Touch-Interfacing Middleware Network Design in 6G

Gupta, Mantisha; Jha, Rakesh Kumar; Sabraj, Manish

doi:10.1109/comsnets56262.2023.10041291

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Distribution distributes information via unidirectional links, cellular consists of multiple cell layers, hotspots for high data rate applications, and personal networks for short-range direct communication. The article further explains the network entities and channel modelling in the 6G communication systems, which also include the control plane and user plane latency [52]. Control plane latency is the idle-to-active time, omitting forward link, paging, and wireline networking signal delays.…”

Section: B Interfacing Existing Network With Other Access Systemsmentioning

confidence: 99%

Security Challenges in 6G Networks: A Game Theory-based Intrusion Detection with Network Slicing and AI/ML Perspectives

Gupta,

Jha,

Sabraj

2024

Preprint

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The vast network of interconnected devices of the Internet of Things poses significant security challenges, necessitating the upgrade of current wireless networks to the 6G standard and an improved intrusion detection system for host networks. Thus this article therefore underscores a game theory concept to analyze the security impediments in wireless communication systems, where a host network bandwidth is jeopardized by the intruder while acting as a legitimate user. While game theory can help with the impending security concerns, network slicing is saliently proposed for large and complicated networks, precipitating a layer wise fragmentation of the concerned network and effectively addressing the security issues in different layers of the IoT framework. However, with increasing network complexity, network security issues incorporating traditional measures become increasingly cumbersome. Therefore, this paper also briefly mentions some of the ML techniques that can be used to classify and segregate valid users from the compromised nodes in the host network. The paper discusses the potential of incorporating intelligence into wireless communication systems to enhance the host network security. The paper further aids in analyzing existing security issues in different layers of wireless communication systems, underscoring network slicing for further classification of intruders from user nodes and training the host network using ML/AI per the user application specification.

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Section: B Interfacing Existing Network With Other Access Systemsmentioning

confidence: 99%

Security Challenges in 6G Networks: A Game Theory-based Intrusion Detection with Network Slicing and AI/ML Perspectives

Gupta,

Jha,

Sabraj

2024

Preprint

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“…The 6G communication network is widely believed to be an indispensable intermedia for next-generation human–computer interaction. Deep learning is a promising solution to boost human–computer interaction applications in the envisioned 6G era [ 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

et al. 2023

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With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.

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Touch-Interfacing Middleware Network Design in 6G

Cited by 2 publications

References 7 publications

Security Challenges in 6G Networks: A Game Theory-based Intrusion Detection with Network Slicing and AI/ML Perspectives

Security Challenges in 6G Networks: A Game Theory-based Intrusion Detection with Network Slicing and AI/ML Perspectives

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

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