Optimizing Resource Allocation in URLLC for Real-Time Wireless Control Systems

Chang, Bo; Zhang, Lei; Li, Emma; Zhao, Guodong; Chen, Zhi

doi:10.1109/tvt.2019.2930153

Cited by 104 publications

(40 citation statements)

References 33 publications

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“…In this Subsection, we will show that the consensus reliability and the consensus latency are contradictory. A wireless communication model, which aims to analyze the packet error probability of the wireless short package transmissions in URLLC [10], is used to find out the relationship between consensus success rate P C and the consensus latency T , which we assume it is caused by downlink and uplink transmission delay, i.e., Raft consensus latency T only composes of communication transmission delay to show the communication impacts on the overall consensus latency. Note that model is an illustrational case and other models can be used without affecting the main conclusion of the paper.…”

Section: Relationship Between Latency and Reliabilitymentioning

confidence: 99%

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Low Reliable and Low Latency Communications for Mission Critical Distributed Industrial Internet of Things

et al. 2021

IEEE Commun. Lett.

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Achieving ubiquitous ultra-reliable low latency consensus in centralized wireless communication systems can be costly and hard to scale up. The consensus mechanism, which has been widely utilized in distributed systems, can provide fault tolerance to the critical consensus, even though the individual communication link reliability is relatively low. In this paper, a widely used consensus mechanism, Raft, is introduced to the Industrial Internet of Things (IIoT) to achieve ultra-reliable and low latency consensus, where the consensus reliability performance in terms of nodes number and link transmission reliability is investigated. We propose a new concept, Reliability Gain, to show the linear relationship between consensus reliability and communication link transmission reliability. We also find that the time latency of consensus is contradictory to consensus reliability. These conclusions can provide guides to deploy Raft protocol in distributed IIoT systems.

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Section: Relationship Between Latency and Reliabilitymentioning

confidence: 99%

“…Note that model is an illustrational case and other models can be used without affecting the main conclusion of the paper. According to [10], the link failure rate 1 P l used in equation 1and (2) can be written as a function of T as follow…”

Section: Relationship Between Latency and Reliabilitymentioning

confidence: 99%

Low Reliable and Low Latency Communications for Mission Critical Distributed Industrial Internet of Things

et al. 2021

IEEE Commun. Lett.

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“…(6), the time delay is modeled into the control function. Thus, the analysis on communication time delay is based on the control parameters in (6). Since the initial control input is empty, the palnt state first increases when the time index k increases.…”

Section: A Effect Of Different Communication Qoss On Control Performmentioning

confidence: 99%

“…In the coming fifth-generation (5G) cellular networks, ultrareliable and low-latency communication (URLLC) is proposed as an indispensable communication service to enable real-time wireless control systems [1]- [6]. A huge amount of applications in control systems, e.g., manufacturing, construction, and medical treatment, are expected to be supported by URLLC.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Communication QoS Design for Real-Time Wireless Control Systems

et al. 2020

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In the coming fifth-generation (5G) cellular networks, ultra-reliable and low-latency communication (URLL-C) is treated as an indispensable service to enable real-time wireless control systems. However, the extremely high qualityof-service (QoS) in URLLC causes significant wireless resource consumption. Moreover, to obtain good control performance may not always require extremely high communication QoS. In this paper, we propose a communication-control co-design scheme to reduce wireless resource consumption, where we obtain a dynamic communication QoS design method to reduce the energy consumption by jointly using extremely high QoS and a relatively low QoS. In this scheme, we first explore the control process served by different communication QoS levels and find that the whole control process can be divided into two phases, where different QoS levels have their advantages in different phases. Then, we obtain a threshold to decide when the extremely high QoS or relatively low QoS should be provided by communications. Simulation results demonstrate that our method can effectively reduce communication energy consumption while maintaining good control performance.

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“…additionally support ultra-reliable low-latency communications (URLLC) [2], [3]. As the name suggests, URLLC target applications requiring high reliability with low-latency for emerging use cases like industrial automation, intelligent transport systems, and haptic communications [4]- [6]. A typical URLLC target is one-way reliability of 99.999% for a data unit of 32 bytes to be delivered within a tight time budget of one millisecond (msec) [2], [7].…”

mentioning

confidence: 99%

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

Karimi

Pedersen

Mahmood

et al. 2020

IEEE Trans. Veh. Technol.

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This paper addresses the problem of downlink radio resource management for ultra-reliable low-latency communications (URLLC) in fifth generation (5G) systems. To support low-latency communications, we study performance of two multiplexing schemes namely in-resource control signalling and joint encoding of data and metadata. In the former, the metadata and data are separately encoded and the metadata is sent at the beginning of transmission time prior to the data. Thus, it benefits from a low-complexity receiver structure to decode the data block. The latter takes advantages of transmitting a larger blocklength to enhance the reliability and improve spectrum efficiency by jointly encoding data and metadata as a single codeword. Dealing with small URLLC payloads, the overhead and error of sending metadata are not negligible and have a significant impact on the system performance in terms of resource usage the reliability of transmission. For each scheme, we derive expressions for the outage probability and resource usage by taking into account impacts of the finite blocklength payloads, overhead and error of sending metadata, and probability of error in uplink feedback channel. We propose a novel framework for joint data and metadata link adaptation to minimize the average number of allocated resources, while ensuring the stringent URLLC quality of service requirements. An optimization problem is formulated for each scheme that is mixed-integer nonconvex problem, difficult to solve in polynomial time. Solutions based on successive convex optimization are proposed. Numerical evaluations show that the proposed algorithms perform close to the optimal solution and demonstrate remarkable gains of up to 27% improvement in resource usage. Finally, we present sensitivity analysis of the results for various network parameters.

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Optimizing Resource Allocation in URLLC for Real-Time Wireless Control Systems

Cited by 104 publications

References 33 publications

Low Reliable and Low Latency Communications for Mission Critical Distributed Industrial Internet of Things

Low Reliable and Low Latency Communications for Mission Critical Distributed Industrial Internet of Things

Dynamic Communication QoS Design for Real-Time Wireless Control Systems

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

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