Handover parameter optimization for self-organizing LTE networks

Isa, Ili Nadia Md; Baba, Mohd Dani; Yusof, Azita Laily; Rahman, Ruhani Ab

doi:10.1109/iscaie.2015.7298317

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…In the HO process, there are three main parameters discussed in the literature, according to [14]. The first of these parameters is the modification of the hysteresis, followed by the modification of time-to-trigger (TTT) and the alteration of A3 event detection in the HO process.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

et al. 2020

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Handover (HO) is designed to facilitate user mobility and ensure quality of service in mobile networks. In multiple base station (eNodeBs) scenarios, the HO priority process is a problem that has been studied in many surveys, as neglecting the use of priority-based schemes can result in high amounts of HO and, consequently, a decrease in the quality of services provided. This paper presents a Heuristic for Handover based on AHP-TOPSIS-FUZZY (H 2 ATF), which generates a priority ranking of eNodeBs from the use of (a) the analytical hierarchical process (AHP) to define the weights of the criteria; (b) the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the selected target cells; and (c) the use of an adaptive hysteresis calculated through a fuzzy inference system based on parameters that directly impact the HO process. Through this proposal, it was possible to define the best time and, together, the best antenna to perform the HO. The results demonstrate a decrease of up to 43% in HO ping pong (HPP), a widely used metric in the literature to evaluate HO heuristics. INDEX TERMS Handover, priority, heterogeneous networks, mobile networks, AHP-TOPSIS, fuzzy logic.

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Section: Related Workmentioning

confidence: 99%

“…Optimizing these parameters in the mobile network environment will lead to a reduction in the total number of HOs, HOFs, HPPs and packet loss. In addition to these research points, other studies are aimed at increasing the data rate, capabilities and costs for users [14].…”

Section: Related Workmentioning

confidence: 99%

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

et al. 2020

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“…Optimization of handover parameters has already been studied in the wireless literature (see e.g. [3], [4], [41]- [43]). However most of the literature mainly focuses on the optimization of handover performance metrics such as early handovers, late handovers or ping-pong handovers whereas the focus here is on the throughput.…”

Section: B Online Optimization Of Base Station Parametersmentioning

confidence: 99%

“…The manual tuning of base station parameters may thus be highly time consuming, tedious and needs to preserve some level of quality of service. In addition, recent developments in cellular network standards lean towards a densification of base stations, encouraging operators to find automated solutions for optimal parameters configuration (see e.g., [1]- [4]).…”

Section: Introductionmentioning

confidence: 99%

Parallel Contextual Bandits in Wireless Handover Optimization

Colin¹,

Thomas²,

Draief³

2019

Preprint

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As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual bandit framework arises as a natural candidate for such a task, its extension to a parallel setting is not straightforward: one needs to carefully adapt existing methods to fully leverage the multi-agent structure of this problem. We propose two approaches: one derived from a deterministic UCB-like method and the other relying on Thompson sampling. Thanks to its bayesian nature, the latter is intuited to better preserve the exploration-exploitation balance in the bandit batch. This is verified on toy experiments, where Thompson sampling shows robustness to the variability of the contexts. Finally, we apply both methods on a real base station network dataset and evidence that Thompson sampling outperforms both manual tuning and contextual UCB.

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“…Finally, self-healing automatically detects errors and performs recovery without downgrading the network service. Therefore, all three of these SON functions combined can overcome the handover issues [14].…”

Section: Motivationmentioning

confidence: 99%

Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

Hao¹

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<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks. In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management. Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

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Handover parameter optimization for self-organizing LTE networks

Cited by 8 publications

References 11 publications

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

Parallel Contextual Bandits in Wireless Handover Optimization

Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

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