2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) 2019
DOI: 10.1109/globalsip45357.2019.8969539
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A BP Neural Network Based Punctured Scheduling Scheme Within Mini-slots for Joint URLLC and eMBB Traffic

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Cited by 7 publications
(4 citation statements)
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“…Existing works on designing resource allocation strategies mostly focuses on two goals, either to maximize the utility of eMBB traffic or to reduce the loss of URLLC puncturing on eMBB utility. [22][23][24][25][26] The authors in Reference 22 adopted a back propagation neural network to reduce the eMBB throughput loss caused by URLLC puncturing. The authors in Reference 23 proposed a model-free deep reinforcement learning method DEMUX to reduce the adverse effects of URLLC preemption on eMBB utility.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Existing works on designing resource allocation strategies mostly focuses on two goals, either to maximize the utility of eMBB traffic or to reduce the loss of URLLC puncturing on eMBB utility. [22][23][24][25][26] The authors in Reference 22 adopted a back propagation neural network to reduce the eMBB throughput loss caused by URLLC puncturing. The authors in Reference 23 proposed a model-free deep reinforcement learning method DEMUX to reduce the adverse effects of URLLC preemption on eMBB utility.…”
Section: Related Workmentioning
confidence: 99%
“…Existing works on designing resource allocation strategies mostly focuses on two goals, either to maximize the utility of eMBB traffic or to reduce the loss of URLLC puncturing on eMBB utility 22–26 . The authors in Reference 22 adopted a back propagation neural network to reduce the eMBB throughput loss caused by URLLC puncturing. The authors in Reference 23 proposed a model‐free deep reinforcement learning method DEMUX to reduce the adverse effects of URLLC preemption on eMBB utility.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, eMBB transmissions are supposed to schedule in the slot, and URLLC transmissions are supposed to schedule in the mini-slot to achieve latency requirements. [5][6][7][8][9][10][11][12] Alsenwi et al 5 maximized the average data rate of eMBB users while URLLC constraints were satisfied, and the variance of eMBB data rates was minimized to protect low-rate eMBB users. However, it did not consider the data rate requirements of eMBB transmissions.…”
Section: Coexistence Of Embb Urllc Servicesmentioning
confidence: 99%
“…To address the URLLC placement problem, the work by Shang et al 11 presented a backpropagation neural network and selected the eMBB user who would suffer the least throughput loss if punctured. Bairagi et al 12 used a heuristic approach to address the resource allocation problem of eMBB transmissions, whereas a one-sided matching approach was used to handle the resource allocation problem of URLLC traffic.…”
Section: Coexistence Of Embb Urllc Servicesmentioning
confidence: 99%