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

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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