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

Chen, Chunlei; Zhang, Huixiang; Hou, Jianlin; Zhang, Yonghui; Zhang, Huihui; Dai, Jiangyan; Pang, Shunpeng; Wang, Chengduan

doi:10.3390/biomimetics8040343

Cited by 6 publications

(2 citation statements)

References 114 publications

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“…It can be employed for dynamic resource allocation, load balancing, and network optimization [ 51 ]. Deep learning models like neural networks can undergo training to forecast network performance, recognize patterns, and enhance resource allocation [ 52 ]. Deep reinforcement learning (DRL) combines deep learning with RL for complex decision making.…”

Section: Ai and ML For 6g Networkmentioning

confidence: 99%

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2024

Sensors

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In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field.

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Section: Ai and ML For 6g Networkmentioning

confidence: 99%

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…It can be employed for dynamic resource allocation, load balancing, and network optimization [107]. Deep learning models like neural networks can undergo training to forecast network performance, recognize patterns, and enhance resource allocation [108]. Deep reinforcement learning (DRL) combines deep learning with RL for complex decision-making.…”

Section: Artificial Intelligence and Machine Learning For 6g Networkmentioning

confidence: 99%

6G Networks and AI Revolution - Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2023

Preprint

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In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in mobile wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and the key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning, quantum communication, and re-configurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and machine learning, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. This paper also addresses critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study not only maps out the current landscape but also lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI(Artificial Intelligence) technologies, we aim to illuminate the path forward in this rapidly evolving field.

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Improved Deep Reinforcement Learning With Faster Graph Recurrent Convolutional Neural Network‐Enabled Adaptive Network Slicing for Tailored Service Delivery in NextGen Networks

Sugapriya,

Vijayabhasker

2024

Int J Communication

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Many new use cases and a broad spectrum of vertical businesses are expected to be supported by next‐generation wireless networks. Network slicing has been implemented to suit the stringent requirements of different services. By dividing the infrastructure network into several logical networks, this technology enables resource allocation based on services. In dynamic and unpredictable situations, managing resources across several domains and dimensions for end‐to‐end (E2E) slicing still presents difficulties. Tenant satisfaction requires striking a balance between the trade‐off between revenue and the expense of resource allocation. Network slicing has emerged as a fundamental paradigm in next‐generation networks to meet the diverse service requirements of various applications and users. However, the dynamic nature of network conditions and service demands poses challenges in efficiently allocating network resources to meet performance objectives. In this paper, a faster graph recurrent convolutional neural network (FGRCNN) with improved deep reinforcement learning (IDRL) is proposed to learn traffic behavior from link and node properties in addition to network structure. To train the FGRCNN model in the IDRL framework without requiring a labeled training dataset, employ the Deep Q‐learning technique. This allows the framework to swiftly adjust to changes in traffic dynamics. A system is proposed to analyze real‐time network and service data enabling dynamic adaptation of network slices for changing traffic patterns and service requirements. A comprehensive framework is presented that integrates deep learning models with network slicing orchestration mechanisms to achieve tailored service delivery. Through extensive simulations and experiments, the effectiveness approach in optimizing resource utilization is demonstrated, improving service quality and enabling agile network management in next‐gen networks. Results highlight the potential of deep learning‐enabled adaptive network slicing to support diverse and evolving service demands in future network environments.

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

Cited by 6 publications

References 114 publications

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

6G Networks and AI Revolution - Exploring Technologies, Applications, and Emerging Challenges

Improved Deep Reinforcement Learning With Faster Graph Recurrent Convolutional Neural Network‐Enabled Adaptive Network Slicing for Tailored Service Delivery in NextGen Networks

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