Mr. Mahmoud AlaaEldin studied his BSc in Electrical Engineering with majors in Electronics and communications at Alexandria University in Egypt. He received his Master’s degree from the American University in Cairo (AUC) in 2019. His Master thesis was focused in channel feedback in frequency division duplex (FDD) massive MIMO systems with multiple antenna users. In doing so, he proposed a novel channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel state information. He also proposed that block diagonalization is a potential precoding scheme to be used with such massive MIMO systems. His master work resulted in a conference paper published in Globecomm 2018 and a Journal paper to appear shortly. Through his years in AUC, he was a graduate fellow and a teacher assistant for multiple courses. Mahmoud was engaged in multiple research projects including the design and implementation of a wireless experiments testbed that is accessed from anywhere in the world through the Internet. He has experience in implementing and testing wireless systems on USRP platforms. His research interests include different areas of wireless communications, signal processing and machine learning. Since August 2019, he joined the university of Manchester as a Marie-Curie early stage researcher for the European ITN Project PAINLESS. His research work focuses on network optimization at the physical layer. It will explore and design low complexity lattice (LNC) and physical (PLNC) layer network coding techniques for the case of large scale distributed and energy-autonomous access-point formations with the objective to maximize the longevity of all the network nodes.
Research updates – Period 1
This work proposes an energy efficient transmission scheme using optimised adaptive users clustering (AUC) and assistive transmission (AT) in energy-autonomous wireless networks. Specifically, the proposed scheme is a two stage scheme that aims at minimising the transmission power consumption per user equipment (UE). In the first stage, an efficient clustering scheme, AUC, is performed by utilising Voronoi tessellation to create optimal UE clusters which minimised the UEs transmission energy consumption. In the second stage, a novel power control mechanism which is based on cooperative physical layer network coding (PNC) assistive transmission, PNC-AT, is introduced to achieve further energy reduction for the UEs. It is demonstrated that the use of a single assisting UE can save up to 33% in average power, compared to conventional transmission, which increases with the number of assisting UEs. Overall, the results reveal that combining PNC-AT and AUC produces superior improvements in UE energy efficiency and network longevity, compared to alternative benchmark techniques.
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