A Systematic Review of Artificial Intelligence Applications in Cellular Networks

Eli-Chukwu, Ngozi Clara; Aloh, J. M.; Ezeagwu, Christopher Ogwugwuam

doi:10.48084/etasr.2788

Cited by 8 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multilayer hierarchical structure was used in [32,33] for fault-cause couples. The collection of current criteria was classified according to the knowledge of industry professionals.…”

Section: Methodsmentioning

confidence: 99%

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

et al. 2022

View full text Add to dashboard Cite

Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

show abstract

“…A multilayer hierarchical structure was used in [32,33] for fault-cause couples. The collection of current criteria was classified according to the knowledge of industry professionals.…”

Section: Methodsmentioning

confidence: 99%

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Frequency doppler is used in advance to measure UE speed according to 3GPP [19] as shown in (1), where fs(t) are the Doppler shift, and fd is the maximum Doppler frequency, which is defined to be either 5, 70 or 300 Hz and the cosine of angle θ(t) is given in (2).…”

Section: Proposed Simulation Environmentmentioning

confidence: 99%

“…The handoff decision has to be made by multiple algorithms such as fuzzy logic and machine learning to address usability issues like latency and complete failure. A combination approach is being used to study cellular coverage and handover forecasts [2], [3]. The longterm evolution (LTE) technology provides stability for low user speeds from 0 to 15 km/h, and faster user speeds from 15 to 120 km/h, as per the 3rd generation partnership project (3GPP).…”

Section: Introductionmentioning

confidence: 99%

Real-time high-speed mobility management

Abuelgasim

Hassan

Khairi

et al. 2021

IJEECS

View full text Add to dashboard Cite

High-speed mobility system has now become a serious concern for mobile operators due to the large frameworks of a heterogeneous network made up of multiple cell types and different frequency bands. Handover (HO) is conducted in a real-life scenario when the user equipment (UE) moves from one network coverage to another by performing proper measurement with high speed. HO breakdown and call loss are observed due to a high speed; thus, high-speed mobility system needs improvement by using the UE speed as one of the key measurement monitoring criteria for the long-term evolution (LTE) network. Vendor consultation has been considered in this paper in addition to real drive test measurement in highways. Results have shown that velocity has a direct impact on the handover quality and overall timing. Results also demonstrate that 120 km/h measurement is better than 140 km/h as UE speed.

show abstract

“…To the best of our knowledge, no other comprehensive attempt to model and analyze the considered data has been reported in the literature. Previous works concentrated on limited network analyses, performed over specific technologies and with the purpose of solving precise problems, often relying on artificial intelligence techniques [ 16 ]. For instance, this has been the case with network optimization and planning tasks, such as the one in [ 17 ], where the authors propose an approach to determine groups of similarly behaving 3G cells, to support human experts in determining the state of the network; and the one in [ 18 ], where the collection of a large-scale dataset to foster mobile network planning is presented.…”

Section: Introductionmentioning

confidence: 99%

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Brunello

Torre

Gallo

et al. 2022

Sensors

View full text Add to dashboard Cite

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only.

show abstract

A Systematic Review of Artificial Intelligence Applications in Cellular Networks

Cited by 8 publications

References 45 publications

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Real-time high-speed mobility management

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Contact Info

Product

Resources

About