• ETRI Journal
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• v.40 no.2
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• pp.227-236
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• 2018

Ultra-dense small cell networks (UD-SCNs) have been identified as a promising scheme for next-generation wireless networks capable of meeting the ever-increasing demand for higher transmission rates and better quality of service. However, UD-SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software-defined networking (SDN)-based hierarchical agglomerative clustering (SDN-HAC) framework, which leverages SDN to centrally control all sub-channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non-cooperative scenarios, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1135-1144
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• 2013

Deploying small cells is a reliable and influential solution to handle the skyrocketing traffic increase in the cellular network, and the small cell technology is evolving to ultra-dense network (UDN). In this paper we propose a small cell on/off algorithm with a simple but essential framework composed of access point (AP), user equipment (UE), and small cell controller (SCC). We propose Device-Assisted Networking for Cellular grEening (DANCE) algorithms that save the energy consumption by tying to minimize the number of turned-on APs while maintaining the network throughput. In doing so, SCC firstly gathers the feedback messages from UEs and then makes a decision including a set of turned-on APs and user association. DANCE algorithm has several variations depending on the number of bits of the UE's feedback message (1 bit vs. N bit), and is divided into AP-first, UE-first, or Proximity ON according to the criteria of selecting the turned-on APs. We perform extensive simulations under the realistic UDN environment, and the results confirm that the proposed algorithms, compared to the baseline, can significantly enhance the energy efficiency, e.g., more than a factor of 10.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.58-67
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• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.494-513
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• 2019

A multi-channel access problem based on hypergraph model in ultra-dense small cell networks is studied in this paper. Due to the hyper-dense deployment of samll cells and the low-powered equipment, cumulative interference becomes an important problem besides the direct interference. The traditional binary interference model cannot capture the complicated interference relationship. In order to overcome this shortcoming, we use the hypergraph model to describe the cumulative interference relation among small cells. We formulate the multi-channel access problem based on hypergraph as two local altruistic games. The first game aims at minimizing the protocol MAC layer interference, which requires less information exchange and can converge faster. The second game aims at minimizing the physical layer interference. It needs more information interaction and converges slower, obtaining better performance. The two modeled games are both proved to be exact potential games, which admit at least one pure Nash Equilibrium (NE). To provide information exchange and reduce convergecne time, a cloud-based centralized-distributed algorithm is designed. Simulation results show that the proposed hypergraph models are both superior to the existing binary models and show the pros and cons of the two methods in different aspects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4108-4122
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• 2016

To address challenges of an unprecedented growth in mobile data traffic, the ultra-dense network deployment is a cost efficient solution to off-load the traffic over other small cells. However, the real traffic is often much lower than the peak-hour traffic and certain small cells are superfluous, which will not only introduce extra energy consumption, but also impose extra interference onto the radio environment. In this paper, an elastic energy efficient cell management scheme is proposed based on flow scheduling among multi-layer ultra-dense cells by a SDN controller. A significant power saving was achieved by a cell-level energy manager. The scheme is elastic for energy saving, adaptive to the dynamic traffic distribution in the office or campus environment. In the end, the performance is evaluated and demonstrated. The results show substantial improvements over the conventional method in terms of the number of active BSs, the handover times, and the switches of BSs.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.25-31
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• 2022

In ultra dense networks (UDNs), macro user equipments (MUEs) have significant interference from small-cell access points (SAPs) since a number of SAPs are deployed in the coverage of macro base stations of 5G mobile communication systems. In this paper, we propose a dynamic channel assignment scheme to increase the performance of MUEs for the uplink of UDNs even though the number of SAPs is increased. The target of the proposed dynamic channel assignment scheme is that the signal-to-interference and noise ratio (SINR) of MUEs is above a given SINR threshold assigning different subchannels to SUEs from those of MUEs. Simulation results show that the proposed dynamic channel assignment scheme outperforms others in terms of the mean MUE capacity even though the mean SUE capacity is decreased a little lower.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1307-1312
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• 2015

As smart-phones become popular, mobile data traffic has been dramatically increasing and intensive researches on the next-generation mobile communication network is in progress to meet the increasing demand for mobile data traffic. In particular, heterogeneous network (HetNet) is attracting much interest because it can significantly enhance the network capacity by increasing the spatial reuse with macro and small cells. In the HetNet, we have several problems such as load imbalance and interference because of the difference in transmit power between macro and small cells and cell range expansion (CRE) can mitigate the problems. In this paper, we propose a new cell selection scheme with adaptive cell range expansion bias (CREB) for ultra dense HetNet and we analyze the performance of the proposed scheme in terms of average cell transmission rate through system-level simulations and compare it with those of other schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.63-66
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• 2015

As smart-phones become popular, mobile data traffic has been dramatically increasing and intensive researches on the next-generation mobile communication network is in progress to meet the increasing demand for mobile data traffic. In particular, heterogeneous network (HetNet) is attracting much interest because it can significantly enhance the network capacity by increasing the spatial resue with macro and small cells. In the HetNet, we have several problems such as load imbalance and interference because of the difference in transmit power between macro and small cells and cell range expansion (CRE) can mitigate the problems. In this paper, we propose a new cell selection scheme with adaptive cell range expansion bias (CREB) for ultra dense HetNet and we analyze the performance of the proposed scheme in terms of average cell transmission rate through system-level simulations and compare it with those of other schemes.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.9-15
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• 2021

In ultra-dense heterogeneous networks, the amount of interference from small-cell base stations(SBS) to macro-cell user equipments (MUEs) increases significantly as the number of SBSs increases and it causes the MUEs to decrease the signal-to-interference and noise ratio(SINR) and system capacity. In this paper, we propose a small-cell based cooperative multi-point(CoMP) communication scheme that can guarantee the performance of MUEs even when the number of SBSs increases. In the proposed scheme, MUEs first find SBSs that give signal strength equal to or greater than a given SINR threshold and then they are served by different numbers of the selected SBSs using CoMP to improve the performance of MUEs. Simulation results show that the proposed small-cell based CoMP scheme outperforms other interference management or CoMP schemes in terms of the SINR and system capacity of MUEs.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.1-8
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• 2021

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.