Adaptive and Dynamic Security in AI-Empowered 6G: From an Energy Efficiency Perspective

Shen, Shuaiqi; Yu, Chong; Ni, Jianbing; Ci, Song

doi:10.1109/mcomstd.101.2000090

Cited by 33 publications

(19 citation statements)

References 13 publications

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“…Energy efficiency is critical for ML-enabled wireless systems, particularly in battery-powered devices. Optimizing energy consumption during training and inference involves techniques such as model sparsity, energy-efficient hardware, and dynamic power management [ 177 , 178 , 179 ].…”

Section: Future Directionsmentioning

confidence: 99%

A Survey of PAPR Techniques Based on Machine Learning

da Silva,

Souto,

Souza

et al. 2024

Sensors

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Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks.

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Section: Future Directionsmentioning

confidence: 99%

A Survey of PAPR Techniques Based on Machine Learning

da Silva,

Souto,

Souza

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…6G networks require embedding AI on the entire network and AI logic into the network structure, where perception and inference interact systematically, ultimately allowing all system elements to self-config and adapt with the ability to recognize unexpected situations [70][71].…”

Section: ) Endogenous Aimentioning

confidence: 99%

Innovative Trends in the 6G Era: A Comprehensive Survey of Architecture, Applications, Technologies, and Challenges

et al. 2023

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While the fifth-generation mobile network is being commercialized worldwide, researchers have recently started looking towards the next generation, called the 6G network. Unlike 5G and previous generations of wireless technologies designed to improve network performance for greater bandwidth, lower latency and greater reliability, 6G ecosystems is considered a platform conducive to innovations in the fields of computing, artificial intelligence, connectivity and sensors, virtualization and more. It is designed to meet the requirements of higher global coverage, greater spectral efficiency, a reduced carbon footprint, with an emphasis on sustainability, equity, trust and security through unprecedented architectural evolutions and technology. 6G will be an integrated network system that includes a traditional terrestrial mobile network, space network, and underwater network to provide ubiquitous network access. Even if there are studies on vision of 6G network that have already been published, there is still a significant amount of ground to cover. There is no decision made yet regarding anything and nothing has been ruled out. The focus of this study is to identify a complete picture of changes in architectures, technologies, and challenges that will shape the 6G network. We hope the research results will provide indications for further studies on 6G ecosystems.

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“…Such integrations are rooted in the physical layer. Nevertheless, some new threats arise due to the physical features of the new technologies [ 54 ]. In the frequency band of THz, the signal exhibits high directionality and enables LoS (Line of Sight) transmission.…”

Section: Applications and Challenges Of The Envisioned 6g Contextmentioning

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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Adaptive and Dynamic Security in AI-Empowered 6G: From an Energy Efficiency Perspective

Cited by 33 publications

References 13 publications

A Survey of PAPR Techniques Based on Machine Learning

A Survey of PAPR Techniques Based on Machine Learning

Innovative Trends in the 6G Era: A Comprehensive Survey of Architecture, Applications, Technologies, and Challenges

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

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