Learning-Driven Wireless Communications, towards 6G

Piran, Md. Jalil; Suh, Doug Young

doi:10.1109/iccece46942.2019.8941882

Cited by 62 publications

(42 citation statements)

References 6 publications

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“…The study in [22] gives an overview of architectures, challenges, and techniques for efficient wireless powering of Internet-of-Things (IoT) networks in 6G. Moreover, Piran and Suh [23] consider the requirements, use cases, and challenges to realize 6G systems with a particular emphasis on artificial intelligence (AI)-based techniques for network management. The role of collaborative AI in 6G systems at the PHY layer and above layers is discussed in [24].…”

Section: B Literature Review: 6g Vision and Performance Aspectsmentioning

confidence: 99%

6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities

Shafi²,

et al. 2021

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Mobile communications have been undergoing a generational change every ten years or so. However, the time difference between the so-called "G's" is also decreasing.While fifth-generation (5G) systems are becoming a commercial reality, there is already significant interest in systems beyond 5G, which we refer to as the sixth generation (6G) of wireless systems. In contrast to the already published papers on the topic, we take a top-down approach to 6G. More precisely, we present a holistic discussion of 6G systems beginning with lifestyle and societal changes driving the need for next-generation networks. This is followed by a discussion into the technical requirements needed to enable 6G applications, based on which we dissect key challenges and possibilities for practically realizable system solutions across all layers of the Open Systems Interconnection stack (i.e., from applications to the physical layer). Since many of the 6G applications

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Section: B Literature Review: 6g Vision and Performance Aspectsmentioning

confidence: 99%

6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities

Shafi²,

et al. 2021

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“…In the physical layer, RL can be employed for link preservation, channel tracking, radio access association, on-demand beamforming, energy harvesting, modulation selection, radio identification, etc. [ 8 , 13 , 16 , 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

“…One of the most important requirements and trends of the next generation of mobile networks is the self-X paradigm. This promises future networks will be able to perform the required functionalities, such as self-learning, self-configuration, self-optimization, self-healing, self-organization, self-aggregation, and self-protection, with no human intervention [ 8 ]. Such a paradigm shift is likely to lead to a more flexible and robust network.…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, the most recent mobile handsets are already offering many exciting AI-based services, such as AI-based cameras, AI-based earphones and intelligent assistants (Siri, Cortana, Google Now, Bixby, Alexa, etc.) [ 8 , 9 ]. On the other hand, current assessment approaches closely rely on mathematical models that capture the structures and dynamics of the communication systems.…”

Section: Introductionmentioning

confidence: 99%

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D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Driouech

Sabir

Ghogho

et al. 2021

Sensors

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Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.

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“…The role of artificial intelligence (AI) in the development of intelligence-enabled edge computing, connections in the process of edge intelligence, intelligent devices for mobile users (MUs), and edge infrastructure components is a driving force behind the development of the sixth generation (6G) of wireless communications [1], [2]. The novel generation is expected to employ wide frequency bands to provide big data.…”

Section: Introductionmentioning

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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Learning-Driven Wireless Communications, towards 6G

Cited by 62 publications

References 6 publications

6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities

6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities

D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

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

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