Cinque Terre

I am a Ph.D. student in the Computer Science Department at Yale University. I work with Dr. Lin Zhong in the Efficient Computing Lab. My research interests are in wireless systems including massive MIMO systems (Argos, Renew) and RF sensing.


Email Address: jian.ding@yale.edu

Office: AKW 311

  Jian Ding

Updates

2020.02

2019.10

2019.07

2018.10

2018.07


2018.06 - 2018.09 & 2017.05 - 2018.01

Awarded the Student Travel Grant of NSDI'20.

Our paper received Best Paper Honorable Mention Award at MobiCom'19.

Our work on Low Cost Soil Sensing Using Wi-Fi Singals is accepted to MobiCom'19.

Awarded the Student Travel Grant of OSDI'18.

Presented my work to Bill Gates during his visit to a FarmBeats deployment site in Carnation, WA. Check out Bill Gates's bolg & video for this visit.

Worked at Microsoft Research as a Research Intern in the FarmBeats team with Dr. Ranveer Chandra.

Publications


  • Jian Ding, Rahman Doost-Mohammady, Anuj Kalia, and Lin Zhong, "Agora: Software-based real-time massive MIMO baseband," being shephered for ACM Int. Conf. emerging Networking EXperiments and Technologies (CoNEXT), December 2020. (Source code)
  • Jian Ding and Ranveer Chandra, "Towards Low Cost Soil Sensing Using Wi-Fi," in Proc. of ACM Int. Conf. Mobile Computing and Networking (MobiCom), October 2019 (PDF, slides). (Best Paper Honorable Mention)
  • Clayton Shepard, Rahman Doost-Mohammady, Jian Ding, Ryan Guerra, and Lin Zhong, "ArgosNet: a multi-cell many-antenna MU-MIMO platform," in Proc. of IEEE Asilomar Conference (invited paper), 2017 (PDF).
  • Clayton Shepard, Jian Ding, Ryan E. Guerra, and Lin Zhong, "Understanding real many-antenna MU-MIMO channels," in Proc. of IEEE Asilomar Conference (invited paper) (PDF, Released data).

Projects

Software-based Baseband Processing for Massive MIMO

  • Massive multiple-input multiple-output (MIMO) is a key technology in 5G New Radio (NR) to improve spectral efficiency. A major challenge in its realization is the huge amount of real-time computation required. All existing massive MIMO baseband processing solutions use dedicated and specialized hardware like FPGAs, which can efficiently process baseband data but are expensive, inflexible and difficult to program. In this work, we design a software-only system called Agora that can handle the high computational demand of real-time massive MIMO baseband processing on a single commodity server. To achieve this goal, we identify the rich dimensions of parallelism in massive MIMO baseband processing, and exploit them with data parallelism across multiple CPU cores. We optimize Agora to best use CPU hardware and software features, including SIMD extensions to accelerate computation, cache optimizations to accelerate data movement, and kernel-bypass packet I/O. We evaluate Agora with up to 64 antennas and show that it meets the data rate and latency requirements of 5G NR.

Low Cost Soil Moisture and EC sensing Using Wi-Fi Signals

  • Existing soil sensing techniques are expensive (cost 100s to 1000s of USD). In this work, we design a Wi-Fi-based soil moisture and EC sensing system leveraging the phenonmenon that RF waves travel slower in soil with higher permittivity. We use a novel technique that exploits relative time-of-flight (ToF) of signals received by multiple antennas to overcome bandwidth limitation of Wi-Fi spectrum.

Exploration of Channel State Information Predictability

  • The predictablility of massive MIMO channels can help save computation resources. We apply channel prediction algorithms based on auto-regressive model and sum-of-sinusoids model to experimentally collected channel state information (CSI) of massive MIMO.

Empirical Foundations for Massive MIMO

  • Performance gains in multi-user MIMO (MU-MIMO) systems highly depends on the understanding of emprical behavior of channels. We leverage the Argos platform to conduct a comphensive real-time channel measurements accross frequency bands, mobilites, and propogation environments. We studied and compared the channel behaviors under different conditions. The results were published in our Asilomar 2016 paper.

    Our channel traces are available on Renew website.