A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

Liu, Qianyu; Kwong, Chiew Foong; Zhang, Sibo; Li, Lincan; Wang, Jing

doi:10.1007/s11276-019-02130-3

Cited by 22 publications

(36 citation statements)

References 24 publications

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“…A cell selection scheme is developed by Liu et al in [36] that achieves the selection task based on multiple attributes decision-making (MADM) and fuzzy logic algorithms. The selection of the serving cell is taken based on many factors which are SINR, reference signal receiving power (RSRP), and jitter.…”

Section: A Non Ml-based Cell Selection Strategiesmentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Alablani

Arafah²

2023

IEEE Access

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Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

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Section: A Non Ml-based Cell Selection Strategiesmentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Alablani

Arafah²

2023

IEEE Access

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“…The two types were the sleeping cell caused by very low user requests and the too busy cells caused by too many requests that cause more demand for resource allocation. In [61], researchers proposed a method that combines fuzzy logic and multiple attribute decision algorithm (MADM) to use historical data and find the optimum target. As a result, the generated model triggers the HO at the right time rather than being late or dismissed.…”

Section: ) Dynamic Hand Over (Ho) Managementmentioning

confidence: 99%

Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey

2022

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Open RAN is an emerging vision and an advancement of the Radio Access Network (RAN). Its purpose is to implement a vendor and network-generation agnostic RAN, provide networking solutions across all service requests, and implement artificial intelligence solutions in different stages of an end-to-end communication path. The 5th Generation (5G) and beyond the 5th Generation (B5G) of networking introduce and support new use cases, such as tactile internet and autonomous driving. The complexity and innovative nature of these use cases require continuous innovation at a high pace in the RAN. The traditional approach of building end-to-end RAN solutions by only one vendor hampers the speed of innovation-furthermore, the lack of a standard approach to implementing artificial intelligence complicates the compatibility of products with the RAN ecosystem. O-RAN Alliance, a community of industry and academic experts in RAN, works on writing Open RAN specifications on top of the 3rd Generation Partnership Project (3GPP) standards. Founded on these specifications, the aim of this paper is to introduce open research topics in Open RAN that overlap the interests of both AI and telecommunication researchers. The paper provides an overview of the architecture and components of Open RAN, then explores AI use cases in Open RAN. Also, this survey includes some plausible AI deployment scenarios that the specifications have not covered. Open RAN in future cities creates opportunities for various use cases across different sectors, including engineering, operations, and research that this paper addresses.

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“…While not highly complex, this clustering method has been implemented several times and proven to work well. It was used most recently in the research efforts within 5G ultra-dense networks [24]. The main premise is segmenting user space to ensures the distance between the candidate clusters is significant by raising the Squash factor related to the overall user space and adjusting the Accept/Reject ratios based on the average mobility index of the users occupying each edge space.…”

Section: User Solution Spacementioning

confidence: 99%

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

Aleyadeh

Moubayed

Heidari

et al. 2022

IEEE Open J. Commun. Soc.

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End-users and service providers have recently favored lower latency services. Edge computing has improved user quality of service (QoS) guarantees through the reduced geographical distance, decreased use of the network backbone, and flexible placement of the container hosting edge devices. The under-utilized isolated edge computing idling nodes are significant for service providers, especially for Internet of Things (IoT) applications. However, the nodes' minimal maintenance remains a hindrance due to related increased failures. Orchestrating over the edge alongside core environments allows tolerant services and more demanding ones to coexist without impacting the user experience. Therefore, the orchestrator's second priority is achieving and maintaining the QoS through optimal recovery method selection by either migrating the live containers or re-instantiating them. This paper proposes an Optimal Container Migration/Re-Instantiation (OC-MRI) model to optimize the orchestration methods focusing on downtime, container dependencies, and latency requirements. Next, we introduce a real-time heuristic-based solution, Edge Computingenabled Container Migration/Re-Instantiation (EC2-MRI). Both models are bench-marked alongside staple greedy approaches. Simulation results showcase the lowest latencies and downtime with the OC-MRI model. Furthermore, the EC2-MRI model shows comparable results to the optimal model with minimal lag.

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A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

Cited by 22 publications

References 24 publications

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey

Optimal Container Migration/Re-Instantiation in Hybrid Computing Environments

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