On Optimizing Resource Allocation for MIMO-NOMA Downlink
Wiroonsak Santipach
Kasetsart University
We consider multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) downlink channels with zero-forcing beamforming transmission. The base station has multiple transmit antennas while all mobile devices have a single receive antenna. To increase spectral efficiency, 2 active users with highly correlated channels may be paired to share the same beamforming vector and thus, will interfere fully with each other. Superposition coding and successive interference cancellation are applied to decode messages for each user in a pair. The performance of the pair depends on the accuracy of the channel direction information (CDI) and increases with the CDI rate. With limited total transmit power and total CDI rate, we would like to optimize the transmit power and CDI rate for each user to achieve max-min fairness for all users in a cell. In this talk, we will discuss different allocation schemes that perform close to the optimum with varying complexity. Some numerical examples show that the resulting rate performance with the proposed allocation is increased by 100% over that with the uniform allocation.
Wiroonsak Santipach received the B.S. (summa cum laude), M.S., and Ph.D. degrees all in electrical engineering from Northwestern University, Evanston, Illinois, USA in 2000, 2001, and 2006, respectively. In 2006, he joined the Department of Electrical Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand, as a Lecturer. Since 2012, he has been an Associate Professor. In 2013 and 2019, he was a visiting scholar at the Institute of Telecommunications, Technische Universität Wien, Austria. He has authored 2 books titled Introduction to Telecommunication Engineering (Kasetsart University Press, 2016) and Wireless Communications With Limited Feedback (Kasetsart University Press, 2020), and co-authored more than 40 technical papers. His current interest includes limited feedback in NOMA and MIMO channels and deep learning in wireless channels. He is currently a senior member of IEEE.
Neural Network Design based on Algorithm Unrolling and Its Applications
Daeyoung Park
Inha University
This tutorial introduces algorithm unrolling methods to design neural networks with iterative algorithms. Algorithm unrolling methods have received a lot of attention because they provide interpretable neural network architectures that exhibit high performance. As examples applied to communication/signal processing problems, we present how to design neural networks to solve the sparse signal recovery problems and MIMO detection problems.
Daeyoung Park received the B.S. and M.E. degrees in electrical engineering and the Ph.D. degree in electrical engineering and computer science from Seoul National University, Seoul, South Korea, in 1998, 2000, and 2004, respectively. He was with Samsung Electronics as a Senior Engineer from 2004 to 2007. Since 2008, he has been with Inha University, Incheon, South Korea, where he is currently a Professor. He is a co-author of Wireless Communications Resource Management (John Wiley & Sons, 2009). His research interests include sparsity-aware signal processing, multiple antenna communication systems, optimization, and machine learning.
When Deep Unfolding Meets Control Engineering
Masaki Ogura
Osaka University
Deep unfolding is a technique for tuning parameters for accelerating the convergence of iterative algorithms and has been actively studied in various fields including wireless communications. In this talk, we provide an overview of the speaker's work on the use of the deep unfolding technique in the field of control engineering. We specifically talk about some recent results on feedback control system design and model predictive control. Furthermore, we will review how the speaker came to know about the deep unfolding technique from the perspective of interdisciplinary research, and also try to discuss the affinity between deep unfolding and control theory research.
Masaki Ogura is an Associate Professor in the Graduate School of Information Science and Technology at Osaka University, Japan. Prior to joining Osaka University, he was a Postdoctoral Researcher at the University of Pennsylvania, USA, and an Assistant Professor at the Nara Institute of Science and Technology, Japan. His research interests include network science, dynamical systems, and stochastic processes with applications in networked epidemiology, design engineering, and biological physics. He was a runner-up of the 2019 Best Paper Award by the IEEE Transactions on Network Science and Engineering and a recipient of the 2012 SICE Best Paper Award. He is an Associate Editor of the Journal of the Franklin Institute.