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.
Hamidouche Kenza, Kasgari Ali Taleb Zadeh, Saad Walid, Bennis Mehdi, Debbah Mérouane
A4 Article in conference proceedings
Place of publication:
2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
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
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