A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR

Huang, Yan; Li, Shaoran; Li, Chengzhang; Hou, Y. Thomas; Lou, Wenjing

doi:10.1109/jiot.2020.2978692

Cited by 69 publications

(38 citation statements)

References 15 publications

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“…We use similar PSUM parameters as of [33]. Moreover, the values of important simulation parameters of our work follow the 5G NR values as indicated in [45]. The decoding probability of the preempted eMBB transmission depends on whether the UE is informed about that or not.…”

Section: Numerical Analysis and Discussionmentioning

confidence: 99%

Coexistence Mechanism Between eMBB and uRLLC in 5G Wireless Networks

Bairagi

Munir

Alsenwi

et al. 2021

IEEE Trans. Commun.

132

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Section: Numerical Analysis and Discussionmentioning

confidence: 99%

Coexistence Mechanism Between eMBB and uRLLC in 5G Wireless Networks

Bairagi

Munir

Alsenwi

et al. 2021

IEEE Trans. Commun.

132

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“…The latency and reliability of each user are used as a feedback to the deep RL algorithm. In [22] and [23], the authors proposed a DRL based algorithms to solve the coexistence problem of eMBB and URLLC. A deep deterministic policy gradient based method is used in [22] while the deep Qlearning algorithm is leveraged in [23].…”

Section: Drl In Wireless Networkmentioning

confidence: 99%

Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach

Alsenwi

Tran

Bennis

et al. 2021

IEEE Trans. Wireless Commun.

228

124

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In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimizationaided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.

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“…with reward r as defined in (7). The long term rewards are discounted by an exponential factor 0 ≤ λ ≤ 1.…”

Section: Q(s[t] a 3 ) Arg Maxmentioning

confidence: 99%

“…Various forays have been made into integrating DL methods with communications resource allocation tasks, mostly based on deep Q-Networks (DQN) [4]- [6] and actor-critic methods [7]. However, model-based approaches have long thrived thanks to the valuable expert knowledge that shapes their design.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care

Gracla¹,

Beck²,

Bockelmann³

et al. 2021

Preprint

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Greater capabilities of mobile communications technology enable interconnection of on-site medical care at a scale previously unavailable. However, embedding such critical, demanding tasks into the already complex infrastructure of mobile communications proves challenging. This paper explores a resource allocation scenario where a scheduler must balance mixed performance metrics among connected users. To fulfill this resource allocation task, we present a scheduler that adaptively switches between different model-based scheduling algorithms. We make use of a deep Q-Network (DQN) to learn the benefit of selecting a scheduling paradigm for a given situation, combining advantages from model-driven and data-driven approaches. The resulting ensemble scheduler is able to combine its constituent algorithms to maximize a sum-utility cost function while ensuring performance on designated high-priority users.

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A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR

Cited by 69 publications

References 15 publications

Coexistence Mechanism Between eMBB and uRLLC in 5G Wireless Networks

Coexistence Mechanism Between eMBB and uRLLC in 5G Wireless Networks

Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach

Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care

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