Cellular networks have become one of the critical infrastructures for society, with users expecting reliable connectivity and performance. Behind the scenes, operating these networks require updating hundreds of parameters at time scales ranging from hours to weeks, which is extremely costly and inefficient for engineers at the network operations center. Further, when failures or inefficient performance occurs, detecting and isolating the root causes is again a challenging, but critical task. This proposal focuses on efficiently operating these networks and developing tools to detect anomalies, both using machine learning techniques. The goal of this proposal is to develop algorithms based on online learning, Bayesian optimization and deep learning for parameter tuning and anomaly detection. Building on partnerships with major cellular providers and the use of real data-traces and testbeds, our algorithms and approaches will have real-world impact.
Sanjay Shakkottai (UT Austin)
Ness Shroff (Ohio State University)
G. Quan, A. Eryilmaz, and N. B. Shroff, “Minimizing Edge Caching Service Costs Through Regret-Optimal Online Learning,” to appear in the IEEE/ACM Transactions on Networking, 2024.
A. Kar, R. Singh, F. Liu, X. Liu, and N. B. Shroff, “Linear Bandits with Side Observations on Networks,” to appear in the IEEE/ACM Transactions on Networking, 2024.
P. Ju, A. Ghosh, and N. B. Shroff, "Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning", 12th International Conference on Learning Representations (ICLR), 2024.
Y. Li, P. Ju, and N. B. Shroff, "Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping", 12th International Conference on Learning Representations (ICLR), 2024.
A. Ghosh, X. Zhou, and N. B. Shroff, "Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL", Artificial Intelligence and Statistics (AISTATS) 2024.
“Bandits with mean bounds,” N. Sharma, S. Basu, K. Shanmugam, and S. Shakkottai. Accepted for publication in Transactions on Machine Learning Research, 2024.
"Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes", N. Sharma, R. Sen, S. Basu, K. Shanmugam, and S. Shakkottai.. ACM Trans. Model. Perform. Eval. Comput. Syst. 9, 3, Article 12, September 2024.
"Predicting the Performance of Cellular Networks: A Latent-resilient Approach", K. Patel, C. Ge, A. Mahimkar, S. Shakkottai, and Y. Shaqalle. To appear in the Proceedings of the 30th Annual International Conference on Mobile Computing and Networking (ACM MobiCom; poster paper), Washington DC, November 2024.
"CIPAT: Latent-resilient toolkit for performance impact prediction due to configuration tuning", K. Patel, C. Ge, A. Mahimkar, S. Shakkottai, and Y. Shaqalle. To appear in the Proceedings of the 1st ACM Workshop on Machine Learning for NextG Networks (ACM MLNextG), Washington DC, November 2024.
“Provably Efficient Model-Free Algorithms for Non-stationary CMDPs,” H. Wei, A. Ghosh, N. B. Shroff, L. Ying, and X. Zhou, AISTATS, Valencia, Spain, Apr. 2023.
“Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation,” A. Ghosh, X. Zhou, and N. B. Shroff, ICLR, Kigali, Rwanda, May 2023.
“Learning in Constrained Markov Decision Processes,” R. Singh, A. Gupta, and N. B. Shroff, IEEE Trans on Control of Network Systems, vol. 10, no. 1, pp. 441-453, Mar. 2023.
"Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits", R. Chawla, D. Vial, S. Shakkottai and R. Srikant. Proceedings of the 40th International Conference on Machine Learning (ICML), Honolulu, HI July 2023.
“Near-optimal Adversarial Reinforcement Learning with Switching Costs,” M. Shi, Y. Liang, and N. B. Shroff, ICLR, Kigali, Rwanda, May 2023.
“Robust Multi-Agent Bandits Over Undirected Graphs”, D. Vial, S. Shakkottai and R. Srikant. Proceedings of the ACM Sigmetrics Conference on Measurement and Modeling of Computer Systems (Sigmetrics), Orlando, FL, June 2023.
“Provably Efficient Model-Free Constrained RL with Linear Function Approximation,” A. Ghosh, X. Zhou, and N. B. Shroff, NeurIPS, New Orleans, Louisiana, USA, Nov. 2022.
“Interference Constrained Beam Alignment for Time-Varying Channels via Kernelized Bandits,” Y. Deng, X. Zhou, A. Ghosh, A. Gupta, and N. B. Shroff, IEEE WiOpt’22, Turin, Italy, Sep. 2022.
“Weighted Gaussian Process Bandits for Non-stationary Environments,” Y. Deng, X. Zhou, B. Kim, A. Tewari, A. Gupta, and N. B. Shroff, AISTATS, March 2022.
“Asymptotically-Optimal Gaussian Bandits with Side Observations”, A. Atsidakou, O. Papadigenopoulos, C. Caramanis, S. Sanghavi and S. Shakkottai. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July 2022.
"Bandit Learning-based Online User Clustering and Selection for Cellular Networks” I. Tariq, K. Patel, T. Novlan, S. Akoum, M. Majmundar, G. de Veciana and S. Shakkottai, IEEE WiOpt’22, Turin, Italy, Sep. 2022.
Shroff co-chaired the Future Directions Workshop on Wireless Communications: XG and Beyond
Shakkottai conducted a short course on causal inference at Georgia Tech
Several presentations to industry partners on research outcomes through the 6G@UT program and individual meetings with companies by both PIs
Collaborations with AT&T Labs researchers on creating tools for cellular network management