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http://dx.doi.org/10.7472/jksii.2021.22.5.1

Interference Aware Fractional Frequency Reuse using Dynamic User Classification in Ultra-Dense HetNets  

Ban, Ilhak (Dept. of Computer Science and Statistics, Chosun University)
Kim, Se-Jin (Dept. of Computer Science and Statistics, Chosun University)
Publication Information
Journal of Internet Computing and Services / v.22, no.5, 2021 , pp. 1-8 More about this Journal
Abstract
Small-cells in heterogeneous networks are one of the important technologies to increase the coverage and capacity in 5G cellular networks. However, due to the randomly arranged small-cells, co-tier and cross-tier interference increase, deteriorating the system performance of the network. In order to manage the interference, some channel management methods use fractional frequency reuse(FFR) that divides the cell coverage into the inner region(IR) and outer region(OR) based on the distance from the macro base station(MBS). However, since it is impossible to properly measure the distance in the method with FFR, we propose a new interference aware FFR(IA-FFR) method to enhance the system performance. That is, the proposed IA-FFR method divides the MUEs and SBSs into the IR and OR groups based on the signal to interference plus noise ratio(SINR) of macro user equipments(MUEs) and received signals strength of small-cell base stations(SBSs) from the MBS, respectively, and then dynamically assigns subchannels to MUEs and small-cell user equipments. As a result, the proposed IA-FFR method outperforms other methods in terms of the system capacity and outage probability.
Keywords
5G; OFDMA; heterogeneous network; fractional frequency reuse; interference management; dynamic user classification;
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