Abstract
The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.
Highlights
We highlight future research based on application scenarios and a multi-level architecture that enables a data-driven deep learning (DL). The contributions of this survey can be summarized as follows: We highlight the key requirements of ultra-reliable and low latency communication (URLLC) and the challenges currently threatening the vision of 6G wireless networks
We provide a detailed discussion of the advantageous new services that will be offered by 6G wireless networks, including their fundamental principles and general applications such as holographic radio, advanced wireless channel coding, massive Internet of Things (IoTs), integrated and haptic communication, and Tactile Internet for URLLC
This study aims to fill the gaps found in the application scenarios of multi-level architectures for 6G networks, including mobility and traffic prediction for all mobile users (MUs), scheduler design at each access point, user connection in a multi-access point [46]–[49], [50]–[53], [54]–[58], and multi-level architecture for URLLC in DL [18], [65], [61]
Summary
The role of artificial intelligence (AI) in the development of intelligence-enabled edge computing, connections in the. It provides a detailed discussion of multi-level architecture by enabling AI It presents a multi-level architecture for URLLC through DL to create a data-driven AI system, 6G networks for intelligent devices, and technologies based on practical learning capability. The contributions of this survey can be summarized as follows: We highlight the key requirements of URLLC and the challenges currently threatening the vision of 6G wireless networks. We provide a detailed discussion of the advantageous new services that will be offered by 6G wireless networks, including their fundamental principles and general applications such as holographic radio, advanced wireless channel coding, massive IoTs, integrated and haptic communication, and Tactile Internet for URLLC.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
