Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead

Alsaeedy, Alaa A. R.; Chong, Edwin K. P.

doi:10.1109/jiot.2019.2920628

Cited by 49 publications

(20 citation statements)

References 8 publications

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“…O contexto da EM no estado RRC_INACTIVE é armazenado em uma gNB âncora [11], não sendo necessário sinalização adicional com o AMF pois o contexto já é conhecido pela rede de acesso. A EM nesse estado fica no modo de espera de forma similar ao estado RRC_IDLE.…”

Section: A Gnb âNcoraunclassified

Comparação do Desempenho de Esquemas de Gerenciamento de Localização em Redes 5G

Oliveira¹,

Alencar²,

Lopes³

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-As redes móveis de quinta geração (5G) apresentam uma mudança de paradigma com relação às tecnologias anteriores, em que uma nova estratégia de gerenciamento de localização foi introduzida para permitir que os requisitos das redes 5G sejam atendidos. Este artigo apresenta o custo de sinalização do novo esquema de gerenciamento de localização proposto pelo 3GPP em redes 5G. O desempenho desse esquema é analisado e comparado com os esquemas adotadas em redes predecessoras. Os resultados encontrados confirmam que o novo esquema reduz o custo de sinalização de uma rede 5G quando comparado aos esquemas tradicionais (redes 2G, 3G e LTE).

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Section: A Gnb âNcoraunclassified

Comparação do Desempenho de Esquemas de Gerenciamento de Localização em Redes 5G

Oliveira¹,

Alencar²,

Lopes³

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…Furthermore, as we have stated before, these two procedures give rise to a trade‐off optimization problem (because of the dependency). In this context, we can calculate the total cost (for more detail, see Alsaeedy and Chong), denoted by C tot , for the combined TAU and Paging overhead, using the following expression:

C_{tot} = C_{tau} \cdot λ + false(1 + \frac{α}{σ} false) \cdot C_{pag} \cdot N_{TAL} \cdot σ,

where C tau and C pag are the relevant message overhead of TAU and Paging , respectively (detailed in Table ), N TAL is the TAL size (ie, number of eNBs in the relevant TAL, detailed in Section ), and λ is the rate of triggering TAU . As detailed in Section , the MME always triggers the required Paging procedure whenever there are incoming data packets intended for a certain UE.…”

Section: Tau and Paging Overheadmentioning

confidence: 99%

“…Moreover, as stated before, the triggering rate of TAU depends on how a specific UE moves via the network coverage area—that is, the more frequent the initiations of TAU , the more the battery power drains. In this context, we introduce the following expression to model the total battery consumption, denoted by Apwr tau (for more detail, see Alsaeedy and Chong):

{Apwr}_{tau} = {Pwr}_{tau} \cdot λ,

where Pwr tau =10 mW (the average battery consumption per TAU ) and λ is the rate of triggering TAU . From this formula, it is clear that if the UEs are very mobile, more battery power is consumed in these UEs—this adversely affects the battery lifetime of mobile UEs.…”

Section: Tau and Paging Overheadmentioning

confidence: 99%

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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“…In addition, some paging schemes for TAL-based mobility management Fig. 1 TA and TAL in the LTE network have been proposed [6,7], and research results on how to apply TAL-based registration to not only 4G, but also 5G, have recently been presented [8][9][10]. However, because of their complex procedure, these were difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

Optimal TAL-based registration with cell-based central policy in mobile cellular networks: a semi-Markov process approach

Jang

Baek

2021

J Supercomput

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LTE networks consist of tracking areas (TAs) or a group of cells, while several TAs constitute a TA list (TAL). The LTE network adopts TAL-based registration, where, if the user equipment (UE) enters a TA that is not in its current TAL, the UE registers the TA to inform the network of its new location. A central policy for TAL allocation, known as a TA-based central policy, was proposed for TAL-based registration. Under the central policy, the TA in which the UE registers its location becomes the central TA of the new TAL. This policy can lessen the possibility of the UE quickly exiting the new TAL. However, considering the actual network architecture, it makes TAL-based registration a challenge to implement. Thus to mitigate this problem, a cell-based central policy is proposed. This study investigates TAL-based registration with cell-based central policy (TbRcc) for LTE networks. TAL-based registration with cell-based central policy and single-cell TA (TbRcc1c) is also proposed to reduce the registration cost and make up the optimal TAL. Furthermore, an improved analysis model is presented to reflect the effect of the implicit registration of calls and obtain the exact cost. Comparing the performance of the proposed scheme with those of classical TAL-based registration and distance-based registration, the performance of the proposed scheme is shown to improve. The results of this study can help research that addresses the mobility management of next-generation networks, as well as LTE networks.

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Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead

Cited by 49 publications

References 8 publications

Comparação do Desempenho de Esquemas de Gerenciamento de Localização em Redes 5G

Comparação do Desempenho de Esquemas de Gerenciamento de Localização em Redes 5G

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

Optimal TAL-based registration with cell-based central policy in mobile cellular networks: a semi-Markov process approach

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