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Towards Massive Distribution of Intelligence for 6G Network Management using Double Deep Q-Networks
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  • Sayantini Majumdar ,
  • Susanna Schwarzmann ,
  • Riccardo Trivisonno ,
  • Georg Carle
Sayantini Majumdar
Huawei Technologies & Technical University of Munich, Huawei Technologies & Technical University of Munich

Corresponding Author:[email protected]

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Susanna Schwarzmann
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Riccardo Trivisonno
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Georg Carle
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Our solution evaluation is based on synthetic data-sets generated by our internally-developed simulation platform. This means we do not use any stand-alone, external or internal data-sets. Our platform code is not public, but we describe in detail the design of the platform and the data-set generator module, for other researchers to reproduce our results. The code is written in 100% Python, leveraging object-oriented approaches for code reusability.
This article has been accepted for publication in IEEE Transactions on Network and Service Management. This is the author’s version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/TNSM.2023.3333875
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
2023Published in IEEE Transactions on Network and Service Management on pages 1-1. 10.1109/TNSM.2023.3333875