Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine

Guo, Weisi

doi:10.1109/mcom.001.2000050

Cited by 177 publications

(116 citation statements)

References 11 publications

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“…research directions of advancing ML technologies into the future 6G network from the perspectives of communication, networking, and computing. Guo outlined the core concepts of explainable AI for 6G in [28], including public and legal motivation, definition, the trade-off between explainability and performance, explainable methods, and an explainable AI framework for future wireless systems. A survey paper [29] provides a comprehensive view of 6G in terms of applications, requirements, challenges, and research directions.…”

Section: Feb Vehicularmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

827

241

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As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), architecture, and enabling technologies. First, we attempt to answer the question of "Is there any need for 6G?" by shedding light on its key driving factors, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of "What 6G may look like?". This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.

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Section: Feb Vehicularmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

827

241

View full text Add to dashboard Cite

show abstract

“…Using training data to train DNNs and deploy computing resources at MUs based on proposed dense high-performance servers in a high real-time system [232]. The DL is utilized to learn from great amounts of supervised data to represent big data analytics and exploit the availability of huge amounts of data [233]. The multi-level storage and computing services are achieved by using fog computing, which is able to reduce the latency in URLLC [217], [234], [235], [173].…”

Section: ) Computing Resources and Multi-level Storagementioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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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-toend 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.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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“…[25] June ML Possible challenges and potential research directions of advancing ML technologies into the future 6G network in terms of communication, networking, and computing perspective. [26] June AI…”

Section: Dec Aimentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang¹,

Han²,

Habibi³

et al. 2021

Preprint

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<p>As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), and enabling technologies. First, we attempt to answer the question of “Is there any need for 6G?” by shedding light on the key driving factors of 6G, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of “What 6G may look like?”. This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.</p>

show abstract

Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine

Cited by 177 publications

References 11 publications

The Road Towards 6G: A Comprehensive Survey

The Road Towards 6G: A Comprehensive Survey

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

The Road Towards 6G: A Comprehensive Survey

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