Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

Filali, Abderrahime; Nour, Boubakr; Cherkaoui, Soumaya; Kobbane, Abdellatif

doi:10.48550/arxiv.2202.06439

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (Filali et al, 2022) have recommended Radio Access Network (RAN) slicing-based two-level approach in the Open-RAN (O-RAN) framework for assigning the validation as well as communication in the RAN sources. In every slicing phase of RAN, the resource slicing issue was developed with the help of the Markov decision approach and learning approach in resolving the problems.…”

Section: Related Workmentioning

confidence: 99%

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

Udayakumar

Ramamoorthy

2023

Preprint

View full text Add to dashboard Cite

An intensive computation source has become increasingly important in recent years to meet the time-critical and low-latency needs of Industrial Internet of Things (IIoT) systems. Existing IIoT-based devices are built with limited computational resources, delivering results in a limited fashion when used in highly resource-intensive applications. Since then, a novel concept known as Edge Computing (EC) has been introduced to reduce network latency and alleviate strain on cloud data centers using an EC server located at the network's periphery. The EC server only managed to gather a small number of resources compared to the resource cloud. Without prior context about task deadline and load, an EC server could not optimally handle latency-sensitive and computation-intensive tasks. Additionally, the EC server did not significantly improve overhead minimization when sending data to and from the remote cloud and the user's device. Parallel to the development of EC, nonorthogonal multiple access (NOMA) has been identified as a technique with the potential to substantially increase spectrum efficiency. In this paper, a NOMA-based EC framework for IIoT system is examined, in which multiple task nodes transfer their task via NOMA to multiple edge servers in proximity for execution. As such, this paper aims to develop a joint optimization model for making decisions about task offloading and allocating resources in Industrial edge computing. An adaptive resource allocation decision model (ARADM) based on deep reinforcement learning (DRL) and heuristically modified long short-term memory (H-LSTM) using hybrid Cat and Mouse Dingo Optimization (HCMDO) is proposed to allocate the task optimally. We formulate joint optimization by considering multi-constraint objective function with communication, computation, and cache parameters using HCMDO. Further, these optimal parameters are used in training an H-LSTM along with benchmark dataset. The outcome of the H-LSTM network utilized in DRL to improve convergence speed, accuracy and stability by predicting optimal cost and load. The goal is to minimize service delay, energy consumption, balance load and maximize resource utilization. The experimental results validated the developed model and its ability to improve the quality of resource allocation in Industrial edge computing.

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

Udayakumar

Ramamoorthy

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Nevertheless, due to the dynamics of the RAN environment, in terms of density of users, user requirements, and wireless channel transmission conditions, RAN slicing remains a significantly challenging problem for MVNOs. These stochastic RAN environment factors have a major impact on the accuracy of the RL models, which decreases the performance of radio resource allocation to the users [14]- [19]. Indeed, when a MVNO builds its resource allocation RL model using training datasets related only to its users' behavior and its surrounding environment, the accuracy of the model may be limited.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning for RAN Slicing in Beyond 5G Networks

Abouaomar¹,

Taïk²,

Filali³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Radio access network (RAN) slicing allows the division of the network into several logical networks tailored to different and varying service requirements in a sustainable way. It is thereby considered a key enabler of 5G and next generation networks. However, determining optimal strategies for RAN slicing remains a challenging issue. Using machine learning algorithms to address such a difficult problem is promising. However, due to the large differences imposed by RAN deployments and the disparity of their required services it is difficult to utilize the same slicing model across all the covered areas. Moreover, the data collected by each mobile virtual network operator (MVNO) in different areas is mostly limited and rarely shared among operators. Federated learning presents new opportunities for MVNOs to benefit from distributed training. In this paper, we propose a federated deep reinforcement learning (FDRL) approach to train bandwidth allocation models among MVNOs based on their interactions with their users. We evaluate the proposed approach through extensive simulations to show the importance of such collaboration in building efficient network slicing models.

show abstract

Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

Cited by 2 publications

References 12 publications

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

Federated Learning for RAN Slicing in Beyond 5G Networks

Contact Info

Product

Resources

About