URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

Ali, Rashid; Zikria, Yousaf Bin; Bashir, Ali Kashif; Garg, Sahil; Kim, Hyung Seok

doi:10.1109/access.2021.3073806

Cited by 83 publications

(29 citation statements)

References 133 publications

(112 reference statements)

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“…5G introduced the Ultra-Reliable Low Latency Communications (URLLC) service to address the issues with low la-tency medium access [45] in a centralized or decentralized manner, with a variety of proposed physical layer [74] and mac layer [75] techniques. Despite the promise of these approaches, as well as 3GPP release 15 (including URLLC) being released in late 2017, roll out of these technologies into real world networks has been limited, and considerable research is ongoing as to the best implementation methods for 5G's technical goals [76].…”

Section: Cellular Networkmentioning

confidence: 99%

A Critical Review of Communications in Multi-Robot Systems

Gielis¹,

Singh²,

Prorok³

2022

Preprint

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Purpose of Review. This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems. Recent Findings. Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machinelearning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the 'sim-to-real' gap in this context. Summary. We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.

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Section: Cellular Networkmentioning

confidence: 99%

A Critical Review of Communications in Multi-Robot Systems

Gielis¹,

Singh²,

Prorok³

2022

Preprint

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“…Mesh network of routers Number of AR/VR user requesting 1 at a time RCR nodes in core network n = 9 Link delay 10 ms Routing decision MDP (A i ) Learning rate (α) 0.1, 0.5, 0.9 Discounted factor (γ) 0.1, 0.5, 0.9 Exploitation/exploration factor (ε) 0.5 Action space [1,5] State space [1,9] This section will evaluate the proposed ISO strategy in ICN systems for optimal routing path that lowers the E2E for haptic-driven AR/VR applications in TI. We computed the stretch as the performance metric for the routing path of the agent.…”

Section: Simulation Environment Matlab 2020a Network Topologymentioning

confidence: 99%

“…The 5G provides URLLC and eMBB services to users that improve quality of service (QoS), quality of tasks (QoT), and quality of experience (QoE) for TI applications [8]. Recently, the AR/VR applications with haptic feedback are getting more attention in 5G-enabled TI [9]. Moreover, the TI applications and the application specific requirements and characteristics are illustrated in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications

et al. 2021

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The fifth-generation (5G) mobile network services are currently being made available for different use case scenarios like enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The ever-increasing data requests from the users have shifted the communication paradigm to be based on the type of the requested data content or the so-called information-centric networking (ICN). The ICN primarily aims to enhance the performance of the network infrastructure in terms of the stretch to opt for the best routing path. Reduction in stretch merely reduces the end-to-end (E2E) latency to ensure the requirements of the 5G-enabled tactile internet (TI) services. The foremost challenge tackled by the ICN-based system is to minimize the stretch while selecting an optimal routing path. Therefore, in this work, a reinforcement learning-based intelligent stretch optimization (ISO) strategy has been proposed to reduce stretch and obtain an optimal routing path in ICN-based systems for the realization of 5G-enabled TI services. A Q-learning algorithm is utilized to explore and exploit the different routing paths within the ICN infrastructure. The problem is designed as a Markov decision process and solved with the help of the Q-learning algorithm. The simulation results indicate that the proposed strategy finds the optimal routing path for the delay-sensitive haptic-driven services of 5G-enabled TI based upon their stretch profile over ICN, such as the augmented reality /virtual reality applications. Moreover, we compare and evaluate the simulation results of propsoed ISO strategy with random routing strategy and history aware routing protocol (HARP). The proposed ISO strategy reduces 33.33% and 33.69% delay as compared to random routing and HARP, respectively. Thus, the proposed strategy suggests an optimal routing path with lesser stretch to minimize the E2E latency.

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“…It is already visible that 5G acts as an integrator for diverse applications and services. To this end, 5G networks utilize vehicular communications [5], device-to-device (D2D) communications [6], machine-to-machine (M2M) communications [7], mobile edge computing (MEC) [8], cloud computing [9] and internet of things (IoT) [10], in order to meet the needs for enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliablelow-latency-communications (URLLC) [11].…”

mentioning

confidence: 99%

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

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In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multipleinput-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

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URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

Cited by 83 publications

References 133 publications

A Critical Review of Communications in Multi-Robot Systems

A Critical Review of Communications in Multi-Robot Systems

Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

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