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http://dx.doi.org/10.6109/jkiice.2022.26.10.1517

DNN-Based Dynamic Cell Selection and Transmit Power Allocation Scheme for Energy Efficiency Heterogeneous Mobile Communication Networks  

Kim, Donghyeon (School of Electronic and Electrical Engineering, Hankyong National University)
Lee, In-Ho (School of Electronic and Electrical Engineering, and Research Center for Hyper-Connected Convergence Technology, Hankyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.10, 2022 , pp. 1517-1524 More about this Journal
Abstract
In this paper, we consider a heterogeneous network (HetNet) consisting of one macro base station and multiple small base stations, and assume the coordinated multi-point transmission between the base stations. In addition, we assume that the channel between the base station and the user consists of path loss and Rayleigh fading. Under these assumptions, we present the energy efficiency (EE) achievable by the user for a given base station and we formulate an optimization problem of dynamic cell selection and transmit power allocation to maximize the total EE of the HetNet. In this paper, we propose an unsupervised deep learning method to solve the optimization problem. The proposed deep learning-based scheme can provide high EE while having low complexity compared to the conventional iterative convergence methods. Through the simulation, we show that the proposed dynamic cell selection scheme provides higher EE performance than the maximum signal-to-interference-plus-noise ratio scheme and the Lagrangian dual decomposition scheme, and the proposed transmit power allocation scheme provides the similar performance to the trust region interior point method which can achieve the maximum EE.
Keywords
Dynamic cell selection; Deep learning; Energy efficiency; Heterogeneous network; Transmit power allocation;
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