Page 11 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks
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ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4



                                               LIST OF ABSTRACTS


               Graph-neural-network-based delay estimation for communication networks with
               heterogeneous scheduling policies

               Pages 1–8
               Martin Happ, Matthias Herlich, Christian Maier, Jia Lei Du, Peter Dorfinger

               Modeling communication networks to predict performance such as delay and jitter is important for
               evaluating and optimizing them. In recent years, neural networks have been used to do this, which may
               have advantages over existing models, for example from queueing theory. One of these neural networks
               is RouteNet, which is based on graph neural networks. However, it is based on simplified assumptions.
               One key simplification is the restriction to a single scheduling policy, which describes how packets of
               different  flows  are  prioritized  for  transmission.  In  this  paper  we  propose  a  solution  that  supports
               multiple scheduling policies (Strict Priority, Deficit Round Robin, Weighted Fair Queueing) and can
               handle mixed scheduling policies in a single communication network. Our solution is based on the
               RouteNet architecture as part of the "Graph Neural Network Challenge". We achieved a mean absolute
               percentage error under 1% with our extended model on the evaluation data set from the challenge. This
               takes neural-network-based delay estimation one step closer to practical use.
               View Article


               Site-specific millimeter-wave compressive channel estimation algorithms with
               hybrid MIMO architectures

               Pages 9–26
               Sai  Subramanyam  Thoota,  Dolores  Garcia  Marti,  Özlem  Tugfe  Demir,  Rakesh  Mundlamuri,  Joan
               Palacios, Cenk M. Yetis, Christo Kurisummoottil Thomas, Sameera H. Bharadwaja, Emil Björnson,
               Pontus Giselsson, Marios Kountouris, Chandra R. Murthy, Nuria González-Prelcic, Joerg Widmer
               In  this  paper,  we  present  and  compare  three  novel  model-cum-data-driven  channel  estimation
               procedures in a millimeter-wave Multi-Input Multi-Output (MIMO) Orthogonal Frequency Division
               Multiplexing  (OFDM)  wireless  communication  system.  The  transceivers  employ  a  hybrid  analog-
               digital architecture. We adapt techniques from a wide range of signal processing methods, such as
               detection and estimation theories, compressed sensing, and Bayesian inference, to learn the unknown
               virtual beamspace domain dictionary, as well as the delay-and-beamspace sparse channel. We train the
               model-based algorithms with a site-specific training dataset generated using a realistic ray tracing-based
               wireless  channel  simulation  tool.  We  assess  the  performance  of  the  proposed  channel  estimation
               algorithms with the same site's test data. We benchmark the performance of our novel procedures in
               terms of normalized mean squared error against an existing fast greedy method and empirically show
               that model-based approaches combined with data-driven customization unanimously outperform the
               state-of-the-art techniques by a large margin. The proposed algorithms were selected as the top three
               solutions  in  the  "ML5G-PHY  Channel  Estimation  Global  Challenge  2020"  organized  by  the
               International Telecommunication Union.

               View Article












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