Deniz Gunduz received his M.S. and Ph.D. degrees in electrical engineering from NYU Tandon School of Engineering in 2004 and 2007, respectively. After his PhD, he served as a postdoctoral research associate at Princeton University, and as a consulting assistant professor at Stanford University. He was a research associate at CTTC in Barcelona, Spain until September 2012, when he joined the Electrical and Electronic Engineering Department of Imperial College London, UK, where he leads the Information Processing and Communications Lab. His research interests lie in the areas of communication and information theory, machine learning and privacy. Dr. Gunduz is an Editor of the IEEE Transactions on Wireless Communications and the IEEE Transactions on Green Communications and Networking. He also served as a Guest Editor for the IEEE Journal on Selected Areas in Communications Special Issue on “Machine Learning for Wireless Communications”, and as an Editor of the IEEE Transactions on Communications (2013-2018). He is the recipient of the IEEE Communications Society – Communication Theory Technical Committee (CTTC) Early Achievement Award in 2017, a Starting Grant of the European Research Council (ERC) in 2016, IEEE Communications Society Best Young Researcher Award for the Europe, Middle East, and Africa Region in 2014. He has received Best Paper Awards at GlobalSIP 2019, WCNC 2018, WCNC 2016 and ISIT 2007. He is a Distinguished Speaker of the IEEE Information Theory Society (2020-21).
Tutorial: Energy harvesting communication networks
Abstract: Devices powered by energy harvesting (EH) are increasingly being deployed in practice, in place of their traditional, battery-operated counterparts, when factors such as the sheer number of nodes or inaccessibility render battery replacement difficult and cost-prohibitive. Their deployment spans the whole gamut of autonomous networked systems: from machine-to-machine and sensor networks, to smart buildings and grid asset monitoring. Over the last years, there has been significant interest in EH technologies among the communications and networking research community. In contrast to battery-operated devices, where minimizing the energy consumption is crucial to prolong lifetime, in EH-powered devices, the objective is the intelligent management of the limited harvested energy to ensure long-term, uninterrupted communication. This tutorial will provide a brief overview of some of the fundamental approaches to the design of energy management policies for EH communication systems. We will focus on analytical models that capture the main challenges related to their design: the intermittent nature of harvested energy, the limited capacity and energy leakage in energy storage devices, and the constraints on device size and complexity.
Keynote: Machine learning applications for 5G and future networks
Abstract: In the first part of this keynote I will show how machine learning can help improve wireless communication systems. Communication system design traditionally followed a model-based approach, where highly specialized blocks are designed separately based on expert knowledge accumulated over decades of research. I will show that data-driven end-to-end designs can meet or even surpass the performance of these highly optimised block-based architectures. In particular, I will focus on wireless image transmission, and show that deep learning based joint source-channel coding architecture not only outperforms state-of-the-art digital communication systems based on separate image compression (BPG/ JPEG200) followed by near capacity-achieving channel codes (LDPC), but also provides graceful degradation with channel quality and adaptation to bandwidth. In the second part, I will focus on federated learning across wireless devices at the network edge, and show that jointly designing the communication protocol with the learning algorithm significantly improves the efficiency and accuracy of distributed learning across bandwidth and power limited wireless devices. In both parts of the talk I will highlight the convergence of machine learning and wireless communication system design, and point to some promising new research directions.