Beyond 5G Resource Slicing With Mixed-Numerologies for Mission Critical URLLC and eMBB Coexistence

Esmaeily, Ali; Mendis, H. V. Kalpanie; Mahmoodi, Toktam; Kralevska, Katina

doi:10.1109/ojcoms.2023.3254816

Cited by 10 publications

(7 citation statements)

References 53 publications

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“…The authors tend to maximize user satisfaction while taking into consideration latency demand and data transmission. Also, a numerology-based resource allocation scheme is presented in [26]; the authors proposed an energy efficient algorithm for eMBB and URLLC services, the proposed algorithm is formulated as mixed-integer non-linear problem. The authors also studied the impact of URLLC users on eMBB users.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint Q-Learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology

Elmosilhy,

Elmesalawy,

Ibrahim

et al. 2024

IEEE Access

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The emergence of the sixth generation (6G) era has highlighted the importance of Network Slicing (NS) technology as a promising solution for catering the diverse service requests of users . With the presence of a large number of devices with different service requests and since each service has different goal and requirements; efficiently allocating Resource Blocks (RBs) to each network slice is a challenging task in order to meet the desired Quality of Service (QoS) standards. However, it is worth noting that the majority of research efforts have primarily concentrated on cellular technologies, leaving behind the potential benefits of utilizing unlicensed bands to alleviate traffic congestion and enhance the capacity of existing LTE networks. In this paper we propose a novel idea by exploiting LTE-WLAN Aggregation technology (LWA) in Multi-Radio Access Technology (RAT) Heterogeneous Networks (HetNet), aiming to solve radio resource allocation problem based on the Radio Access Network (RAN) slicing and 5G New Radio (NR) scalable numerology technique. A joint optimization problem is proposed by jointly finding an efficient resource allocation ratio for each slice in each Base Station (BS) and by finding the optimum value of scalable numerology with the objective of maximizing users' satisfaction. In order to solve this problem, a novel three-stage framework is proposed which is based on channel state information as a pre-association stage, Reinforcement Learning (RL) algorithm as finding the optimum value of slice resource ratio and scalable numerology, and finally Regret Learning Algorithm (RLA) as users' re-association phase. Furthermore, a comprehensive performance evaluation is conducted against different base-line approaches. The simulation results show that our proposed model balance and achieve improvement in users' satisfaction by deploying the proposed Multi-RAT Het-Net architecture that leverage LWA technology.

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Section: Literature Reviewmentioning

confidence: 99%

Joint Q-Learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology

Elmosilhy,

Elmesalawy,

Ibrahim

et al. 2024

IEEE Access

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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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“…Due to the stringent delay requirements of URLLC traffic, it is typically prioritized, which can have various impacts on eMBB transmission. For example, in works such as [28][29][30], delay, transmission interruption, or a reduced bit rate of eMBB traffic can occur through puncturing or pre-emption methods. Alternatively, in [30,31], a reduction in throughput is observed.…”

Section: Related Workmentioning

confidence: 99%

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

Makeeva,

Kochetkova,

Alkanhel

2023

Mathematics

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Fifth generation (5G) networks support ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). The coexistence of URLLC and eMBB is often organized by non-orthogonal multiple access (NOMA), giving priority to URLLC and resulting in eMBB quality of service (QoS) degradation. In this paper, we address the issue of joint URLLC and eMBB transmission, focusing on the problem from the perspective of delay-tolerant eMBB. Due to the priority given to URLLC, we assume that an eMBB session may be interrupted if there are no free resources available for URLLC or delayed when a new eMBB session arrives. To make the scheme more flexible, we propose that interrupted and delayed eMBB sessions periodically check for free resources, rather than continuously. To analyze this scenario, we propose a retrial queuing system with two retrial buffers (orbits) for interrupted and delayed eMBB sessions. The stationary probability distribution, provided in matrix form by recursive formulas, is presented. The paper concludes with a numerical example showing that the scheme with two buffers, compared to one buffer, practically doubles the average number of active eMBB sessions while keeping the interruption probability below 0.001. We provide an illustration of the configuration of eMBB retrial rates to meet its QoS requirements.

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Beyond 5G Resource Slicing With Mixed-Numerologies for Mission Critical URLLC and eMBB Coexistence

Cited by 10 publications

References 53 publications

Joint Q-Learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology

Joint Q-Learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology

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

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

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