Applied Sciences

Research

16 pages, 5531 KiB

Open AccessArticle

Machine Learning-Based Paging Enhancement in 5G Network

by Wan-Kyu Choi and Jae-Young Pyun

Appl. Sci. 2022, 12(19), 9555; https://doi.org/10.3390/app12199555 - 23 Sep 2022

Cited by 1 | Viewed by 3047

Recently, technologies such as big data, artificial intelligence, and machine learning have been applied to intelligently and effectively operate fourth-generation (4G) and fifth-generation (5G) network systems. In particular, we are interested in using them in 4G mobility management entities and 5G access and [...] Read more.

Recently, technologies such as big data, artificial intelligence, and machine learning have been applied to intelligently and effectively operate fourth-generation (4G) and fifth-generation (5G) network systems. In particular, we are interested in using them in 4G mobility management entities and 5G access and mobility management functions (AMFs), where functional enhancement or performance improvement is required. This paper presents an enhanced paging approach based on supervised machine learning and a Markov process for the performance improvement of paging in 5G AMFs. User equipment (UE) profile information in 5G AMFs classifies subscribers into two types using a UE classifier model with k-nearest neighbors (KNN)-supervised learning. In this paper, UE movement data between next-generation NodeBs (gNodeBs) are analyzed, and the Markov process is applied to construct a transition probabilistic model. When a UE moves to an adjacent gNodeB in the 5G connection management-idle state, a method for predicting the gNodeB movement is required to perform paging effectively on the predicted gNodeBs. In the proposed paging method, the AMF applies the UE profile information to the KNN-supervised learning model and classifies the subscriber UE type. In addition, on the UE movement between gNodeBs statistics, it generates state-transition probabilities and then performs paging on the gNodeB list. Experimentally, the paging responses and signals of the proposed method are compared with the existing paging methods and presented with the result that the UE location is identified using the recently visited gNodeB list in the tracking area of the AMF. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

14 pages, 3004 KiB

Open AccessArticle

Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network

by Zagdsuren Tumurkhuyag and Jang Hyun Baek

Appl. Sci. 2022, 12(18), 9173; https://doi.org/10.3390/app12189173 - 13 Sep 2022

Viewed by 1432

Abstract

In this study, we have considered zone-based registration (ZBR), which is the most widely adopted type in mobile communication networks. Based on a performance comparison between one-zone-based registration (1ZR) and two-zone-based registration (2ZR), 2ZR is known to be superior to 1ZR in most [...] Read more.

In this study, we have considered zone-based registration (ZBR), which is the most widely adopted type in mobile communication networks. Based on a performance comparison between one-zone-based registration (1ZR) and two-zone-based registration (2ZR), 2ZR is known to be superior to 1ZR in most cases. However, the existing studies on the comparison of 1ZR and 2ZR have a critical problem: The basic assumption in 1ZR is that the entire zone is paged simultaneously when a call arrives, whereas, in 2ZR, the zone is paged by two steps. With these different paging schemes, a proper comparison cannot be made. Therefore, in this study, we analyzed the performance of 1ZR by adopting 2-step paging (2SP) such as 2ZR for a proper performance comparison under equivalent conditions. This study also presents an analytical model of assigning paging areas under 2SP in 1ZR when the zone consists of multiple cells. Considering the mobility characteristics assumed in the previous studies on 2ZR, we have presented the mobility model for movement among cells in a zone, obtained the steady-state probability of each cell by using the Markov chain model to calculate the paging cost under 2SP, and ultimately calculated the total signaling cost. Through various numerical results, it was observed that, when 1ZR also adopts 2SP such as 2ZR, 1ZR can be superior to 2ZR in many cases. In conclusion, by adopting 2SP in both 1ZR and 2ZR, it would be possible to reduce the total signaling cost by selecting the better 1ZR and 2ZR while considering traffic changes. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

12 pages, 1238 KiB

Open AccessArticle

5G Based on MNOs for Critical Railway Signalling Services: Future Railway Mobile Communication System

by Ana González-Plaza, Rafael Gutiérrez Cantarero, Rafael B. Arancibia Banda and César Briso Rodríguez

Appl. Sci. 2022, 12(18), 9003; https://doi.org/10.3390/app12189003 - 8 Sep 2022

Cited by 10 | Viewed by 3274

Abstract

This paper describes the prototype and tests carried out to demonstrate the feasibility of implementing 4G/5G systems based on mobile network operators (MNOs) to transmit signalling critical data for railway systems as part of a possible solution for the Future Railway Mobile Communication [...] Read more.

This paper describes the prototype and tests carried out to demonstrate the feasibility of implementing 4G/5G systems based on mobile network operators (MNOs) to transmit signalling critical data for railway systems as part of a possible solution for the Future Railway Mobile Communication System (FRMCS). This communication system design is performed from a protocol-stack perspective, introducing the KPIs for the physical layer (RSRP, RSRQ and SINR), network layer (latency and jitter) and application layer (signalling communication timeout). The analysis focuses on the characterisation of signalling critical railway data to assess the data traffic behaviour in a telecommunications laboratory. Finally, the study validates the approach in a realistic railway environment. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

18 pages, 3454 KiB

Open AccessArticle

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

by Yosvany Hervis Santana, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, Wout Joseph and David Plets

Appl. Sci. 2022, 12(8), 3923; https://doi.org/10.3390/app12083923 - 13 Apr 2022

Cited by 22 | Viewed by 3715

Abstract

Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss [...] Read more.

Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

16 pages, 5945 KiB

Open AccessArticle

Compact and Highly Sensitive Bended Microwave Liquid Sensor Based on a Metamaterial Complementary Split-Ring Resonator

by Said Mosbah, Chemseddine Zebiri, Djamel Sayad, Issa Elfergani, Mohamed Lamine Bouknia, Samira Mekki, Rami Zegadi, Merih Palandoken, Jonathan Rodriguez and Raed A. Abd-Alhameed

Appl. Sci. 2022, 12(4), 2144; https://doi.org/10.3390/app12042144 - 18 Feb 2022

Cited by 21 | Viewed by 3268

Abstract

In this paper, we present the design of a compact and highly sensitive microwave sensor based on a metamaterial complementary split-ring resonator (CSRR), for liquid characterization at microwave frequencies. The design consists of a two-port microstrip-fed rectangular patch resonating structure printed on a [...] Read more.

In this paper, we present the design of a compact and highly sensitive microwave sensor based on a metamaterial complementary split-ring resonator (CSRR), for liquid characterization at microwave frequencies. The design consists of a two-port microstrip-fed rectangular patch resonating structure printed on a 20 × 28 mm² Roger RO3035 substrate with a thickness of 0.75 mm, a relative permittivity of 3.5, and a loss tangent of 0.0015. A CSRR is etched on the ground plane for the purpose of sensor miniaturization. The investigated liquid sample is put in a capillary glass tube lying parallel to the surface of the sensor. The parallel placement of the liquid test tube makes the design twice as efficient as a normal one in terms of sensitivity and Q factor. By bending the proposed structure, further enhancements of the sensor design can be obtained. These changes result in a shift in the resonant frequency and Q factor of the sensor. Hence, we could improve the sensitivity 10-fold compared to the flat structure. Subsequently, two configurations of sensors were designed and tested using CST simulation software, validated using HFSS simulation software, and compared to structures available in the literature, obtaining good agreement. A prototype of the flat configuration was fabricated and experimentally tested. Simulation results were found to be in good agreement with the experiments. The proposed devices exhibit the advantage of exploring multiple rapid and easy measurements using different test tubes, making the measurement faster, easier, and more cost-effective; therefore, the proposed high-sensitivity sensors are ideal candidates for various sensing applications. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

21 pages, 12941 KiB

Open AccessArticle

Hybrid Network–Spatial Clustering for Optimizing 5G Mobile Networks

by Aristotelis Margaris, Ioannis Filippas and Kostas Tsagkaris

Appl. Sci. 2022, 12(3), 1203; https://doi.org/10.3390/app12031203 - 24 Jan 2022

Cited by 8 | Viewed by 2925

Abstract

5G is the new generation of 3GPP-based cellular communications that provides remarkable connectivity capabilities and extreme network performance to mobile network operators and cellular users worldwide. The rollout process of a new capacity layer (cell) on top of the existing previous cellular technologies [...] Read more.

5G is the new generation of 3GPP-based cellular communications that provides remarkable connectivity capabilities and extreme network performance to mobile network operators and cellular users worldwide. The rollout process of a new capacity layer (cell) on top of the existing previous cellular technologies is a complex process that requires time and manual effort from radio planning-engineering teams and parameter optimization teams. When it comes to optimum configuration of the 5G gNB cell parameters, the maximization of achieved coverage (RSRP) and quality (SINR) of the served mobile terminals are of high importance for achieving the very high data transmission rates expected in 5G. This process strongly relies on network measurements that can be even more insightful when mobile terminal localization information is present. This information can be generated by modern algorithmic techniques that act on the cellular network signaling measurements. Configuration algorithms can then use these measurements combined with location information to optimize various cell deployment parameters such as cell azimuth. Furthermore, data-driven approaches are shown in the literature to outperform traditional, model-based algorithms as they can automate the optimization of parameters while specializing in the characteristics of each individual geographical zone. In the context of the above, in this paper, we tested the automated network reconfiguration schemes based on unsupervised learning and applied statistics for cell azimuth steering. We compared network metric clustering and geospatial clustering to be used as our baseline algorithms that are based on K-means with the proposed scheme—hybrid network and spatial clustering based on hierarchical DBSCAN. Each of these algorithms used data generated by an initial scenario to produce cell re-configuration actions and their performance was then evaluated on a validated simulation platform to capture the impact of each set of gNB reconfiguration actions. Our performance evaluation methodology was based on statistical distribution analysis for RSRP and SINR metrics for the reference scenario as well as for each reconfiguration scheme. It is shown that while both baseline algorithms improved the overall performance of the network, the proposed hybrid network–spatial scheme greatly outperformed them in all statistical criteria that were evaluated, making it a better candidate for the optimization of 5G capacity layers in modern urban environments. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

19 pages, 5134 KiB

Open AccessArticle

5G/B5G Service Classification Using Supervised Learning

by Jorge E. Preciado-Velasco, Joan D. Gonzalez-Franco, Caridad E. Anias-Calderon, Juan I. Nieto-Hipolito and Raul Rivera-Rodriguez

Appl. Sci. 2021, 11(11), 4942; https://doi.org/10.3390/app11114942 - 27 May 2021

Cited by 18 | Viewed by 3650

Abstract

The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification [...] Read more.

The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

16 pages, 2661 KiB

Open AccessArticle

Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks

by Osama Abuajwa, Mardeni Bin Roslee and Zubaida Binti Yusoff

Appl. Sci. 2021, 11(10), 4592; https://doi.org/10.3390/app11104592 - 18 May 2021

Cited by 30 | Viewed by 2998

Abstract

In this work, we investigate resource allocation and user pairing to improve the system’s Throughput for the downlink non-orthogonal multiple access (NOMA)-based 5G networks. The proposed resource allocation involves user pairing, subchannel power allocation, and proportional power allocation among the multiplexed users. The [...] Read more.

In this work, we investigate resource allocation and user pairing to improve the system’s Throughput for the downlink non-orthogonal multiple access (NOMA)-based 5G networks. The proposed resource allocation involves user pairing, subchannel power allocation, and proportional power allocation among the multiplexed users. The resource allocation is a non-deterministic polynomial (NP-hard) problem that is difficult to tackle throughput maximization. The user pairing and power allocation are coupled to address the substantial requirements of the NOMA system. The NOMA system requires an efficient deployment of resource allocation techniques to enhance the system’s throughput performance. In this work, we propose simulated annealing (SA) to optimize the power allocation and perform user pairing to maximize the throughput for the NOMA system. Also, we provide mathematical proof on the near-optimal solution for subchannel power and mathematical analysis on the optimal value of the power ratio for the multiplexed users in the NOMA system. The SA provides a significant throughput performance that increases by 7% compared to the existing numerical optimization methods. Results obtained show that SA performs with sufficient reliability and low time complexity in terms of Throughput improvement. Full article

(This article belongs to the Special Issue 5G Network Planning and Design)

► Show Figures

Figure 1

Journal Menu

Journal Browser

5G Network Planning and Design

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI