Towards Intelligent LTE Mobility Management through MME Pooling

Aqeeli, Emad; Moubayed, Abdallah; Shami, Abdallah

doi:10.1109/glocom.2014.7417804

Cited by 5 publications

(15 citation statements)

References 17 publications

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“…In addition, the authors proposed another study with lower complex linear programming model that follows the concept of previous approach for minimizing the signaling load . Two studies in Aqeeli et al discussed the TAU and paging signaling overhead with regards to TAL construction with the perspective of relating the management mobility entity with TAL. Both the studies proposed a centralized and a distributed MME scheme.…”

Section: Related Workmentioning

confidence: 99%

“…Both the studies explore the significance of having different distribution of MME to the whole system and the total signaling overhead effect. However, the first study proposed a static technique that distributes the cells to tracking are lists in fixed time. It is shown that the centralized technique offers a more optimized signaling overhead when compared with the distributed one.…”

Section: Related Workmentioning

confidence: 99%

“…The objective of this study is to minimize the total signaling overhead caused by paging and TAU. Two studies have been proposed earlier which implement various methods to minimize the total overhead. The first study is to minimize the overhead gain by using two pooling schemes namely; centralized and distributed.…”

Section: System Modelmentioning

confidence: 99%

“…The original model that was proposed in Aqeeli et al is used as a base model for both the centralized and the distributed pooling schemes. In this context, the formulation of the problem considers the centralized scheme owing to its significant performance advantage over the distributed scheme.…”

Section: System Modelmentioning

confidence: 99%

“…Recent advancements in cellular networks such as LTE and LTE‐advance offer a fast and high bandwidth connectivity delivered to the users. However, the massive growth in handheld device technology and an equally rapid increase in mobile applications have made the task daunting while introducing new obstacles such as the increasing signaling overhead and a tangible power consumption resulted in processing the baseband computational requirements . Signaling overhead is one of the vital issues that impact the cellular behavior causing a significant load in the core network, hence effecting the cellular network reliability.…”

Section: Introductionmentioning

confidence: 99%

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Optimal location management in LTE networks using evolutionary techniques

Aqeeli

Hashim

Haque

et al. 2019

Int J Communication

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Summary Wireless network is considered a vital enabler in the world of information technology, specifically, LTE and LTE advanced networks, which are the latest technologies owing to their fast speed, robustness, and large bandwidth. However, in spite of the aforementioned advancements, signaling overhead poses critical challenges in terms of network availability, especially those caused by location management messages which are related to users’ mobility behavior. This paper seeks to address the problem of signaling overhead caused by the location management messages specifically, tracking area update (TAU) and paging by deploying three evolutionary algorithms, namely particle swarm optimization (PSO), artificial bee colony (ABC), and gravitational search algorithm (GSA). The deployed algorithms guarantee yielding the minimum values of the signaling overhead for TAU, paging, and the battery power consumption of the user. It is shown that ABC‐based algorithm has faster convergence and better signaling overhead when compared with other implemented algorithms. Moreover, the measured relative standard deviation (RSD) value of all algorithms shows low uncertainty of around 1% for the objective function and 3% for the paging, TAU, and power. Hence, the three applied optimization algorithms have proven to be efficient and reliable for solving the problem in a large‐scale environment.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal location management in LTE networks using evolutionary techniques

Aqeeli

Hashim

Haque

et al. 2019

Int J Communication

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A review of mobility management entity in LTE networks: Power consumption and signaling overhead

Alsaeedy

Chong

2019

Int J Network Mgmt

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Mobility management (MM) in Long-Term Evolution (LTE) networks is a vital process to keep an individual User Equipment (UE) connected while moving within the network coverage area. MM Entity (MME) is the LTE component responsible for tracking and paging procedures and controlling the corresponding signaling between the UE and its serving cell, which is necessary for data-packet exchange. Because of the massive increase in the density of mobile UEs, MME is burdened by the high volume signaling load, especially because most of that load comes from Tracking Area Update (TAU) and Paging messages, which are essential to exchange UE-specific information with the network. To achieve cost-efficient resource provisioning, many solutions have been proposed for TAU and Paging management to optimize not only UE experience (ie, battery power consumption) but also network resources (ie, bandwidth). In this paper, we discuss various solution schemes for TAU and Paging in terms of complexity, latency, and computation costs. Also, this review discusses the adverse effects of these solutions on the LTE Key Performance Indicators (KPIs). Furthermore, we present a new trend of MM solutions in LTE networks, called software-defined network (SDN) and software-defined virtualization (SDNV).To this end, we examine the existing schemes and challenges in the literature toward next-generation wireless networks (eg, 5G, Internet-of-Things [IoT], and machine to machine [M2M] communications), and we describe user mobility models that are used to analyze the network performance.Int J Network Mgmt. 2020;30:e2088.wileyonlinelibrary.com/journal/nem market in the United States reveals that most signaling loads on the MME are caused by TAU and Paging procedures, as shown in Figure 2; cost about 34% of the total signaling load on the MME. 3 Generally, LNOs use many LTE Key Performance Indicators (KPIs) measurements, as primary indicators, to evaluate and measure the network performance to satisfy the end-user requirements according to Service Level Agreement (SLA). 12 Basically, the Paging Success Rate and TAU Success Rate are defined as the two LTE KPIs that measure how well the TAU and Paging procedures are succeeded. 13,14 Having low KPI values of Paging Success Rate and TAU Success Rate can be caused by low UE and/or network performances. Many researchers and practitioners try to mitigate the overall signaling loads on the MME by improving both the TAU and Paging procedures and the end-user experience to maintain the related KPIs at optimal values.

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