A reinforcement learning agent for mixed-numerology interference-aware slice spectrum allocation with non-deterministic and deterministic traffic

Zambianco, Marco; Verticale, Giacomo

doi:10.1016/j.comcom.2022.03.010

Cited by 10 publications

(1 citation statement)

References 14 publications

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“…Mcwade et al [15] proposed a two-stage suboptimization approach to deal with the problem of optimizing subcarriers and power allocation to maximize the spectral efficiency of multinumerology-nonorthogonal multiple access. Thus, we start to investigate resource allocation between different numerologies [16][17][18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

Wang,

Tao,

Zhao

et al. 2024

Wireless Communications and Mobile Computing

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This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.

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