Special Issue "Radio Access Network Planning and Management"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 20 June 2021.

Special Issue Editors

Prof. Dr. Pablo Muñoz Luengo
E-Mail Website
Guest Editor
Research Center on Information and Communication Technologies, University of Granada, Granada, Spain
Interests: 5G mobile communication; RAN planning and management; RAN optimization; RAN fault management; wireless technologies and networks; radio resource management; self-organizing networks; network slicing; network softwarization and virtualization; artificial-intelligence-based radio resource management; big data and network data analytics; spectrum sharing
Prof. Dr. Isabel de la Bandera Cascales
E-Mail Website
Guest Editor
Communications Engineering Department, University of Málaga, Málaga, Spain
Interests: mobile communications networks; 5G networks; data analytics; fault management; radio access network optimization; machine learning application for network management; self-organizing networks; proactive network management

Special Issue Information

Dear Colleagues,

The advent of new broadband services and the Internet of Things era is leading to a revolutionary shift in the way mobile networks are managed. One of the biggest challenges is how to meet the stringent requirements for extremely high capacity density and extremely low latency in future radio access networks (RANs). 5G and beyond networks are envisioned to be much denser in terms of the number of access points and users, making network planning an arduous task with a continuous increase in CAPEX.

Moreover, mobile operators need to effectively deal with the complexity of operation of a multi-vendor, multi-technology, and softwarized RAN, while keeping OPEX and CAPEX as low as possible. As mobile operators migrate from the current physical networks to hybrid networks that support virtualized functions, capacity challenges grow significantly. Interoperability with legacy technologies, the use of optical wireless communication technologies such as Li-Fi, and new paradigms such as O-RAN, network slicing, and mobile edge computing will mitigate the cost of 5G and beyond network deployments.

Artificial intelligence (AI), machine learning (ML), and big data will also play an indisputable role in RAN planning and management. The paradigm of self-organizing networks (SONs) introduced in 4G networks needs to be evolved towards a set of disruptive technologies that leverage the unprecedented levels of computational capacity and take advantage of end-to-end intelligence. Network management is foreseen to support a flexible architecture that brings intelligence from centralized computing facilities to end terminals, going one step beyond the classification and prediction tasks that have been considered so far. The use of deep learning techniques for data analytics and metric cross-correlation will facilitate multi-objective, holistic optimization as well as near-real-time anomaly detection, enabling proactive resolution and root cause analysis, which are missing in current SON approaches.

This Special Issue aims at collecting contributions concerning planning, optimization, configuration, and fault management related to 5G and beyond RANs. Potential authors are invited to submit original research articles and review papers on the topics covered in this Special Issue, which include but are not limited to the following:

  • ML and big data for RAN management and orchestration;
  • Self-organizing networks evolution towards intelligent RANs;
  • AI/ML-driven coverage, capacity, and frequency planning;
  • Data-driven fault detection, diagnosis, and prediction;
  • Deep and reinforcement learning for RAN planning;
  • AI/ML for cloud-RAN planning and resource management;
  • Management of softwarized and virtualized RAN;
  • Capacity planning for mobile edge computing;
  • Open-source RAN planning and optimization tools;
  • Planning and management of non-public networks
  • Indoor network planning and small cell deployments;
  • Network access backhaul integration and planning;
  • RAN topology design and optimization at different layers;
  • Planning methods and tools for ultra-dense networks;
  • RAN slicing optimization and intelligent brokering mechanisms;
  • Advanced spectrum planning and management;
  • RAN management automation using geo-localized data;
  • Innovative architectures for intelligent RAN management;
  • Multi-tenant, multi-vendor, multi-technology RAN management;
  • Intent and policy-based management for intelligent RANs;
  • End-to-end performance evaluation for RAN management;
  • Economic aspects of RAN planning and operations.

Prof. Dr. Pablo Muñoz Luengo
Prof. Dr. Isabel de la Bandera Cascales
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Radio resource management
  • Self-organizing networks
  • RAN dimensioning and planning
  • RAN optimization RAN fault management
  • AI-driven radio network management
  • Ultra-dense networks
  • Small cells deployments
  • RAN slicing RAN sharing
  • Radio network data analytics
  • Spectrum planning

Published Papers (5 papers)

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Research

Article
Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series
Electronics 2021, 10(10), 1151; https://doi.org/10.3390/electronics10101151 - 12 May 2021
Viewed by 301
Abstract
Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks [...] Read more.
Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient. Full article
(This article belongs to the Special Issue Radio Access Network Planning and Management)
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Article
Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
Electronics 2021, 10(7), 829; https://doi.org/10.3390/electronics10070829 - 31 Mar 2021
Viewed by 288
Abstract
In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a [...] Read more.
In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services. Full article
(This article belongs to the Special Issue Radio Access Network Planning and Management)
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Article
Traffic Steering for eMBB in Multi-Connectivity Scenarios
Electronics 2020, 9(12), 2063; https://doi.org/10.3390/electronics9122063 - 03 Dec 2020
Viewed by 569
Abstract
Multi-connectivity (MC) is one of the most important features to be introduced in 5G networks, allowing User Equipment (UE) to simultaneously aggregate radio resources from several network nodes to enhance both data rates and reliability. Thus, this feature enables a further flexibility in [...] Read more.
Multi-connectivity (MC) is one of the most important features to be introduced in 5G networks, allowing User Equipment (UE) to simultaneously aggregate radio resources from several network nodes to enhance both data rates and reliability. Thus, this feature enables a further flexibility in the allocation of resources to the UEs in order to fulfil the users’ requirements in more complex 5G scenarios. This paper takes advantage of this wide flexibility to present a traffic steering approach that determines the amount of traffic to be held by each of the serving nodes in a multi-connectivity scenario. In this sense, the proposed technique is based on network and UE performance metrics in order to maximize the users’ perceived quality of experience (QoE) for enhanced Mobile Broadband (eMBB) services. It is then compared with a homogeneous traffic split among the serving nodes, with a single-connectivity approach and with state-of-the-art solutions. The benefits are analysed in terms of throughput and Mean Opinion Score (MOS), which is the main QoE metric. The analysis shows that a noticeable UE throughput improvement is reached when the proposed traffic steering method is applied. Consequently, this enhancement is noticed in the users’ QoE, which can lead to a reduction of operating expenses (OPEX) of the network. Full article
(This article belongs to the Special Issue Radio Access Network Planning and Management)
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Article
Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks
Electronics 2020, 9(10), 1710; https://doi.org/10.3390/electronics9101710 - 18 Oct 2020
Cited by 2 | Viewed by 1185
Abstract
The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization [...] Read more.
The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency. Full article
(This article belongs to the Special Issue Radio Access Network Planning and Management)
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Article
Backhaul-Aware Dimensioning and Planning of Millimeter-Wave Small Cell Networks
Electronics 2020, 9(9), 1429; https://doi.org/10.3390/electronics9091429 - 02 Sep 2020
Viewed by 886
Abstract
The massive deployment of Small Cells (SCs) is increasingly being adopted by mobile operators to face the exponentially growing traffic demand. Using the millimeter-wave (mmWave) band in the access and backhaul networks will be key to provide the capacity that meets such demand. [...] Read more.
The massive deployment of Small Cells (SCs) is increasingly being adopted by mobile operators to face the exponentially growing traffic demand. Using the millimeter-wave (mmWave) band in the access and backhaul networks will be key to provide the capacity that meets such demand. However, dimensioning and planning have become complex tasks, because the capacity requirements for mmWave links can significantly vary with the SC location. In this work, we address the problem of SC planning considering the backhaul constraints, assuming that a line-of-sight (LOS) between the nodes is required to reliably support the traffic demand. Such a LOS condition reduces the set of potential site locations. Simulation results show that, under certain conditions, the proposed algorithm is effective in finding solutions and strongly efficient in computational cost when compared to exhaustive search approaches. Full article
(This article belongs to the Special Issue Radio Access Network Planning and Management)
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