A distributed AI/ML framework for D2D Transmission Mode Selection in 5G and beyond

Ioannou, Iacovos; Christophorou, Chrıstophoros; Vassiliou, Vasos; Pitsillides, Andreas

doi:10.1016/j.comnet.2022.108964

“…However, it is challenging for the current conventional resource allocation approaches in radio networks to assure such various QoS requirements, particularly the URLLC requirements. Compared with the resource allocation in conventional cellular networks, transmission mode selection is essential to address as well due to the coupling with the utilization of wireless resources [235] and, meanwhile, cope with the dynamics of the wireless propagation environment both turn into a further challenging issue. Particularly, from the viewpoint of optimization, the transmission mode selection issues are typically used in various traditional optimization algorithms, i.e., water filling algorithm [229], heuristic algorithm, bisection algorithm, and NP-hard [233], [236].…”

Section: Communication Mode Selection Mechanismmentioning

confidence: 99%

“…Actually, the training information is constantly dispersed at UEs and infeasible to be uploaded due to limited bandwidth, traffic delay, and confidentiality factors. Initially, in contrast to the supposition in [235], the time-changing fast fading radio channel is constantly unknown at UEs according to the high dynamic wireless propagation environment. Finally, imperfect native training information on every UE limits robust learning of the DRL scheme, and improper clusters might radically worsen the network performance.…”

Section: Communication Mode Selection Mechanismmentioning

confidence: 99%

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Das¹,

Mudi²,

Rahman³

et al. 2023

Preprint

1

0

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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“…However, it is challenging for the current conventional resource allocation approaches in radio networks to assure such various QoS requirements, particularly the URLLC requirements. Compared with the resource allocation in conventional cellular networks, transmission mode selection is essential to address as well due to the coupling with the utilization of wireless resources [229] and, meanwhile, cope with the dynamics of the wireless propagation environment both turn into a further challenging issue. Particularly, from the viewpoint of optimization, the transmission mode selection issues are typically used in various traditional optimization algorithms, i.e., water filling algorithm [223], heuristic algorithm, bisection algorithm, and NP-hard [227], [230].…”

Section: Communication Mode Selection Mechanismmentioning

confidence: 99%

“…Actually, the training information is constantly dispersed at UEs and infeasible to be uploaded due to limited bandwidth, traffic delay, and confidentiality factors. Initially, in contrast to the supposition in [229], the time-changing fast fading radio channel is constantly unknown at UEs according to the high dynamic wireless propagation environment. Finally, imperfect native training information on every UE limits robust learning of the DRL scheme, and improper clusters might radically worsen the network performance.…”

Section: Communication Mode Selection Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

Das,

Buragohain,

Nandi

2024

Computer Networks

0

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A distributed AI/ML framework for D2D Transmission Mode Selection in 5G and beyond

Cited by 8 publications

References 26 publications

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Federated Reinforcement Learning for 6G Wireless Networks: Fundamentals, Challenges and Future Research Trends

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

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