Qingyang Song

Qingyang Song is a professor in the field of Computer Science and Engineering, focusing on wireless networks and vehicular communications. His research spans topics such as network selection, resource allocation, and fault-tolerant routing mechanisms across heterogeneous systems, including UMTS, wireless LAN, and vehicular networks. While his institutional affiliations are not explicitly stated in the provided text, his collaborative work with researchers like A. Jamalipour and Lei Guo highlights his contributions to next-generation network optimization.

His research interests include Network Coding, Software Defined Networking (SDN), 5G Systems, and Energy Efficiency. He has pioneered techniques to balance user preferences with network conditions, reducing handoffs and improving connectivity. Recent work explores UAV-assisted networks, quantum-inspired reinforcement learning for wireless VR, and secrecy rate optimization in IRS-aided SWIPT systems.

Qingyang Song's publications reflect a strong emphasis on Engineering and Computer Science disciplines, particularly in vehicular communications, spectrum sharing, and hybrid routing frameworks. His work has been featured in IEEE journals and conferences, including IEEE Transactions on Vehicular Technology and IEEE Wireless Communications.