Projects

Software-based Baseband Processing for Massive MIMO

  • On-going...

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.

    We developed a website based on the Pyramid web framework to store, manage, and analyze our channel measurment data.