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Machine Learning-Enabled Radio Resource Allocation for Sustainability of Wireless Engineering Technologies

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 6825

Special Issue Editors


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Guest Editor
Research Fellow, Department of Information and Communication Technology, University of Agder, Kristiansand, Norway
Interests: Cognitive radio networks; 5G systems; Internet of Things; URLLC; machine learning; network performability

Special Issue Information

Dear Colleagues,

An efficient radio resource allocation (RRA) in wireless engineering technologies (WET), such as 5G and Beyond 5G (B5G), is a vital challenge comprising various wireless network functionalities. The 5G/B5G architecture governing RRA in existing radio access networks (RAN) results due to incremental engineering, with novel RRA techniques continuously being enhanced to pursue the technology evolution. While RRA techniques have accelerated the rapid advancement of the conventional 3GPP LTE system, after a decade, WET development leads to an ever more shared RRA architecture established on an ever-growing number of parameters.

The complexity of RRA techniques differs on the dimensionality of the current challenges and the available execution delays. It is anticipated that RRA is expected to reach extraordinary complexity with the 5G/B5G WET, which will introduce new technology components, such as massive MIMO, mmWave communication, end-to-end network slicing, next-generation vehicular to everything (gV2X) networks, software-defined networks (SDNs), edge/fog computing, and broader licensed/unlicensed radio spectrums. Thus, enhancing such massive RRA issues and challenges with traditional state-of-the-art mechanisms is especially challenging for 5G/B5G WET sustainability.

Recently, there has been an increasing trend of fusing machine learning (ML) with every technology to build intelligent systems. In RRA approaches, wireless channel access control policies and algorithms can be programmed as ML-enabled smart mechanisms. ML helps to optimize and adjust RRA parameters dynamically. A general-purpose ML framework capable of autonomously generating algorithms specialized in RRA functionality in WET sustainability is required.

Therefore, in this Special Issue, we aim to focus on the most recent advances in ML research areas encompassing the RRA in the 5G/5BG WET sustainability domain. This Special Issue will bring together researchers from diverse fields and specialization, such as communication engineering, computer engineering, computer science, information technology, statistics, and mathematics. We invite researchers from industry, academia, and government organization to discuss challenging ideas and novel research contributions, demonstrate results, and share standardization efforts on the RRA approaches for 5G/B5G WET sustainability and related areas.

This SI will bring together publications that address the various heterogeneity and representation challenges identified above. Topics of interest include but are not limited to:

  • Radio access networks’ sustainability:
    • ML-enabled RRA approaches for 5G WET;
    • ML-enabled RRA approaches for B5G WET;
    • ML-enabled RRA approaches for unlicensed spectrum WET;
    • ML-enabled RRA approaches for shared spectrum WET (LTE-A, LTE-LAA, LWA, etc.);
  • ML-enabled end-to-end network slicing for WET sustainability;
  • ML-enabled next-generation V2X WET sustainability;
  • ML-enabled software-defined networks sustainability:
    • ML-enabled SDN frameworks;
    • ML-enabled edge/fog computing sustainability.

Dr. Rashid Ali
Dr. Indika A. M. Balapuwaduge
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 submissions that pass pre-check are 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. Sustainability 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 2400 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

  • machine learning
  • 5G
  • beyond 5G
  • wireless engineering technology
  • software-defined networking
  • radio resource allocation

Published Papers (2 papers)

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12 pages, 2180 KiB  
Article
Deep Learning-Based Multiparametric Predictions for IoT
by Muhammad Ateeq, Muhammad Khalil Afzal, Muhammad Naeem, Muhammad Shafiq and Jin-Ghoo Choi
Sustainability 2020, 12(18), 7752; https://doi.org/10.3390/su12187752 - 19 Sep 2020
Cited by 6 | Viewed by 2797
Abstract
Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to [...] Read more.
Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96% and 98%, respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system. Full article
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Review

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26 pages, 1746 KiB  
Review
A Survey of Context-Aware Messaging-Addressing for Sustainable Internet of Things (IoT)
by Alaa Omran Almagrabi and Yasser D. Al-Otaibi
Sustainability 2020, 12(10), 4105; https://doi.org/10.3390/su12104105 - 18 May 2020
Cited by 5 | Viewed by 3148
Abstract
Nowadays, communication engineering technology is merging with the Internet of Things (IoT), which consists of numerous connected devices (referred to as things) around the world. Many researchers have shown significant growth of sensor deployments for multiple smart engineering technologies, such as smart-healthcare, smart-industries, [...] Read more.
Nowadays, communication engineering technology is merging with the Internet of Things (IoT), which consists of numerous connected devices (referred to as things) around the world. Many researchers have shown significant growth of sensor deployments for multiple smart engineering technologies, such as smart-healthcare, smart-industries, smart-cities, and smart-transportation, etc. In such intelligent engineering technologies, sensors continuously generate a bunch of messages in the network. To enhance the value of the data in the messages, we must know the actuality of the data embedded inside the messages. For this purpose, the contextual information of the data creates a vital challenge. Recently, context-aware computing has emerged to be fruitful in dealing with sensor information. In the ubiquitous computing domain, location is commonly considered one of the most essential sources of context. However, whenever users or applications are concerned with objects, and their site or spatial relationships, location models or spatial models are necessary to form a model of the environment. This paper investigates the area of context-aware messaging and addressing services in diverse IoT applications. The paper examines the notion of context and the use of context within the data exchanged by the sensors in an IoT application for messaging and addressing purposes. Based on the importance and need for context of the information, we identify three critical categories of new IoT applications for context-aware messaging and addressing services: emergency applications, applications for guiding and reminding, and social networking applications. For this purpose, a representative range of systems is reviewed according to the application type, the technology being used, their architecture, the context information, and the services they provide. This survey assists the work of defining an approach for context-aware messaging services domain by discovering the area of context-aware messaging. Full article
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