The Wireless Intelligent Networks (WIN) Lab at Santa Clara University explores next-generation wireless systems that adapt, learn, and make intelligent decisions — with a focus on efficient resource allocation, smarter access protocols, and user-centered performance.
We focus on technologies like WiFi 7, 5G/6G, and intelligent multiple access protocols—including NOMA, RSMA, and 6TiSCH — alongside research in QoE/QoS optimization, machine learning techniques in wireless network, network economics, wireless multimedia and cognitive radio.
Whether you’re passionate about future wireless networks, curious about how machine learning can shape next gen communications, or eager to build real-world impact through research — the WIN Lab is a collaborative space where your ideas matter, your growth is supported, and your work can shape the networks of tomorrow.
Semantic Aware User Grouping for 6G NOMA
Method for grouping users in 6G Non-Orthogonal Multiple Access (NOMA) networks that accounts not only for signal strength and interference, but also for how important each user's data is to transmit accurately (semantic importance). The approach aims to improve overall network efficiency and user experience by prioritizing the most meaningful information while maintaining strong performance across all connected users.
Wi-Fi 7 Multi-Link Scheduling with Reinforcement Learning
Wi-Fi 7 Multi-Link Operation project that uses simulation plus reinforcement learning to decide which band (2.4/5/6 GHz) should carry each uplink packet or video frame as conditions change (signal quality, congestion, interference, etc). Improves what users actually feel, lower delay for interactive traffic, and fewer stalls/missed deadlines for HEVC (H.265) streaming. Outperforms fixed band and simple round robin/heuristic scheduling under challenging network conditions.
Undergraduate Students
Graduate Students
Ph.D. Students: I’m looking to welcome kind, curious, and self-driven Ph.D. students to join the WIN Lab. If you’re passionate about wireless networks, protocols for 6G, machine learning for networks, or network economics, feel free to email me with your CV and research interests.
M.S. Students: If you’re interested in completing a master’s thesis or working on directed research under my supervision, I’d love to hear from you. RA positions are offered based on performance in prior research, typically through a directed research pathway.
Undergraduate Students: Curious minds are always welcome! If you're excited to explore research, build real-world systems, or dive into wireless and IoT innovations, drop by my office or send a quick note. No prior research experience is required—just enthusiasm and a willingness to learn.