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 7932
Special Issue Editors
Interests: reinforcement learning; federated reinforcement learning; wireless networks; network performance analysis; IP routing; IoTs; tactile internet; 5G; URLLC
Special Issues, Collections and Topics in MDPI journals
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
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Keywords
- machine learning
- 5G
- beyond 5G
- wireless engineering technology
- software-defined networking
- radio resource allocation
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