Enabling URLLC Applications Through Reconfigurable Intelligent Surfaces: Challenges and Potential

Almekhlafi, Mohammed; Arfaoui, Mohamed Amine; Assi, Chadi; Ghrayeb, Ali

doi:10.1109/iotm.007.2100124

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…2) RIS/relay-assisted multiplexing URLLC and eMBB: RIS has been shown to significantly reduce URLLC latency and improve channel gains in URLLC and eMBB multiplexing systems [12], [18], [38]. In [39], RIS was used to maximize URLLC reliability and minimize eMBB rate loss by jointly optimizing RIS phase shift, frequency, and base station transmission power.…”

Section: A Motivationsmentioning

confidence: 99%

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

Peng,

Hsia,

Han

et al. 2024

IEEE Trans. Wireless Commun.

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By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system. This study investigates the delay and backlog violation behavior of URLLCs and eMBB multiplexing systems supported by different technologies, e.g. RIS and DF relay, for different scheduling policies of static priority, nonpreemption, and earliest deadline first. A tight analysis approach based on the Martingale theory was proposed to evaluate the serviceability of URLLC and eMBB multiplexing systems. On this basis, the Martingale theory analyzes the delay and backlog bounds by transforming the arrival and service processes into exponential forms of the moment generating function. Furthermore, this study derives the closed-form expression of delay and backlog bound for the URLLCs and eMBB multiplexing in two-hop heterogeneous communication networks. Numerical results demonstrate that the proposed Martingale-based tightly analytical method outperforms the state-of-the-art classic stochastic network calculus for evaluating delay and backlog violation in URLLC and eMBB multiplexing systems.

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Section: A Motivationsmentioning

confidence: 99%

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

Peng,

Hsia,

Han

et al. 2024

IEEE Trans. Wireless Commun.

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“…Whereas, it is not a trivial job to integrate IRS into the wireless network architecture. Moreover, the design and optimization of the IRS is a daunting job due to the high complexity [ 24 ].…”

Section: Applications and Challenges Of The Envisioned 6g Contextmentioning

confidence: 99%

“…In addition, in some critical scenarios, like distant surgery and healthcare robotics, not only latency but also reliability is neglectable to guarantee QoS or even safety. There have been research works on deep-learning-based URLLC (ultrareliable and low-latency communication) of 6G [ 24 ]. As shown in Figure 4 , URLLC can be explored from the following perspectives: radio access, diversity, data processing, coverage and pervasive intelligence.…”

Section: Investigation Of Existing Workmentioning

confidence: 99%

“…Third, due to the dynamic nature of the 6G network, the traditional offline-online mode may not necessarily satisfy the performance demands. Ongoing research works are investigating self-adaptive solutions that can automatically explore novel optimization strategies and transfer knowledge to the URLLC applications [ 24 ].…”

Section: Investigation Of Existing Workmentioning

confidence: 99%

“…Due to the high complexity of 6G communication networks, it is a challenging task to bridge the gap between deep learning and 6G. Potential challenges can stem from the complexity of the underlying hardware [ 19 , 20 ], novel characteristics of the carrier-waves [ 21 , 22 ], high dynamic features of the channel [ 23 , 24 ], heterogeneity of the application requirements [ 25 ], tradeoffs among diverse performance metrics [ 26 ], lack of training data [ 27 ], etc.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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URLLC in Beyond 5G and 6G Networks: An Interference Management Perspective

et al. 2023

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Ultra-reliable and low-latency communications (URLLC) is one of the cornerstone services of fifth-generation and beyond (B5G) wireless networks. The URLLC use cases demand strict block error probability and very low-latency targets, and thus even a small 32 byte of payload corruption could be detrimental to the user experience and the overall system performance. Ideally, the current heterogeneous network model and innovative technologies have paved the way for the seamless optimization of B5G URLLC applications. Yet, unprecedented interference issues due to the uncoordinated nature of modern wireless networks, innovative radio access techniques, and frequency reuse methods can significantly affect the performance of the URLLC systems. Therefore, understanding and mitigation of all types of interference associated with each URLLC technology, deployment scenario, and wireless transmission mode are prudent. In this regard, many authors have recently presented interpretative and analytical studies on the management of interference issues; however, the discussion was limited to application-centric approaches and earlier research trends. In this review article, we primarily discuss different types of interference challenges related to the URLLC systems using contemporary designs, frameworks, access modes, and enabling technologies for B5G and sixth-generation (6G) communication networks. We present state-of-the-art research work, in-depth analysis of interference problems, and guidance on the futuristic 6G URLLC technologies and communication networks. This study intends to provide a holistic vision of B5G/6G URLLC systems, their empirical aspects, limitations, essential techniques, and an outlook toward future research avenues.

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Enabling URLLC Applications Through Reconfigurable Intelligent Surfaces: Challenges and Potential

Cited by 10 publications

References 15 publications

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

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

URLLC in Beyond 5G and 6G Networks: An Interference Management Perspective

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