Welcome to my personal website! I’m Qing An, a 5th year PhD student at Rice University advised by Prof. Rahman Doost-Mohammady and Prof. Ashutosh Sabharwal. I am broadly interested in Wireless System. My recent research focuses on massive MIMO, virtual/open RAN and AI/ML in wireless. Previously, I got my Bachelor degree in Electrical and Computer Engineering from Dalian University of Technology, China and Master degree in Electrical and Computer Engineering from University of Michigan, Ann Arbor. Recent News!

Recent News!

• October 2024: Our paper “DRAGON: A DRL-based MIMO Layer and MCS Adapter in Open RAN 5G Networks” is accepted to Open-AI RAN Workshop @ ACM Mobicom 2024. This work is done during my internship @ Mavenir System Inc.. See you in Washington, DC!
• September 2024: Our paper “Helix: A RAN Slicing Based Scheduling Framework for Massive MIMO Networks” is accepted to ACM CoNEXT’24. See you in LA!
• May 2024: Excited to announce that I will be joining Mavenir System Inc. (Dallas, TX) as a summer intern to work on AI/ML in Open RAN.
• December 2023: Successfully defended my MS thesis, “Machine Learning-Based Resource Scheduling and Link Adaptation in Massive MIMO Networks”.
• October 2023: Our paper “ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO” is accepted to IEEE Asilomar’23. See you in Pacific Grove!
• June 2023: Our paper “A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks” is accepted to IEEE TMLCN!
• January 2023: Our mini-project “ML-based adaptive modulation selection for MIMO networks” won the first prize in POWDER-RENEW Mobile and Wireless Week!
• August 2021: I joined Rice Wireless Group and started working with Prof. Rahman and Prof. Ashu!

Publications

DRAGON: A DRL-based MIMO Layer and MCS Adapter in Open RAN 5G Networks
Qing An, Rahman Doost-Mohammady, Roy Yang, Kamakshi Sridhar
ACM Mobicom 2024 Workshop (To Appear)

Helix: A RAN Slicing Based Scheduling Framework for Massive MIMO Networks
Qing An, Divyanshu Pandey, Rahman Doost-Mohammady, Ashutosh Sabharwal, Srinivas Shakkottai
ACM CoNEXT 2024 (To Appear)

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks
Qing An, Santiago Segarra, Chris Dick, Ashutosh Sabharwal ,Rahman Doost-Mohammady
IEEE TMLCN 2023
Paper | Code

ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
Qing An, Mehdi Zafari, Chris Dick, Santiago Segarra, Ashutosh Sabharwal ,Rahman Doost-Mohammady
IEEE Asilomar 2023
Paper | Code