Per-flow end-to-end delay bounds in heterogeneous wireless networks

Liu, Qiuming; Jiang, Xuejing; Zhou, Yang

doi:10.1109/compcomm.2017.8322559

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Depending on the delay metrics, the existing queueing theory research papers can be classified into three categories, i.e., average delay [142][143][144][145], hard deadline [15,[146][147][148][149], and statistical QoS [16,[150][151][152][153][154][155][156].…”

Section: B Queueing Theory Toolsmentioning

confidence: 99%

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

She¹,

Sun²,

Gu³

et al. 2020

Preprint

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As one of the key communication scenarios in the 5th and also the 6th generation (6G) cellular networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. The state-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLC. A holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how to integrate theoretical knowledge (models, analysis tools, and optimization frameworks) of wireless communications into different kinds of deep learning algorithms for URLLC. We first introduce the background of URLLC and review promising network architectures and deep learning frameworks in 6G. To better illustrate how to improve learning algorithms with theoretical knowledge, we revisit model-based analysis tools and cross-layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

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Section: B Queueing Theory Toolsmentioning

confidence: 99%

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

She¹,

Sun²,

Gu³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Quiming Liu et. al has considered the influence of network topology on the delay analysis in a largescale wireless Ad-hoc network [14]. The packet transmission delay is an important parameter to evaluate the network QoS performance.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient traffic contention and control mechanism to improve QoS in heterogeneous wireless sensor networks

Kavitha

Latchoumy²,

A³

2020

IJEECS

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<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Heterogeneous Wireless Sensor Networks (HWSN) is the key to gather information from a cooperative network. Collective nodes are organized to form cooperative networks. In order to access the network resources, various protocols were introduced for portable devices. In HWSN, the sensor nodes are scattered, and the position of the nodes are altruistic. Hence, the main challenges in HWSN are topology control, battery optimization, packet loss and link lifetime. The existing techniques do not concentrate on all these issues at the same time. The objective of this work is to provide congestion-free data transfer with higher throughput, decreased packet drop ratio and minimum transmission delay. In the proposed methodology, three protocols are designed and developed, namely, Hop by Hop Rate Adjustment Protocol (HHRA), Energy Efficient Data Transfer Protocol (EEDT) and Alternative Routing Congestion Control Protocol (ARCC). The HHRA protocol senses the traffic in the channel and adjusts the transmission rate accordingly which controls the flow of data to avoid congestion. Secondly, the EEDT protocol is used to find specific nodes that are more efficient between the source to the sink and transfer data packets through those nodes which reduce the hop counts to minimize transmission delay. Thirdly, the ARCC protocol is used to redirect the path of transmission during the occurrence of congestion. Thus, the proposed work ensures congestion free transmission with the higher quality of service and minimum transmission delay.</p><p> </p></td></tr></tbody></table>

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“…However, every architecture requires a communication protocol and interfaces made up by applications and users [36]. The network calculus, introduced by Rene L. Cruz in [37][38] [39], only assumes that the number of bytes sent on the network links does not exceed an arrival curve service (traditionally, a leaky bucket) which can represent both periodic and aperiodic traffic. The network calculus then determines the packet maximum end-to-end delay or jitters from exchange matrices which model the traffic between the industrial devices.…”

mentioning

confidence: 99%

Traffic characterization in a communications channel for monitoring and control in real-time systems

Serna-Guarín

Morantes-Guzmán

Delgado-Trejos

et al. 2020

IJEEI

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The response time for remote monitoring and control in real-time systems is a sensitive issue in device interconnection elements. Therefore, it is necessary to analyze the traffic of the communication system in pre-established time windows. In this paper, a methodology based on computational intelligence is proposed for identifying the availability of a data channel and the variables or characteristics that affect the performance and data transfer, which is made up of four stages: a) integration of a communication system with an acquisition module and a final control structure; b) communication channel characterization by means of traffic variables; and c) relevance analysis from the characterization space using SFFS (sequential forward oating selection); d) Channel congestion classification as Low or High using a classifier based on Naive Bayes algorithm. The experimental setup emulates a real process using an on/off remote control of a DC motor on an Ethernet network. The communication time between the client and server was integrated with the operation and control times, to study the whole response time. This proposed approach allows support decisions about channel availability, to establish predictions about the length of the time window when the availability conditions are unknown.

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Per-flow end-to-end delay bounds in heterogeneous wireless networks

Cited by 6 publications

References 14 publications

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

An efficient traffic contention and control mechanism to improve QoS in heterogeneous wireless sensor networks

Traffic characterization in a communications channel for monitoring and control in real-time systems

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