Collaborative Research: SWIFT: Decentralized Intelligent Spectrum Sharing in UAV Networks (DISH-uNET) via Hardware-software Co-design (10/01/2022 to 09/30/2025)

Intelligent unmanned aerial vehicles (UAVs, or “drones”) are attracting the interest of the net- working community as a “tool” to provide new capabilities, to extend the infrastructure of wireless networks and to make it more flexible and resilient. Thanks to their unique characteristics such as fast deployment, high mobility, processing capabilities, and reduced size, UAVs are an enabling technology for numerous future wireless applications. Among these, increasing network coverage, providing advanced network services such as location-aware content delivery, and massive MIMO transmissions are notable. UAV-aided wireless networks will enable present and future Internet of Things (IoT) and 5G applications, and be a driver for new military and civilian applications spanning battlefield inspection, border control and aerial surveillance, precision agriculture, envi- ronmental monitoring, transportation and delivery of goods.

Currently, despite the great potential of UAV networks, the design of such networks faces great challenges due to the high mobility of UAVs, the limited power constraint of UAVs, and the non- stationary environment. In addition, these networks often demand high data-rate to meet the needs for transmitting real-time video and images. Conventional designs, typically centralized and operate at sub-6 GHz band, can neither meet the high data-rate demands nor quickly adapt to the fast time-varying environment experienced by the flying UAVs. Hence, the resulting UAV networks will be vulnerable to link failures or other disruptive events such as adversarial attacks on the central controller. Hence, it is critical to develop new tools and methodology to make such networks resilient. This motivates us to develop novel approaches for decentralized intelligent spectrum sharing in mmWave UAV networks (DISH-uNET). Once successfully executed, the proposed project will have a significant engineering and societal impact and substantially advance the state-of-the-art in the design of intelligent UAV networks with strong resiliency attributes.

Project Page on NSF Website: here.

PIs:

• University of Utah: Drs. Mingyue Ji, Rong-Rong Chen.

• University at Buffalo: Dr. Zhangyu Guan.

• University of Maryland, College Park: Dr. Cunxi Yu.

News and Collaborative Activities

• We obtained two supplementary awards to implement our algorithms and conduct experiments using POWDER and AERPAW.

• All the PIs met at 5th Buffalo Day for 5G and Wireless Internet of Things. In particular, PIs Zhangyu Guan and Mingyue Ji are co-organizers of this workshop. All PIs presented their results of this project. Some photos can be found here.

  • Dr. Rong-Rong Chen presented ‘‘Learning-based Beamforming and Phase-Shift Design for RIS-aided Networks".

  • Dr. Cunxi Yu presented ‘‘Machine Learning Assisted Electronic Design Automation Systems for Edge Computing".

  • Dr. Mingyue Ji presented ‘‘ A New Multi-Agent Deep Reinforcement Learning-enabled Distributed Power Allocation Scheme for mmWave and sub-6GHz Cellular Networks".

Research Topics and Publications:

• Energy-efficient Systolic Accelerator for Simultaneous Real-time Signal Processing and ML

  • RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
    J. Yin, Y. Li, D. Robinson, and C. Yu, in ACM/IEEE Design Automation Conference (DAC’23).

  • Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks
    N. Wu, Y. Li, S. Dai, C. Hao, C. Yu and Y. Xie, in ACM/IEEE Design Automation Conference (DAC’23).

• Transceiver Design for High Mobility UAV Communication

  • Channel Estimation and Turbo Equalization for Coded OTFS and OFDM: A Comparison
    X, Huang, A. Farhang and R.-R. Chen, submitted to IEEE Wireless Communication Letter, 2023.

• Bridging Lyapunov Optimization Framework, Game Theory, and Reinforcement Learning in Decentralized Spectrum Sharing

  • Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning
    X. Zhang, A. Bhuyan, S. K. Kasera and M. Ji, IEEE ICIT 2023 (invited paper).

  • A Novel Multi-Agent Deep Reinforcement Learning-enabled Distributed Power Allocation Scheme for mmWave Cellular Networks
    X. Zhang, A. Bhuyan, S. K. Kasera and M. Ji, IEEE ICC 2023 Workshop - Data Driven Intelligence for Networks and Systems (DDINS).

• Mobility-Resilient mmWave Beam Learning

  • A Mobility-Resilient Spectrum Sharing Framework for Operating Wireless UAVs in the 6 GHz Band
    J. Hu, S. K. Moorthy, A. Harindranath, Zhaoxi Zhang, Zhiyuan Zhao, N. Mastronarde, E. S. Bentley, S. Pudlewski and Z. Guan, in IEEE/ACM Transactions on Networking, 2023.

  • Swarm UAV Networking With Collaborative Beamforming and Automated ESN Learning in the Presence of Unknown Blockages
    S. K. Moorthy, N. Mastronarde, S. Pudlewski, E. S. Bentley and Z. Guan, Elsevier Journal of Computer Networks, vol. 231, July 2023.

  • OSWireless: Hiding the Specification Complexity for Zero-Touch Software-Defined Wireless Networks
    S. K. Moorthy, N. Mastronarde, E. S. Bentley, M. Medley and Z. Guan, Elsevier Journal of Computer Networks, accepted, Oct. 2023.

  • Digital Twin-Enabled Domain Adaptation for Zero-Touch UAV Networks: Survey and Challenges
    M. McManus, Y. Cui, J. Zhang, J. Hu, S. K. Moorthy, Z. Guan, N. Mastronarde, E. S. Bentley and M. Medley, Elsevier Journal of Computer Networks, vol. 236, Nov. 2023.

  • Demo: Scaling Out srsRAN Through Interfacing Wirelessly srsENB With srsEPC
    N. Mishra, Y. V. Iyengar, A. C. Raikar, N. Thomas, S. K. Moorthy, J. Hu, Z. Zhao, N. Mastronarde, E. S. Bentley, M. Medley and Z. Guan, in IEEE INFOCOM, 2023.

Outreach Activities and Broader Impacts

• TBD.