Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/4/343 |
| _version_ | 1797585362721177600 |
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| author | Chunlei Chen Huixiang Zhang Jinkui Hou Yonghui Zhang Huihui Zhang Jiangyan Dai Shunpeng Pang Chengduan Wang |
| author_facet | Chunlei Chen Huixiang Zhang Jinkui Hou Yonghui Zhang Huihui Zhang Jiangyan Dai Shunpeng Pang Chengduan Wang |
| author_sort | Chunlei Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:05:59Z |
| format | Article |
| id | doaj.art-01c7ad7c498449a9b79927d5e9db2700 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T00:05:59Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-01c7ad7c498449a9b79927d5e9db27002023-11-19T00:22:29ZengMDPI AGBiomimetics2313-76732023-08-018434310.3390/biomimetics8040343Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and ProspectsChunlei Chen0Huixiang Zhang1Jinkui Hou2Yonghui Zhang3Huihui Zhang4Jiangyan Dai5Shunpeng Pang6Chengduan Wang7School of Computer Engineering, Weifang University, Weifang 261061, ChinaSchool of Cyberspace Security, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Computer Engineering, Weifang University, Weifang 261061, ChinaSchool of Computer Engineering, Weifang University, Weifang 261061, ChinaSchool of Computer Engineering, Weifang University, Weifang 261061, ChinaSchool of Computer Engineering, Weifang University, Weifang 261061, ChinaSchool of Computer Engineering, Weifang University, Weifang 261061, ChinaSchool of Computer Engineering, Weifang University, Weifang 261061, ChinaWith 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.https://www.mdpi.com/2313-7673/8/4/343human-centric6Gdeep learning |
| spellingShingle | Chunlei Chen Huixiang Zhang Jinkui Hou Yonghui Zhang Huihui Zhang Jiangyan Dai Shunpeng Pang Chengduan Wang Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects Biomimetics human-centric 6G deep learning |
| title | Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects |
| title_full | Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects |
| title_fullStr | Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects |
| title_full_unstemmed | Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects |
| title_short | Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects |
| title_sort | deep learning in the ubiquitous human computer interactive 6g era applications principles and prospects |
| topic | human-centric 6G deep learning |
| url | https://www.mdpi.com/2313-7673/8/4/343 |
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