Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning

Wu, Yuansheng; Zhao, Gansen; Ni, Dadong; Du, Junyi

doi:10.1186/s13638-021-01939-x

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…The , per slice is dependent on and values, where their values can be determined either by with initial values or valued from the previous Q-learning algorithm iteration (line 3). In each state, an action is performed based on the ε-greedy policy [40] that represents both the exploration (random action selection) and the exploitation (action with the maximum Q-value) trade-off (lines 4,5). is then calculated according to (20)…”

Section: Rl-based Resource Slicing (Q-learning) Algorithmmentioning

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: Rl-based Resource Slicing (Q-learning) Algorithmmentioning

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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“…Simulation results prove the efficiency of the framework as compared to reactive schemes in reducing unnecessary handovers. User handoff in 5G RAN network slicing has been addressed in [70]. The authors devised an intelligent handoff policy that considers two main constraints: physical resources of base stations and logical connection of network slices.…”

Section: ) Literature Reviewmentioning

confidence: 99%

Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges

Brik

Boutiba

Ksentini

2022

IEEE Open J. Commun. Soc.

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Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN architecture featuring open, softwaredriven, virtual, and intelligent radio access architecture. O-RAN architecture is based on (1) disaggregated RAN functions that run as Virtual Network Function (VNF) and Physical Network Function (PNF); (2) the notion of RAN controller that runs centrally RAN applications such as mobility management, users' scheduling, radio resources allocation, etc. The RAN controller is in charge of enforcing the application decisions by using open interfaces with the RAN functions. One important feature introduced by O-RAN is the heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent RAN applications that are able to fulfill the Quality of Service (QoS) requirements of the envisioned 5G and beyond network services. In this work, we first give an overview of the evolution of RAN architectures toward 5G and beyond, namely C-RAN, vRAN, and O-RAN. We also compare them based on various perspectives, such as edge support, virtualization, control and management, energy consumption, and AI support. Then, we review existing DL-based solutions addressing the RAN part. We also show how they can be integrated/mapped to the O-RAN architecture since these works were not initially adapted to the O-RAN architecture. In addition, we present two case studies for DL techniques deployment in O-RAN. Furthermore, we describe how the main steps of deployed DL models in O-RAN can be automated, to ensure stable performance of these models, introducing ML system operations (MLOps) concept in O-RAN. Finally, we identify key technical challenges, open issues, and future research directions related to the Artificial Intelligence (AI)-enabled O-RAN architecture.

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Handoffs in Next-Generation Wireless Networks

Mahajan

Din

2022

Proceedings of International Conference on Communication and Artificial Intelligence

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Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning

Cited by 9 publications

References 14 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

Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges

Handoffs in Next-Generation Wireless Networks

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