معرفی
Leila Musavian is a Professor of Wireless Communications at the University of Surrey's Department of Electrical and Electronic Engineering within the Faculty of Engineering and Physical Sciences. She is a leading researcher in next-generation wireless communication systems with a focus on 5G/6G technologies, particularly in the areas of non-orthogonal multiple access (NOMA), energy harvesting communications, physical layer network coding, and ultra-reliable low-latency communications (uRLLC). Her research has significant implications for vehicular networks, IoT applications, and future wireless infrastructure.
Her research interests span multiple critical areas of modern wireless communications, including energy-efficient resource allocation, security in wireless networks, massive MIMO systems, and reconfigurable intelligent surfaces. She has pioneered work on the performance analysis of NOMA systems under statistical quality of service constraints, which has become increasingly important for emerging applications requiring ultra-reliable and low-latency communications. Her research bridges theoretical foundations with practical implementations, often incorporating machine learning techniques for optimization of wireless networks.
Her publication portfolio shows a clear evolution toward cutting-edge topics in wireless communications, with recent focus on holographic beamforming via dynamic metasurface antennas, robotic wireless energy transfer, and deep reinforcement learning applications for vehicular optical camera communications. These works demonstrate her ability to identify and address emerging challenges in next-generation wireless systems, particularly those related to 6G technologies and beyond.
Professor Musavian has successfully supervised numerous PhD students who have gone on to become active researchers in the field, with many continuing to collaborate with her on cutting-edge projects. Her research has been supported by significant grants from national and international funding bodies, though specific grant details are not visible in the current dataset.
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