Enhanced Radio Access Procedure in Sliced 5G Networks

Vikhrova, Olga; Suraci, Chiara; Tropeano, Angelo; Pizzi, Sara; Samouylov, Konstantin; Araniti, Giuseppe

doi:10.1109/icumt48472.2019.8970776

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…Assuming that the lifetime of each slice type to be an exponentially distributed random variable [31], and slice acceptance is based on the preference of the MNO regarding the SINR requirements. Then, using the dynamic resource percentage threshold scheme, a dynamic RA objective to maximize the UL capacity of eMBB services under the constraint of maximum transmission power of users and guaranteed data rate for URLLC users is given as follows:…”

Section: A the Optimization Problemmentioning

confidence: 99%

An SDN Controller-Based Network Slicing Scheme Using Constrained Reinforcement Learning

Hlophe

Maharaj

2022

IEEE Access

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In order to meet the strong diversification of services that demand network flexibility that will be able to serve the dire need for transmission resources, network slicing was embraced as a plausible solution. Reinforcement learning (RL) has been applied in resource allocation (RA) problems, but has not yet marked the translation from traditional optimization approaches primarily due to its inability to satisfy state constraints. The aim of this article is to address this challenge. This article proposes a logical architecture for network slicing based on software-defined networking (SDN), where an SDN controller controls the network slicing process in a centralized fashion, and manages the resource allocation (RA) process with the help of the slice manager. The considered problem jointly addresses power and channel allocation using a hybrid access mode for ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) slices. Proper assumptions on the arrival rates, packet length distributions, as well as power and delay constraints were used to design the behavior of the reward function to realize a constrained RL approach. Here, the Bellman optimality equation was reformulated into a primaldual optimization problem through the use of Nesterov's smoothing technique and the Legendre-Fenchel transformation. The proposed algorithm shows favorable performance over the traditional RL strategy in attributes favoring eMBB services, i.e., the average bit rate, and significantly outperforms both baselines in attributes favoring URLLC services, i.e., average latency. Systematically, on the power-delay performance evaluation, it shows that it can adapt very well in rapidly time-varying non-Markovian environments and still successfully satisfy the delay constraints of the applications hosted on a slice.

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Section: A the Optimization Problemmentioning

confidence: 99%

An SDN Controller-Based Network Slicing Scheme Using Constrained Reinforcement Learning

Hlophe

Maharaj

2022

IEEE Access

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“…In [ 19 ], the authors considered various resource allocation strategies to more effectively handle the access in the mobile network for different slices: enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and mMTC. Unlike the work presented by the authors of [ 19 ], the RAA procedure does not differently handle the types of slices. Thus, the eMBB, URLLC, and mMTC slices are treated with the same priority level.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike the work presented in [ 10 ], RAA is executed in parallel with the original traditional 4-step Random-Access approach in 5G. The approaches presented in [ 10 , 19 ] are focused only on the access blocking probability. They do not show the elapsed time for all device’s registration and there is no consideration of the energy consumption during the slicing procedure.…”

Section: Related Workmentioning

confidence: 99%

Random-Access Accelerator (RAA): A Framework to Speed Up the Random-Access Procedure in 5G New Radio for IoT mMTC by Enabling Device-To-Device Communications

Medel

Brito

2020

Sensors

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Mobile networks have a great challenge by serving the expected billions of Internet of Things (IoT) devices in the upcoming years. Due to the limited simultaneous access in the mobile networks, the devices should compete between each other for resource allocation during a Random-Access procedure. This contention provokes a non-depreciable delay during the device’s registration because of the great number of collisions experienced. To overcome such a problem, a framework called Random-Access Accelerator (RAA) is proposed in this work, in order to speed up network access in massive Machine Type Communication (mMTC). RAA exploits Device-To-Device (D2D) communications, where devices with already assigned resources act like relays for the rest of devices trying to gain access in the network. The simulation results show an acceleration in the registration procedure of 99%, and a freed space of the allocated spectrum until 74% in comparison with the conventional Random-Access procedure. Besides, it preserves the same device’s energy consumption compared with legacy networks by using a custom version of Bluetooth as a wireless technology for D2D communications. The proposed framework can be taken into account for the standardization of mMTC in Fifth-Generation-New Radio (5G NR).

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“…Other works also studied the coexistence between eMBB and mMTC, e.g. [7], [8] and [9], but being restricted to single antenna receivers as well.…”

Section: Introductionmentioning

confidence: 99%

Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception

Tominaga

Alves

López

et al. 2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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In this work, we study the coexistence in the same Radio Access Network (RAN) of two use cases present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.

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Enhanced Radio Access Procedure in Sliced 5G Networks

Cited by 7 publications

References 11 publications

An SDN Controller-Based Network Slicing Scheme Using Constrained Reinforcement Learning

An SDN Controller-Based Network Slicing Scheme Using Constrained Reinforcement Learning

Random-Access Accelerator (RAA): A Framework to Speed Up the Random-Access Procedure in 5G New Radio for IoT mMTC by Enabling Device-To-Device Communications

Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception

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