Unsupervised Machine Learning in 6G Networks -State-of-the-art and Future Trends

Rekkas, Vasileios P.; Sotiroudis, Sotirios P.; Sarigiannidis, Panagiotis; Karagiannidis, George K.; Goudos, Sotirios K.

doi:10.1109/mocast52088.2021.9493388

Cited by 15 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the proposed scheme requires common agreement to maintain the threat library in a distributed heterogenous network, otherwise this scheme will surface several compatibility issues. Likewise, the intelligent detection of attacks, such as jamming, malware, DoS or DDoS [95]. In [48], the authors presented a moving target defense mechanism for proactive defense against multiple attacks including MitM.…”

Section: Intrusion Detection-based Countermeasuresmentioning

confidence: 99%

Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions

et al. 2023

View full text Add to dashboard Cite

Challenges faced in network security have significantly steered the deployment timeline of Fifth Generation (5G) communication at a global level; therefore, research in Sixth Generation (6G) security analysis is profoundly necessitated. The prerogative of this paper is to present a survey on the emerging 6G cellular communication paradigm to highlight symmetry with legacy security concepts along with asymmetric innovative aspects such Artificial Intelligence (AI), Quantum Computing, Federated Learning, etc. We present a taxonomy of the threat model in 6G communication in five security legacy concepts, including Confidentiality, Integrity, Availability, Authentication and Access control (CIA3). We also suggest categorization of threat-countering techniques specific to 6G communication into three types: cryptographic methods, entity attributes and Intrusion Detection System (IDS). Thus, with this premise, we distributed the authentication techniques in eight types, including handover authentication, mutual authentication, physical layer authentication, deniable authentication, token-based authentication, certificate-based authentication, key agreement-based authentication and multi-factor authentication. We specifically suggested a series of future research directions at the conclusive edge of this survey.

show abstract

Section: Intrusion Detection-based Countermeasuresmentioning

confidence: 99%

Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions

et al. 2023

View full text Add to dashboard Cite

show abstract

“…An overview of UML applications in the domain of networking is provided in [34]. In [35], the state-of-the-art UML algorithms focusing on the 6G wireless communication systems were examined. A UML algorithm was employed to tackle an optimization problem for the user selection and power allocation optimization sub-problem in a non-orthogonal multiple access (NOMA) schemes to achieve optimal solutions [36], power control problems for device-todevice scenarios [37], and user interference [38].…”

Section: Quantum-inspired Machine Learning Approachesmentioning

confidence: 99%

Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions

Duong

Ansere

Narottama

et al. 2022

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferation growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing face a huge limited computing capabilities to support the sixthgeneration (6G) networks with highly dynamic applications and services. Under this regard, the fast developing quantum computing with machine learning (QML) for 6G networks is investigated. QML algorithms can significantly enhanced the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide comprehensive overviews through machine learning approaches for their applications in the 6G networks. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning in 6G networks are elaborated.

show abstract

“…DL is based on neural networks' architectures, using multiple layers ("deep") of artificial neurons [7]. DL has been utilized in the field of wireless communications too, introducing a data driven approach and offering new insights, such as new system's modeling [8,9] and distributed computation [10]. In this context, there is ongoing research in the applications of DL in CF M-MIMO, thus providing new insights in the current research.…”

mentioning

confidence: 99%

The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems

Iliadis

Zaharis

Sotiroudis

et al. 2022

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.

show abstract

Unsupervised Machine Learning in 6G Networks -State-of-the-art and Future Trends

Cited by 15 publications

References 24 publications

Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions

Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions

Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions

The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems

Contact Info

Product

Resources

About