Jian Ding, Rahman Doost-Mohammady, Anuj Kalia, and Lin Zhong, "Agora: Real-time massive MIMO baseband processing in software," to appear ACM Int. Conf. emerging Networking EXperiments and Technologies (CoNEXT), December 2020. (PDF, 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).
Chandra, Ranveer, and Jian Ding, "Soil measurement system using wireless signals," U.S. Patent No. 10,761,206, September, 2020 (Link).
Jian Ding, "Software-based Baseband Processing for Massive MIMO", Master’s Thesis, Rice University, August 2019 (PDF).
- 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.
- 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.
- 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.
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.
I am from Shaoxing, Zhejiang, China. I got my B.E. from Zhejiang University in Optical Engineering. I came to the US for a Ph.D. program in Electrical and Computer Engineering at Rice University in 2014 and got my M.S. there. I transferred to Yale in January 2020 together with my advisor's move.
I love photography. I post my favorite photos here.