Wireless technology has evolved at a remarkable rate, with data rates increasing by four orders of magnitude over the past twenty years. New wireless communication technologies such as millimeter-wave (mmWave), and full-duplex, along with the emergence of edge-cloud computing will enable additional improvements, ultimately exceeding one giga-bits-per-second data rates and sub-millisecond delays. This project will develop network control algorithms that leverage these emerging wireless technologies to enable robust and resilient next generation of wireless networks and usher in novel applications, such as smart cities, connected vehicles, virtual reality, telemedicine, and advanced manufacturing.
The goal of this project is to develop autonomous network control algorithms that will enable robust and resilient distributed computing wireless networks through a combination of physical layer technology, edge-cloud computing, and intelligent network control. In particular, the project team will develop an autonomous network control framework that can jointly address resiliency in the physical and application layers by dynamically adapting the next-generation wireless communication systems to the time-varying conditions of the environment and, at the same time, intelligently allocating the available communication and computation resources in order to meet the performance requirements of time-sensitive edge-cloud services. The network control framework will make resource allocation decisions dynamically and in an autonomous manner, without prior knowledge of traffic statistics and network conditions, making it resilient to changes in the network and environment. The project team will prototype, experiment, and evaluate the network control framework and its application to edge cloud systems using the COSMOS testbed. The project will also include impactful outreach and education activities which, among other things, will engage K–12 teachers and students from NYC and the neighboring Harlem community in wireless networking, thereby aiming to broaden participation in Engineering and STEM.
Participants:
Eytan Modiano (PI)
Gil Zussman (Co-PI)
Kadota, Igor (Postdoc)
Kim, Jip (Postdoc)
Adhikari, Abhi (Graduate Student)
Fu, Xinzhe (Graduate Student)
Ghasemi, Mahshid (Graduate Student)
Gordon, Trevor (Graduate Student)
Jolly, Aditya (Graduate Student)
Kohli, Manav (Graduate Student)
Ojha, Shivam (Graduate Student)
Mehta, Aahan (High School Student)
Publications:
M. Ghasemi, S. Kleisarchaki, T. Calmant, J. Lu, S. Ojha, Z. Kostic, L. Gürgen, G. Zussman, and J. Ghaderi, “Demo: Real-time multi-camera analytics for traffic information extraction and visualization,” in Proc. IEEE PerCom’23, 2023. [download]
P. Netalkar, A. Zahabee, C. E. Caicedo Bastidas, I. Kadota, D. Stojadinovic, G. Zussman, I. Seskar, and D. Raychaudhuri, “Large-scale dynamic spectrum access with IEEE 1900.5.2 spectrum consumption models,” in Proc. IEEE WCNC’23, 2023. [download]
Y.-K. Huang, Z. Wang, E. Ip, Z. Qi, G. Zussman, D. Kilper, K. Asahi, H. Kageshima, Y. Aono, and T. Chen, “Field trial of coexistence and simultaneous switching of real-time fiber sensing and 400GbE supporting DCI and 5G mobile services,” in Proc. IEEE/OPTICA Optical Fiber Communication Conference (OFC’23), W3H.4, 2023. [download]
D. Chizhik, J. Du, M. Kohli, A. Adhikari, R. Feick, R. Valenzuela, and G. Zussman, “Accurate urban path loss models including diffuse scatter,” in Proc. 17th European Conf. on Antennas and Propagation (EuCAP’23), 2023. [download]
T. Gordon, “Towards real-world dynamic spectrum access using deep reinforcement learning,” M.S. Thesis, Columbia University, 2023.
A. Jolly, “Real-time adaptive wideband full duplex radios,” M.S. Thesis, Columbia University, 2023.
Z. Kostic, A. Angus, Z. Yang, Z. Duan, I. Seskar, G. Zussman, and D. Raychaudhuri, “Smart city intersections: Intelligence nodes for future metropolises,” IEEE Computer, Special Issue on Smart and Circular Cities, vol. 55, pp. 74–85, Dec. 2022. [download]
I. Kadota, D. Jacoby, H. Messer, G. Zussman, and J. Ostrometzky, “Switching in the Rain: Predictive wireless x-haul network reconfiguration,” Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS/Proc. ACM SIGMETRICS’23), vol. 6, no. 3, p. 55:1–26, Dec. 2022. [download][presentation]
M. Kohli, A. Adhikari, G. Avci, S. Brent, J. Moser, S. Hossain, A. Dash, I. Kadota, R. Feick, D. Chizhik, J. Du, R. Valenzuela, and G. Zussman, “Outdoor-to-indoor 28GHz wireless measurements in Manhattan: path loss, location impacts, and 90% coverage,” in Proc. ACM MOBIHOC’22, 2022. [download][dataset (NIST)][dataset (local)][presentation]
A. Angus, Z. Duan, G. Zussman, and Z. Kostic, “Real-time video anonymization in smart city intersections,” in Proc. IEEE MASS’22 (invited), 2022. [download][presentations][dataset][video]
Xinzhe Fu and Eytan Modiano, "Joint Learning and Control in Stochastic Queueing Networks with Unknown Utilities," ACM Sigmetrics, 2023.
M. Ghasemi, S. Kleisarchaki, T. Calmant, L. Gürgen, J. Ghaderi, Z. Kostic, and G. Zussman, “Demo: Real-time camera analytics for enhancing traffic intersection safety,” in Proc. ACM MobiSys’22, 2022. [download]