Dynamic SON-Enabled Location Management in LTE Networks

Aqeeli, Emad; Moubayed, Abdallah; Shami, Abdallah

doi:10.1109/tmc.2017.2775221

Cited by 11 publications

(18 citation statements)

References 25 publications

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“…An important issue to consider is that the inter-list handover resides on three factors: UEs, probability of the user to move between cells, and the cost of inter-MME. A careful look at the aforementioned discussion reveals a bottleneck effect between the total signaling cost of TAU and paging from one side, and the total cost of the inter-list handover from the other side [5,6]. Thus, the location management addressed in this work is NP-hard, and locating approximate solution is NP-hard.…”

Section: Introductionmentioning

confidence: 94%

“…The main difference between centralized scheme and distributed one is the method of calculating TAU. According to the result of comparison between the two schemes, distributed scheme requires extra signal load from MME relocation, thus, it can be stated that centralized scheme is more efficient than the distributed scheme [5,6]. Therefore, the problem considered in this section utilizes a centralized MME pooling scheme [5,14].…”

Section: Problem Formulationmentioning

confidence: 99%

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Location management in LTE networks using multi-objective particle swarm optimization

Hashim

Abido

2019

Computer Networks

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Long-term evolution (LTE) and LTE-advance (LTE-A) are widely used efficient network technologies serving billions of users, since they are featured with high spectrum efficiency, less latency, and higher bandwidth. Despite remarkable advantages offered by these technologies, signaling overhead remains a major issue in accessing the network. In particular, the load of signaling is mainly attributed to location management. This paper proposes an efficient approach for minimizing the total signaling overhead of location management in LTE networks using multi-objective particle swarm optimization (MOPSO). Tracking area update (TAU) and paging are considered to be the main elements of the signaling overhead of optimal location management in LTE. In addition, the total inter-list handover contributes significantly to the total signaling overhead. However, the total signaling cost of TAU and paging is adversely related to the total inter-list handover. Hence, two cost functions should be minimized where the first function is the total signaling cost of TAU and paging and the second cost function is the total signaling overhead. The trade-off between these two objectives can be circumvented by MOPSO, which alleviates the total signaling overhead. A set of non-dominated solutions on the Pareto-optimal front is defined and the best compromise solution is presented. The proposed algorithm results in a feasible compromise solution between the two objectives, minimizing the signaling overhead, and in turn, the consumption of the power battery of the user. The efficacy and the robustness of the proposed algorithm have been proven through a large scale environment problem illustrative example. The location management in LTE networks using MOPSO best compromise solution has been compared to the results obtained by a mixed integer non-linear programming (MINLP) algorithm.

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Section: Introductionmentioning

confidence: 94%

Section: Problem Formulationmentioning

confidence: 99%

Location management in LTE networks using multi-objective particle swarm optimization

Hashim

Abido

2019

Computer Networks

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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%

“…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%

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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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