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

By Kenza Hamidouche, Ali Taleb Zadeh Kasgari, Walid Saad, Medhi Bennis, Merouane Debbah. 2018

24 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “However, this RSU-centric approach is impractical due to the fact that: i) The overhead needed for frequent communications with all the VUEs in a highly dynamic network will degrade the networkwide performance, and ii) VUEs may prefer not to share their QSI with other vehicles, in which warrants col”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “, in cell association [31], computation offloading [32], and vehicular networks [33].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, background): “The problem of cell association when the density of users increases has been extensively addressed in the past [133], [134], but recently as ML techniques have emerged, Q-learning algorithms have been proposed to enable users to select their serving BS faster by exploiting local data and the learnin”