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Transfer Learning for Tilt-Dependent Radio Map Prediction

Profile image of Claudia PareraClaudia Parera

2020, IEEE Transactions on Cognitive Communications and Networking

https://doi.org/10.1109/TCCN.2020.2964761
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15 pages

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Abstract

Machine learning will play a major role in handling 1 the complexity of future mobile wireless networks by improving 2 network management and orchestration capabilities. Due to the 3 large number of parameters that can be monitored and config-4 ured in the network, collecting and processing high volumes of 5 data is often unfeasible or too expensive at network runtime, 6 which calls for taking resource management and service orches-7 tration decisions with only a partial view of the network status. 8 Motivated by this fact, this paper proposes a transfer learning 9 framework for reconstructing the radio map corresponding to a 10 target antenna tilt configuration by transferring the knowledge 11 acquired from another tilt configuration of the same antenna, 12 when no or very limited measurements are available from the 13 target. The performance of the framework is validated against 14 standard machine learning techniques on a data set collected 15 from a 4G commercial base stations. In most of the tested scenar-16 ios, the proposed framework achieves notable prediction accuracy 17 with respect to classical machine learning approaches, with a 18 mean absolute percentage error below 8%. 19 Index Terms-Radio map prediction, antenna tilt, transfer 20 learning. 21 I. INTRODUCTION 22 F IFTH generation wireless networks (5G) are expected to 23 improve the performance of cellular systems, achieving 24 higher data rates, reduced latency, higher reliability and sup-25 port for greater numbers of users. To achieve this, 5G resorts 26 to dense and heterogeneous deployments, coupled with higher 27 flexibility in the network access and core domains, which can 28 be dynamically managed in either a centralized or distributed 29 manner. To cope with such a complex scenario, it is foreseen 30 that machine learning tools will play a major role in enabling 31

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