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
|Author:||Hamidouche, Kenza1; Kasgari, Ali Taleb Zadeh2; Saad, Walid2;|
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
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202002195831
Institute of Electrical and Electronics Engineers,
|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.
IEEE/CIC International Conference on Communications in China - Workshops
|Pages:||1 - 6|
2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
IEEE International Conference on Communications Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research was supported by the U.S. National Science Foundation under Grants CNS-1460316 and IIS-1633363.
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