Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems

Kenza Hamidouche (1) , Ali Taleb Zadeh Kasgari , Walid Saad (2) , Mehdi Bennis (3) , Merouane Debbah (4)

1 LANEAS - Large Networks and Systems Group
2 LIPN - Laboratoire d'Informatique de Paris-Nord
3 CWC - Centre for Wireless Communications [University of Oulu]
4 L2S - Laboratoire des signaux et systèmes

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.

Domaines: Apprentissage [cs.LG]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Traitement des images [eess.IV]

Kenza Hamidouche, Ali Taleb Zadeh Kasgari, Walid Saad, Mehdi Bennis, Merouane Debbah. Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems. IEEE International Conference on Communications (ICC 2018), May 2018, Kansas City, United States. ⟨10.1109/ICCW.2018.8403664⟩. ⟨hal-01923643⟩