Jia (Kevin) Liu,

Major Activities

1. Efficient Scheduling Design for Massive MIMO Cellular Networks

   In this thrust, we focus on multi-user scheduling problems at the link layer in cellular networks, where the base stations are equipped with Massive MIMO systems. In Massive MIMO cellular networks, the main challenge of the multi-user scheduling problem stems from the imperfect or incomplete channel state information (CSI), which is critical for all opportunistic scheduling designs. In this project, our goal is to develop efficient scheduling policies that can adapt to the CSI availability and offer: (i) provable throughput-optimality, (ii) asymptotic rate-function delay optimality, and (iii) low complexity.

2. Optimal Routing and Congestion Control for Massive MIMO Multi-Hop Networks

   In this research thrust, we will concentrate on the joint multi-hop routing and congestion control optimization problems in Massive MIMO multi-hop networks. This research is particularly relevant for wireless back-haul networks, where each link employs Massive MIMO. Here, our goal will be to ensure that not only are the end-to-end session rates utility-optimal under our proposed joint multi-hop routing and congestion control algorithm with imperfect and/or incomplete CSI, but also that all routing and congestion decision variables converge to the optimal solution with a fast speed.

3. Energy Analytics for Massive MIMO Wireless Networks

   In the aforementioned thrusts, we have focused on the throughput and delay performances of our proposed control and optimization algorithms. In this thrust, we are concerned with the energy expenditure of Massive MIMO wireless networks. The energy expenditure performance is important because the energy consumption of cellular base stations has become a growing concern in recent years and there is a compelling need for wireless networks to go green. Unlike conventional MIMO power minimization problems, in this project, we are interested in Massive MIMO base stations equipped with emerging green technologies (e.g., renewable energy sources and storage). The time-varying nature of energy costs, traffic-load, and renewable energy-supply would significantly complicate the power management of Massive MIMO networks.

Education Opportunities

At ISU and OSU, several Ph.D. students have been supported on this project. The students on this project have been trained on a variety of cross-disciplinary topics, which include wireless networking, wireless communications, 5G technologies, machine learning, artificial intelligence, etc. Such an academic and engineering training helps prepare the student’s future career in academic and industry.

Outreach Activities

1. 2016

   The research results have been published in highly selective peer-reviewed conferences (IEEE INFOCOM, ACM SIGMETRICS, and ACM MOBIHOC). PI Liu has also given talks at Air Force Research Labs Information Institute, 2016 Information Theory and Applications Workshop at UC San Diego, University of Delaware, and Rensselaer Polytechnic Instittue, which highlighted the key approaches and contributions of the project.

2. 2017

   The research results have been published in highly selective peer-reviewed conferences (IEEE INFOCOM, ACM SIGMETRICS, ACM MOBIHOC, IFIP Networking, IEEE/IFIP WiOpt). PI Liu has also given talks at Air Force Research Labs Information Institute, 2017 Information Theory and Applications Workshop at UC San Diego, University of Delaware, and Rensselaer Polytechnic Institute, University of Kansas, North Carolina State University, University of Tennessee, University of North Carolina at Charlotte, University of Arizona, Virginia Commonwealth University, Wichita State University, Tulane University, Mississippi State University, Iowa State University, The Ohio State University, Northwestern University, Purdue University, which highlighted the key approaches and contributions of the project.

3. 2018

   Research findings have been published in highly selective peer-reviewed conferences (IEEE INFOCOM, ACM SIGMETRICS, ACM MOBIHOC, IFIP Networking, IEEE/IFIP WiOpt, ACM e-Energy). In this reporting period, PI Liu has given talks at 2018 Information Theory and Applications Workshop at UC San Diego, Northwestern University, Purdue University, University of Victoria, Sun Yat-Sen University, and South China University of Technology, which highlighted the key approaches and contributions of the project. PI Liu was also invited as a keynote speaker for the 2018 International Workshop on Fog and Edge Computing for Intelligent IoT Applications (EFC-IoT) and the 2018 International Workshop on Resource Allocation, Cooperation and Competition in Wireless Networks (RAWNET), both in Shanghai, China. PI Liu was also invited to give a tutorial at the 2018 Midwest Big Data Summer School at Iowa State University.

4. 2019

   Research findings have been published in highly selective peer-reviewed conferences (IEEE INFOCOM, IEEE/IFIP WiOpt, AISTATS, NeurIPS, ACM ACSAC). In this reporting period, PI Liu has given talks at 2019 Information Theory and Applications Workshop at UC San Diego, Sun Yat-Sen University, Tsinghua University, Shanghai Jiao Tong University, and ShanghaiTech University.

5. 2020

   Research findings have been published in highly selective peer-reviewed conferences in networking, communications, and machine learning (IEEE JSAC, IEEE TMC, IEEE TNSE, NeurIPS, ICML, etc.). In this reporting period, PI Liu has given talks at 2020 Information Theory and Applications Workshop at UC San Diego, The Ohio State University, University of Michigan, Chinese University of Hong Kong at Shenzhen, and Sun Yat-Sen University, Tech Talks at Google, TADS Lunch-and-Learn Seminar Series at Iowa State University, CAM-MDL Joint Seminar Series at Iowa State University, which highlighted the key approaches and contributions of the project.

6. 2021

   Research findings have been published in highly selective peer-reviewed conferences in networking, communications, and machine learning (IEEE JSAC, IEEE TMC, IEEE TNSE, NeurIPS, ICML, etc.). In this reporting period, PI Liu has given talks at the 3rd Buffalo Day and Wireless Internet-of-Things,Tech Talks at Amazon, Texas Tech University, which highlighted the key approaches and contributions of the project.

Publications

1. J. Liu, A. Eryilmaz, N. B. Shroff, and E. S. Bentley, "Heavy-Ball: A New Approach for Taming Delay and Convergence in Wireless Network Optimization," in Proc. IEEE INFOCOM, San Francisco, CA, Apr. 2016 (Best Paper Award, acceptance rate: 17%).


2. J. Liu, A. Eryilmaz, N. B. Shroff, and E. S. Bentley, "Understanding the Impact of Limited Channel State Information on Massive MIMO Network Performances," in Proc. ACM MobiHoc, Paderborn, Germany, July 2016 (acceptance rate: 17%).


3. J. Liu, "Achieving Low-Delay and Fast-Convergence in Stochastic Network Optimization: A Nesterovian Approach," in Proc. ACM Sigmetrics, Antibes Juan-les-Pins, Jun. 2016 (acceptance rate: 13%).


4. J. Liu, N. B. Shroff, C. H. Xia, H. D. Sherali, "Joint Congestion Control and Routing Optimization: An Efficient Second-Order Distributed Approach,'' IEEE/ACM Transactions on Networking, vol. 24, no. 3, pp. 1404-1420, Jun. 2016.


5. J. Liu, A. Eryilmaz, N. B. Shroff, and E. Bentley, "Understanding the Impacts of Limited Channel State Information on Massive MIMO Cellular Network Optimization," IEEE Journal on Selected Areas in Communications (JSAC), vol. 35, no. 8, pp. 1715-1727, Aug. 2017.


6. J. Liu and E. Bentley, "Hybrid-Beamforming-Based Milli-meter Wave Cellular Network Optimization," in IEEE/IFIP WiOpt, Paris, May 2017.


7. K. Zheng, X. Wang, and J. Liu, "DISCO: Distributed Traffic Flow Consolidation for Power Efficient Data Center Network," in Proc. IFIP Networking, Stockholm, Sweden, June 2017.


8. H. Shi, J. Liu, and Q. Chen, "HVAC Precooling Optimization for Green Buildings: An RC-Network Approach," in Proc. ACM e-Energy, Karlsruhe, Germany, June 2018.


9. B. Li, B. Ji, and J. Liu, "Efficient and Low-Overhead Uplink Scheduling for Large-Scale Wireless Internet-of-Things," in Proc. IEEE/IFIP WiOpt, Shanghai, China, May 2018.


10. J. Liu, "High-Order Momentum: Improving Latency and Convergence for Wireless Network Optimization," in Proc. IEEE INFOCOM, Honolulu, HI, Apr. 2018


11. M. Fang, G. Yang, N. Z. Gong, and J. Liu, "Poisoning Attacks to Graph-Based Recommender Systems," in Proc. ACM Annual Computer Security Applications Conference (ACM ACSAC), San Juan, Puerto Rico, Dec. 2018.


12. B. Li, B. Ji, and J. Liu, "Efficient and Low-Overhead Uplink Scheduling for Large-Scale Wireless Internet-of-Things," IEEE Transactions on Mobile Computing, Dec. 2019.


13. F. Li, J. Liu, and B. Ji, "Combinatorial Sleeping Bandits with Fairness Constraints,” IEEE Transactions on Network Science and Engineering, Dec. 2019 (Best Paper Award, acceptance rate: 17%)


14. H. Shi, J. Liu, and Q. Chen, "HVAC Precooling Optimization for Green Buildings: An RC-Network Approach," IEEE Transactions on Sustainable Computing, Dec. 2019.


15. W. Ren, J. Liu, and N. B. Shroff, "On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons" in Proc. NeurIPS, Vancouver, Canada, Dec. 2019


16. H. Yang, X. Zhang, M. Fang, and J. Liu, "Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach," in Proc. IEEE CDC, Nice, France, Dec. 2019.


17. B. Li and J. Liu, "Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?" in Proc. IEEE/IFIP WiOpt, Avignon, France, Jun. 2019.


18. W. Ren, J. Liu, and N. B. Shroff, "Exploring k out of Top ρ Fraction of Arms in Stochastic Bandits," in Proc. AISTATS, Naha, Okinawa, Japan, Apr. 2019.


19. X. Zhang, J. Liu, Z. Zhu, and E. Bentley, "Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks," in Proc. IEEE INFOCOM, Paris, France, Apr. 2019


20. F. Li, J. Liu, and B. Ji, "Combinatorial Sleeping Bandits with Fairness Constraints," in Proc. IEEE INFOCOM, Paris, France, Apr. 2019.


21. Wenbo Ren, Jia Liu, and Ness B. Shroff, "On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons," in Proc. NeurIPS, Vancouver, Canada, Dec. 2019 (acceptance rate: 21%).


22. Zhida Qin, Xiaoying Gan, Jia Liu, Hongqiu Wu, Haimin Jin, and Luoyi Fu, "Exploring Best Arm with Top Reward-Cost Ratio in Stochastic Bandits," in Proc. IEEE INFOCOM, Toronto, Canada, Jul. 2020 (acceptance rate: 19.8%).


23. Xin Zhang, Jia Liu, Zhengyuan Zhu, and Elizabeth Bentley, "Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors," in Proc. IEEE INFOCOM, Toronto, Canada, Jul. 2020 (acceptance rate: 19.8%).


24. Xuxi Yang, Lisen Deng, Jia Liu, Peng Wei, and Husheng Li, "Multi-Agent Autonomous Operations in Urban Air Mobility with Communication Constraints," in Proc. AIAA SciTech, Orlando, Florida, Jan. 2020.


25. Xin Zhang, Minghong Fang, Jia Liu, and Zhengyuan Zhu, "Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach," in Proc. ACM MobiHoc, Shanghai, China, Oct. 2020 (acceptance rate: 15%).


26. Haibo Yang, Xin Zhang, Minghong Fang, and Jia Liu, "Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization," in Proc. IEEE SPAWC, Special Session on Distributed Signal Processing for Coding and Communications, Atlanta, GA, May 2020 (Invited Paper).


27. Wenbo Ren, Jia Liu, and Ness B. Shroff, "The Sample Complexity of Best-K Items Selection from Pairwise Comparisons," in Proc. ICML, Vienna, Austria, July 2020 (acceptance rate: 21.8%).


28. Xin Zhang, Jia Liu, and Zhengyuan Zhu, "Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning," in Proc. IEEE CDC, Jeju Island, Korea, December 2020.


29. Peizhong Ju, Xiaojun Lin, Jia Liu, "Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree," in Proc. NeurIPS, Vancouver, Canada, December 2020 (Spotlight Presentation, spotlight rate: 3% acceptance rate: 20%).


30. Menglu Yu, Chuan Wu, Bo Ji, and Jia Liu, "A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource Scheduling," in Proc. IEEE INFOCOM, Virtual Event, May 2021 (acceptance rate: 19.9%).


31. Xin Zhang, Jia Liu, Zhengyuan Zhu, and Elizabeth S. Bentley, "Low Sample and Communication Complexities in Decentralized Learning: A Triple Hybrid Approach," in Proc. IEEE INFOCOM, Virtual Event, May 2021 (acceptance rate: 19.9%).


32. Xiaoyu Cao*, Minghong Fang*, Jia Liu, and Neil Gong, "FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping," in Proc. NDSS, Virtual Event, Feb. 2021 (*co-primary authors, acceptance rate: 16%).


33. Wenbo Ren, Jia Liu, and Ness Shroff, "On Logarithmic Regret for Bandits with Knapsacks," in Proc. IEEE CISS, Special Session on Online Optimization and Learning, Virtual Event, March 2021 (Invited Paper).


34. Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian and Jia Liu, "Data Poisoning Attacks and Defenses to Crowdsourcing Systems," in Proc. ACM WWW (TheWebConf), Virtual Event, Apr. 2021 (acceptance rate: 20.6%).


35. Haibo Yang, Minghong Fang, and Jia Liu, "Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning," in Proc. ICLR, Virtual Event, May 2021 (acceptance rate: 28.6%).


36. Xin Zhang, Jia Liu, Zhengyuan Zhu, and Elizabeth S. Bentley, "GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning," in Proc. ACM MobiHoc, Shanghai, China, Jul. 2021 (acceptance rate: 20.1%).


37. Tianchen Zhou, Jia Liu, Chaosheng Dong, and Jingyuan Deng, "Incentivized Bandit Learning with Self-Reinforcing User Preferences," in Proc. ICML, Virtual Event, Jul. 2021 (acceptance rate: 20.4%).


38. Fengjiao Li, Jia Liu, and Bo Ji, "Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach," in Proc. IEEE MASS, Virtual Event, Oct. 2021 (acceptance rate: 28.3%).


39. Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat and Pramod K. Varshney,"Achieving Optimal Sample and Communication Complexities for Non-IID Federated Learning," in Proc. ICML Workshop on Federated Learning for User Privacy and Data Confidentiality (FL-ICML'21), Virtual Event, Jul. 2021.


40. Haibo Yang, Jia Liu, and Elizabeth S. Bentley,"CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning," in Proc. IEEE/IFIP WiOpt, Virtual Event, Oct. 2021.


41. Hongwei Zhang, Yong Guan, Ahmed Kamal, Daji Qiao, Mai Zheng, Anish Arora, Ozdal Boyraz, Brian Cox, Thomas Daniels, Matthew Darr, Doug Jacobson, Ashfaq Khokhar, Sang Kim, James Koltes, Jia Liu, Mike Luby, Larysa Nadolny, Joshua Peschel, Patrick Schnable, Anuj Sharma, Arun Somani, and Lie Tang,"ARA: A Wireless Living Lab Vision for Smart and Connected Rural Communities," in Proc. Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (ACM WiNTECH), Virtual Event, Oct. 2021.


42. Wenbo Ren, Jia Liu, and Ness B. Shroff, "Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons," in Proc. NeurIPS, Virtual Event, Dec. 2021 (acceptance rate: 26%).


43. Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, and Pramod Varshney, "STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning," in Proc. NeurIPS, Virtual Event, Dec. 2021 (acceptance rate: 26%).


44. Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, and Songtao Lu, "Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning," in Proc. NeurIPS, Virtual Event, Dec. 2021 (acceptance rate: 26%).


45. Bin Li* and Jia Liu*, "Achieving Information Freshness with Selfish and Rational Users in Mobile Crowd-Learning,'' IEEE Journal on Selected Areas in Communications (JSAC), vol. 39, no. 5, pp. 1266-1276, May 2021 (*Co-primary authors).


46. Bin Li, Jia Liu, and Bo Ji, "Low-Overhead Wireless Uplink Scheduling for Large-Scale Internet-of-Things,'' IEEE Transactions on Mobile Computing (TMC), vol. 20, no. 2, pp. 577-587, Feb. 2021.