Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenge...
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doaj-657e9201aa9241c9bbc3ea39d79da9842021-01-15T00:01:00ZengMDPI AGElectronics2079-92922021-01-011016916910.3390/electronics10020169Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G NetworksSherief Hashima0Basem M. ElHalawany1Kohei Hatano2Kaishun Wu3Ehab Mahmoud Mohamed4RIKEN Advanced Intelligence Project (AIP), Fukuoka 819-0395, JapanGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, ChinaRIKEN Advanced Intelligence Project (AIP), Fukuoka 819-0395, JapanGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, ChinaCollege of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Aldwaser 11991, Saudi ArabiaDevice-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.https://www.mdpi.com/2079-9292/10/2/169D2D communicationmmWavemachine-learning applications5GB5G |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sherief Hashima Basem M. ElHalawany Kohei Hatano Kaishun Wu Ehab Mahmoud Mohamed |
spellingShingle |
Sherief Hashima Basem M. ElHalawany Kohei Hatano Kaishun Wu Ehab Mahmoud Mohamed Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks Electronics D2D communication mmWave machine-learning applications 5G B5G |
author_facet |
Sherief Hashima Basem M. ElHalawany Kohei Hatano Kaishun Wu Ehab Mahmoud Mohamed |
author_sort |
Sherief Hashima |
title |
Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks |
title_short |
Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks |
title_full |
Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks |
title_fullStr |
Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks |
title_full_unstemmed |
Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks |
title_sort |
leveraging machine-learning for d2d communications in 5g/beyond 5g networks |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-01-01 |
description |
Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS. |
topic |
D2D communication mmWave machine-learning applications 5G B5G |
url |
https://www.mdpi.com/2079-9292/10/2/169 |
work_keys_str_mv |
AT sheriefhashima leveragingmachinelearningford2dcommunicationsin5gbeyond5gnetworks AT basemmelhalawany leveragingmachinelearningford2dcommunicationsin5gbeyond5gnetworks AT koheihatano leveragingmachinelearningford2dcommunicationsin5gbeyond5gnetworks AT kaishunwu leveragingmachinelearningford2dcommunicationsin5gbeyond5gnetworks AT ehabmahmoudmohamed leveragingmachinelearningford2dcommunicationsin5gbeyond5gnetworks |
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