I am an undergraduate researcher attending UCR advised by Zhaowei Tan at GeNeLa. Recently, I've focused on leveraging deep reinforcement learning to make 5G Open RAN networks more energy efficient. Currently, I am fortunate to be a Digital Agriculture Fellow leveraging drones for wireless soil data collection, as well as a research intern at UCR's AASHI DoD Center of Excellence, where I implemented a dynamic session key evolution scheme for LoRaWAN, and I am currently working on near-real-time RAW optimization using deep reinforcement learning.
In my free time, I enjoy going railfanning and eating excessive roujiamo.
Projects
lora-x-halow
A compact prototype for our ACM Hotmobile submission featuring a LoRaWAN-HaLow coexistence framework. An Openwrt-based Raspberry Pi 3 wearing a Seeed Wio WM-6108 Mini PCIe module acts as a HaLow AP. A Raspberry Pi 4 running Raspbian with a Semtech SX1303 module acts as the LoRaWAN gateway. I modified the LoRa packet forwarding utility to send packet metadata to a ZMQ server via IPC. Further extensions for algorithm design are left to future work.
GitHub →
genela-daf-datalogger
Supported by UCR's Digital Agriculture Fellowship in collaboration with the Verdi Water Management Group, I created a custom solar-powered Raspberry Pi data logger which accumulates soil data and transmits the data if it detects the collection drone flying overhead.
GitHub →