Study of Almost Blank Subframe Configurations for Traffic offload in HetNets

Teerasuttakorn, Nonthapat; Nuanyai, Kittipong; Zamani, Alireza; Schmeink, Anke; Chantaraskul, Soamsiri

doi:10.1109/ictc.2018.8539494

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…A low ABS ratio causes offloaded users to have low average data rates due to overloaded users in the ABS [38]. With the proper configuration of the CIO value and ABS ratio, the QoE of cell edge users and the overall system performance can be enhanced [39].…”

Section: ) Cell Range Expansion (Cre)mentioning

confidence: 99%

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

et al. 2022

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The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

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Section: ) Cell Range Expansion (Cre)mentioning

confidence: 99%

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

et al. 2022

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“…In [21], a system with two-tier HetNet using fixed ABS is evaluated with the variation of the offered load. The study presents the ABS benefits in terms of fairness and a better balance between the 5th-percentile UE throughput of small cells and the average UE throughput of the macro cell.…”

Section: Related Workmentioning

confidence: 99%

MACD e-ICIC: a dynamic LTE interference coordination method based on trend and trading know-how

2020

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Mobile communications are preparing for the incredible changes from 4th Generation (4G) to 5th Generation (5G) in the coming years. In this new generation, co-channel interference is one of the critical challenges to be tackled due to network densification by providing high data rates through several macro and small cells working together, configuring the so-called Heterogeneous Network (HetNet). The umbrella of 3GPP system (including LTE and its evolution towards 5G) provides the Almost Blank Subframe (ABS) as a scheme of the Enhanced Inter-Cell Interference Coordination (e-ICIC) framework to mitigate interference among macro and small cells. The ABS mutes some of the macro cell transmissions in selected subframes to decrease interference to small cells, thus orthogonalizing macro and small cell transmissions in time-domain. In this paper, we use a moving average convergence/divergence technique based on trading know-how to propose a real time ABS e-ICIC algorithm. Our proof-of-concept simulation results show relative capacity gains of 112% compared to the case without ABS.

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“…We obtain the distributions of rate, spectral and energy efficiency and use them to optimize the cell range expansion bias and resource allocation parameters. There are several studies in the literature investigating the optimal selection of resource allocation parameters, range expansion bias values and user associations such as [20][21][22][23][24][25]. The HetNet models used in these papers are similar to the HetNet deployment we use in this study.…”

Section: Related Workmentioning

confidence: 99%

“…Instead of using an analytical model in order to obtain SINR CDF as we have done in this paper, [22] assumes that pico BSs collect the real SINRs of the users and use these values to calculate the optimal system parameters. In [23,24], several radio resource management and interference coordination schemes with various CRE bias values are evaluated via simulations for a HetNet model. In [25], the optimal time resource allocation parameter selection problem is formulated as a mixed integer nonlinear programming problem, and the problem is solved in order to maximize the user with the minimum data rate.…”

Section: Related Workmentioning

confidence: 99%

Downlink data rate, energy and spectral efficiency distribution in heterogeneous networks with cell-edge located small cells

Yenihayat

Karasan

2019

Wireless Netw

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In this paper, the probability distributions of per user downlink data rate, spectral efficiency (SE) and energy efficiency (EE) are analytically derived for a heterogeneous network model with cell-edge located small cells. The high accuracies of analytically derived cumulative distribution functions (CDF) are verified using distributions obtained via simulations. CDF expressions are then used in order to optimize key performance indicators (KPI) which are selected here as 10th percentile downlink data rate (R 10), spectral efficiency (SE 10) and energy efficiency (EE 10). In addition to optimizing KPIs separately, we also investigate the variation of the KPIs with respect to each other employing the analytically derived distributions. The results show that the resource allocation parameter values maximizing R 10 are very close to the values that maximize EE 10. However, the values that are optimal for EE 10 and R 10 are not optimal for SE 10 , which demonstrates the EE and SE trade-off in HetNets.

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Study of Almost Blank Subframe Configurations for Traffic offload in HetNets

Cited by 4 publications

References 9 publications

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

MACD e-ICIC: a dynamic LTE interference coordination method based on trend and trading know-how

Downlink data rate, energy and spectral efficiency distribution in heterogeneous networks with cell-edge located small cells

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