Recent Advances in Radio Resource Management for Heterogeneous LTE/LTE-A Networks

Lee, Ying Loong; Chuah, Teong Chee; Loo, Jonathan; Vinel, Alexey

doi:10.1109/comst.2014.2326303

Cited by 167 publications

(110 citation statements)

References 130 publications

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“…Many strategies has been proposed for CRRM, i.e. in [12] the results shows that CRRM has much better performance in networks in comparisons to that networks without CRRM, such performance gain is valid for networks with www.ijacsa.thesai.org either real time (RT) and non-real time (NRT) services, in different terms, mainly capacity gain and blocking probability of the call [13].…”

Section: Related Workmentioning

confidence: 90%

Common Radio Resource Management Algorithms in Heterogeneous Wireless Networks with KPI Analysis

Tarapiah¹,

Aziz²,

Atalla³

2015

ijacsa

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Abstract-The rapid increase of number of personal wireless communication equipped devices boosts the user service demands on wireless networks. Thus, the spectrum resource management in such networks becomes an important topic in the near future. Notwithstanding, typically, users equipped with multiple wireless interfaces, thus the access operational scenario is no longer based on single Radio Access Technology (RAT). In this work, we studied the heterogeneous wireless communication scenarios, as a joint cooperative management of different RATs through which network providers can satisfy as possible as wide variety of user services demands in a more efficient manner by exploiting their varying characteristics and properties. To achieve this objective, a Common Radio Resource Management (CRRM) algorithms and techniques are proposed and designed to efficiently manage and optimize the radio resources in a heterogeneous wireless networks. In this context, this work studies and analyzes some common radio resource management techniques to efficiently distribute traffic among the available radio access technologies while provid-ing adequate quality of service levels under heterogeneous traffic scenarios. The most interesting algorithms have been critically analyzed and then some in depth investigations with attention on implementations and techno-economic issues are performed on some of the identified CRRM algorithms.

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Section: Related Workmentioning

confidence: 90%

Common Radio Resource Management Algorithms in Heterogeneous Wireless Networks with KPI Analysis

Tarapiah¹,

Aziz²,

Atalla³

2015

ijacsa

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“…The main advantage of machine learning approach over other techniques is its ability to learn the wireless environment and to adapt to it. To the best of our knowledge and according to some of more recent surveys, i.e., [22], there is only little work in the literature that is considering a machine learning for frequency assignment in small cell networks. In [23], the authors propose a machine learning approach based on reinforcement learning in a multi-agent system according to which the frequency assignment actions are taken in a decentralized fashion without having a complete knowledge on actions taken by other small cells.…”

Section: Related Work and Proposed Contributionmentioning

confidence: 99%

“…Additionally, as argued in [22], the existing techniques for femtocell-aware spectrum allocation need further investigation, i.e., co-tier interference and global fairness are still open issues. The main issue is to strike a good balance between spectrum efficiency and interference, i.e., to mitigate the trade-off between orthogonal spectrum allocation and co-channel spectrum allocation.…”

Section: Related Work and Proposed Contributionmentioning

confidence: 99%

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

Bojović

Meshkova

Baldo

et al. 2016

J Wireless Com Network

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Self-organizing networks (SONs) are expected to minimize operational and capital expenditure of the operators while improving the end users' quality of experience. To achieve these goals, the SON solutions are expected to learn from the environment and to be able to dynamically adapt to it. In this work, we propose a learning-based approach for self-optimization in SON deployments. In the proposed approach, the learning capability has the central role to perform the estimation of key performance indicators (KPIs) which are then exploited for the selection of the optimal network configuration. We apply this approach to the use case of dynamic frequency and bandwidth assignments (DFBA) in long-term evolution (LTE) residential small cell network deployments. For the implementation of the learning capability and the estimation of KPIs, we select and investigate various machine learning and statistical regression techniques. We provide a comprehensive analysis and comparison of these techniques evaluating the different factors that can influence the accuracy of the KPI predictions and consequently the performance of the network. Finally, we evaluate the performance of learning-based DFBA solution and compare it with the legacy approach and against an optimal exhaustive search for best configuration. The results show that the learning-based DFBA achieves on average a performance improvement of 33 % over approaches that are based on analytical models, reaching 95 % of the optimal network performance while leveraging just a small number of network measurements.

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“…Because of the small volume, low cost, and short transmission distances (a few meters to tens of meters in general), these low-power access points can solve "fade area" and "busy area" problems effectively, thereby improving sum-throughput and spectrum efficiency. Though vast improvements in sum-throughput have been made, researchers in the field of communications have reached a consensus that the incremental improvements fail to meet the escalating data demands of the foreseeable future [3][4][5]. Besides, in most wireless systems of interest, different users require different data rates, which may be accommodated by allowing users to subscribe to different levels of service.…”

Section: Introductionmentioning

confidence: 99%

An Improved User Association Algorithm for MAP–FAPs Heterogeneous Networks

2016

Symmetry

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Heterogeneous networks (HetNets) give users the opportunity to access different access points(APs), which will simultaneously affect user performance and system performance, so user association in HetNets plays a critical role in enhancing the load balancing and the system sum-throughput of networks. Meanwhile, the incremental sum-throughput currently fails to meet the escalating data demands. Besides, ensuring fairness amongst users constitutes another urgent issue in the radio resource management (RRM) of HetNets. What is more, few works consider the maximum service user number constraint in femtocell access points (FAPs). To solve the aforementioned problem, this paper associates users to APs by considering system sum-throughput and fairness at the same time in HetNets under a maximum service user number constraint of FAPs; accordingly, the user association problem is formulated. By releasing constraint, the optimal user association algorithm is obtained by Lagrangian function, and based on this optimal solution, a low complexity suboptimal user association algorithm is proposed. At last, this paper investigates the relationship between system sum-throughput and maximum service user number of FAPs. Numerical simulation results show that the proposed algorithm can improve sum-throughput and fairness at the same time at a specific maximum service user number of FAPs.

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Recent Advances in Radio Resource Management for Heterogeneous LTE/LTE-A Networks

Cited by 167 publications

References 130 publications

Common Radio Resource Management Algorithms in Heterogeneous Wireless Networks with KPI Analysis

Common Radio Resource Management Algorithms in Heterogeneous Wireless Networks with KPI Analysis

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

An Improved User Association Algorithm for MAP–FAPs Heterogeneous Networks

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