University of Oulu

K. Hamidouche, A. T. Z. Kasgari, W. Saad, M. Bennis and M. Debbah, "Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems," 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, MO, 2018, pp. 1-6. doi: 10.1109/ICCW.2018.8403664

Collaborative artificial intelligence (AI) for user-cell association in ultra-dense cellular systems

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Author: Hamidouche, Kenza1; Kasgari, Ali Taleb Zadeh2; Saad, Walid2;
Organizations: 1LSS, CentraleSupelec, Université Paris-Saclay, Gif-sur-Yvette, France
2Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA
3CWC - Centre for Wireless Communications, Oulu, Finland
4Mathematical and Algorithmic Sciences Lab, Huawei France R&D, Paris, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-02-19


In this paper, the problem of cell association between small base stations (SBSs) and users in dense wireless networks is studied using artificial intelligence (AI) techniques. The problem is formulated as a mean-field game in which the users’ goal is to maximize their data rate by exploiting local data and the data available at neighboring users via an imitation process. Such a collaborative learning process prevents the users from exchanging their data directly via the cellular network’s limited backhaul links and, thus, allows them to improve their cell association policy collaboratively with minimum computing. To solve this problem, a neural Q-learning learning algorithm is proposed that enables the users to predict their reward function using a neural network whose input is the SBSs selected by neighboring users and the local data of the considered user. Simulation results show that the proposed imitation-based mechanism for cell association converges faster to the optimal solution, compared with conventional cell association mechanisms without imitation.

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Series: IEEE/CIC International Conference on Communications in China - Workshops
ISSN: 2474-9133
ISSN-E: 2474-9141
ISSN-L: 2474-9133
ISBN: 978-1-5386-4328-0
ISBN Print: 978-1-5386-4329-7
Pages: 1 - 6
Article number: 8403664
DOI: 10.1109/ICCW.2018.8403664
Host publication: 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
Conference: IEEE International Conference on Communications Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the U.S. National Science Foundation under Grants CNS-1460316 and IIS-1633363.
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