Hi there!

This is Jingqi Huang. I am a Computer Science Ph.D. student at Purdue University. My advisor is Prof. Muhammad Shahbaz. Before joining Purdue, I received my master and bachalor degree in 2020 and 2018 from University of California, San Diego (UCSD) and Beijing University of Posts and Telecommunications (BUPT), respectively. My research interests are in the broad areas of networks and systems, including several related topics. Recently, I am working on characterization and optimization of 5G mobile core.

You can check out my CV, and contact me via email.


    [7] Haotian Deng, Qianru Li, Jingqi Huang, Chunyi Peng. "iCellSpeed: Increasing Cellular Data Speed with Device-Assisted Cell Selection". The 26th Annual International Conference on Mobile Computing and Networking (MobiCom'20)

    [6] Song Wang*, Jingqi Huang* (co-primary), Xinyu Zhang, "Demystifying Millimeter-Wave V2X: Towards Robust and Efficient Directional Connectivity Under High Mobility", The 26th Annual International Conference on Mobile Computing and Networking (MobiCom'20)

    [5] Song Wang*, Jingqi Huang* (co-primary), Xinyu Zhang, Hyoil Kim and Sujit Dey, "X-Array: Approximating Omnidirectional Millimeter-Wave Coverage Using an Array of Phased Arrays", The 26th Annual International Conference on Mobile Computing and Networking (MobiCom'20)

    [4] Anfu Zhou, Shaoqing Xu, Song Wang, Jingqi Huang, Shaoyuan Yang, Teng Wei, Xinyu Zhang and Huadong Ma, "Robot Navigation in Radio Beam Space: Leveraging Robotic Intelligence for Seamless mmWave Network Coverage", ACM International Symposium on Mobile Ad Hoc Networking (MobiHoc'19) [pdf]

    [3] Song Wang*, Jingqi Huang* (co-primary) and Anfu Zhou, ”KPad: Maximizing Channel Utilization for MU-MIMO Systems using Knapsack Padding”, IEEE International Conference on Communications 2018 (IEEE ICC'18) [pdf]

    [2] Anfu Zhou, Shaoqing Xu, Song Wang, Jingqi Huang, Shaoyuan Yang, Xinyu Zhang and Huadong Ma, "Robotic Millimeter-Wave Wireless Networks", Accepted by to IEEE/ACM Transactions on Networking (ToN)

    [1] Anfu Zhou, Zheng Zhang, Jingqi Huang, Song Wang, Xinyu Zhang and Huadong Ma, ”Towards Robust Millimeter Wave Links under Mobility and Blockage via Efficient Model-driven Beam Steering”, to be submitted


Enable WiFi-like coverage in millimeter-wave (mmWave) network

The emerging mmWave networking technology promises to unleash a new wave of multi-Gbps wireless applications. However, mmWave is known to be highly directional and bear limited coverage, so maintaining stable link connection and achieving WiFi-like coverage is challenging and not well solved. Thus we developed several systems to enable robust connection under link dynamic and enable room-level omnidirectional coverage in mmWave network.

Enable omnidirectional coverage using array of phased array (APA) architecture

We propose X-Array, which jointly selects the arrays and beams, and applies a dynamic co-phasing mechanism to ensure different arrays’ signals enhance each other. X-Array also incorporates a link recovery mechanism to identify alternative arrays/beams that can efficiently recover the link from outage. We have implemented X-Array on a commodity 802.11ad APA radio. Our experiments demonstrate that X-Array can approach omni-directional coverage and maintain high performance in spite of link dynamics.

Leverage robotic relay to realize room-level seamless coverage

Our design enables a robot relay automatically constructs the geometry/reflectivity of the environment using measured RSS only, then navigates itself along an optimal moving trajectory, and ensures continuous connectivity for the client despite environment/human dynamics. Our field trials demonstrate that our design can achieve nearly full coverage in dynamic environments, even with constrained speed and mobility region.

Model-driven beam prediction to achieve robust mmWave connection under mobility and blockage

Our design leverages the observation that mmWave spatial channel profiles (SCP) of nearby locations are highly-correlated, so we can reconstruct the SCP as the Tx/Rx moves with possible blockages, using a reverse-engineering approach without explicit channel scanning. We also design and incorporate greedy approximation algorithms to resolve the high computational complexity issue conventionally involved with reverse-engineering. In this way, we can predict new optimal beams and realign links for mobile/blocked users with minimal overhead.

Maximizing the channel ultilization in Multi-User Multiple-Input-Multiple-Output (MU-MIMO) system

MU-MIMO is the hallmark of IEEE 802.11 ac and ax, which guarantees the multi-fold throughput gain by supporting multiple concurrent data transmissions from multiple users. But the channel can be significantly under-utilizatized due to the diverse size of the frames which is widely-distributed from dozens of bytes to maximum transmission unit (MTU) usually of 1500 bytes. Then all the shorter frames have to wait till the longest frame completes its transmission. Thus we were exploring the possibility of utilizing the channel by adding more data streams in the idle channel.

Maximizing channel utilization for MU-MIMO systems using Knapsack padding

We propose a novel model-driven frame padding design to maximize MU-MIMO channel utilization. We first formally formulate the frame padding problem as a multi-stream knapsack model, and then design a stream decoupling mechanism to handle the unique and complicated inter-stream interference underlying the model, so as to derive the optimal padding schedule efficiently. We evaluate our design using trace-driven emulation. Extensive evaluation results demonstrate remarkable throughput gain (up to 42%) compared with the state-of-the-art.

Skill set

  • Python, C/C++, Java, P4, SQL...
  • SDN/OpenFlow, ONF Aether, Docker, Kubernetes, Mininet, Wireless Insight ...