SDN-Based Hierarchical Agglomerative Clustering Algorithm for Interference Mitigation in Ultra-Dense Small Cell Networks

Yang, Guang; Cao, Ye; Esmailpour, Amir; Wang, Deqiang

doi:10.4218/etrij.2017-0084

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Therefore, interferencecentric sufferings of UD-SCNs are highly imperative. In [127], the authors suggested centralized control of subchannels called SDN-HAC (SDN-based Hierarchical Agglomerative Clustering). SDN-HAC decides cluster merging on suitability function-based similarity criterion.…”

Section: K Hierarchical Agglomerative Clusteringmentioning

confidence: 99%

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

et al. 2023

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The outright aim of Beyond Fifth Generation (B5G) communication is to bring a revolution in Quality of Service (QoS) through enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC). Thereby, due to this massive expansion in the communication paradigm, interference is bound to surface unexpected challenges in the wireless domain. Thus, interference mitigation is a foundational aim in the research paraphernalia of B5G communication. Presently, Software Defined Networking (SDN) is emerging to empower wireless communication with centralized routing-based interference mitigation. With this premise, we aim to present a focused review on SDN-based interference mitigation in wireless communication. Initially, we discuss current research horizon in the subject domain and briefly explore literature for routing-based interference mitigation. Thereby, we analyze the potential of SDN through evaluation of existing solutions for interference mitigation in B5G communication. We conclude our survey by highlighting prominent future research directions in SDN-based interference mitigation.

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Section: K Hierarchical Agglomerative Clusteringmentioning

confidence: 99%

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

et al. 2023

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“…In [465], the optimal number of reserved channels is decided based on secondary user retainability and channel availability, while an application policy considers the primary user channel availability minimum by monitoring the incoming traffic requests. A Hierarchical Agglomerative Clustering (HAC) framework to control all sub-channels in the network to decide on cluster merging in ultra-dense small cell networks with an application policy to mitigate severe interference among small cell base stations has been proposed in [466]. Furthermore, mobility management also includes offloading, such as Wi-Fi device to device offloading from cellular networks when device to device communication is available [467].…”

Section: Network Managementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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“…Following considerable research efforts, many schemes have been proposed to minimize the effect of co-tier interference in HetNets, including clustering techniques, cognitive approaches, resource allocation solutions and power control techniques [7][8] [9] [10]. Among them, Sultan et al [11] discussed power and radio resource management techniques to mitigate the co-tier interference in femtocells.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Solution for Video Delivery to Mitigate Co-Tier Interference in 5G HetNets

Anand,

Togou,

Muntean

2023

IEEE Trans. Multimedia

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The exponential demand for multimedia services is one reason behind the substantial growth of mobile data traffic. Video traffic patterns have significantly changed in the past two years due to the coronavirus disease . The worldwide pandemic has caused many individuals to work from home and use various online video platforms (e.g., Zoom, Google Meet, and Microsoft Teams). As a result, overloaded macrocells are unable to ensure high Quality of Experience (QoE) to all users. Heterogeneous Networks (HetNets) consisting of small cells (femtocells) and macrocells are a promising solution to mitigate this problem. A critical challenge with the deployment of femtocells in HetNets is the interference management between Macro Base Stations (MBSs), Femto Base Stations (FBSs), and between FBS and FBS. Indeed, the dynamic deployment of femtocells can lead to co-tier interference. With the rolling out of the 5G mobile network, it becomes imperative for mobile operators to maintain network capacity and manage different types of interference. Machine Learning (ML) is considered a promising solution to many challenges in 5G HetNets. In this paper, we propose a Machine Learning Interference Classification and Offloading Scheme (MLICOS) to address the problem of co-tier interference between femtocells for video delivery. Two versions of MLICOS, namely, MLICOS1 and MLICOS2, are proposed. The former uses conventional ML classifiers while the latter employs advanced ML algorithms. Both versions of MLICOS are compared with the classic Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). The ML models are assessed based on the prediction accuracy, precision, recall and F-measure. Simulation results show that MLICOS outperforms the other schemes by providing the highest throughput and the lowest delay and packet loss ratio. A statistical analysis was also carried out to depict the degree of interference faced by users when different schemes are employed.

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SDN-Based Hierarchical Agglomerative Clustering Algorithm for Interference Mitigation in Ultra-Dense Small Cell Networks

Cited by 5 publications

References 30 publications

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey

A Comprehensive Survey on Knowledge-Defined Networking

A Machine Learning Solution for Video Delivery to Mitigate Co-Tier Interference in 5G HetNets

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