Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems
Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems
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DOI：
10.1109/iccw.2018.8403664
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Kenza Hamidouche;Ali Taleb Zadeh Kasgari;W. Saad;M. Bennis;M. Debbah
中科院分区：
文献类型：
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作者：
Kenza Hamidouche;Ali Taleb Zadeh Kasgari;W. Saad;M. Bennis;M. Debbah
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.
