Xingqi Wu

About

Xingqi Wu is a leading researcher in next-generation wireless communications, with a primary focus on ultra-reliable low-latency communication (URLLC) and Open Radio Access Networks (O-RAN). His most impactful work, "Joint Admission Control and Resource Provisioning for URLLC Traffic in O-RAN: A Constrained Multi-Agent Reinforcement Learning Approach" (2025, 3 citations), addresses a critical challenge in mission-critical applications like industrial robotics and remote healthcare. Wu’s key contribution lies in developing a constrained multi-agent reinforcement learning framework that dynamically manages admission control and resource allocation, preventing network overload and resource contention under high traffic loads. This work directly tackles the stringent reliability and latency requirements of URLLC, offering a scalable solution for real-world O-RAN deployments. By bridging reinforcement learning with network optimization, Wu has provided a foundational approach for ensuring robust performance in next-generation RAN environments. His research is highly relevant for students and engineers working on 5G/6G systems, network slicing, and AI-driven network management, demonstrating how intelligent algorithms can safeguard critical communications in increasingly congested wireless networks.

Research Focus

Key Achievements

Top Papers

1. 1
   Joint Admission Control and Resource Provisioning for URLLC Traffic in O-RAN: A Constrained Multi-Agent Reinforcement Learning Approach

   3 citations · 2025

Key Collaborators

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