Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2303.01960v1
• Date: Fri, 3 Mar 2023 14:34:25 GMT
• Title: Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning
• Authors: Ibrahim Tamim, Sam Aleyadeh, Abdallah Shami
• Abstract summary: Open RAN (O-RAN) is a promising paradigm for building an intelligent RAN architecture. This paper presents a Machine Learning (ML)-based Traffic Steering (TS) scheme to predict network congestion and then steer O-RAN traffic to avoid it and reduce the expected delay. Our solution is evaluated against traditional reactive TS approaches that are offered as xApps in O-RAN and shows an average of 15.81 percent decrease in queuing delay across all deployed SFCs.
• Score: 3.59419219139168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of Next-Generation Networks is to improve upon the current networking paradigm, especially in providing higher data rates, near-real-time latencies, and near-perfect quality of service. However, existing radio access network (RAN) architectures lack sufficient flexibility and intelligence to meet those demands. Open RAN (O-RAN) is a promising paradigm for building a virtualized and intelligent RAN architecture. This paper presents a Machine Learning (ML)-based Traffic Steering (TS) scheme to predict network congestion and then proactively steer O-RAN traffic to avoid it and reduce the expected queuing delay. To achieve this, we propose an optimized setup focusing on safeguarding both latency and reliability to serve URLLC applications. The proposed solution consists of a two-tiered ML strategy based on Naive Bayes Classifier and deep Q-learning. Our solution is evaluated against traditional reactive TS approaches that are offered as xApps in O-RAN and shows an average of 15.81 percent decrease in queuing delay across all deployed SFCs.

Related papers

• Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach [20.344810727033327]
  We propose and design a hybrid TL-aided approach to provide safe and accelerated convergence in DRL-based O-RAN slicing. The proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the average initial reward value and the percentage of converged scenarios.
  arXiv  Detail & Related papers  (2023-09-13T18:58:34Z)
• How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing? [20.344810727033327]
  We propose a novel forecasting-aided DRL approach and its respective O-RAN practical deployment workflow to enhance DRL convergence. Our approach shows up to 22.8%, 86.3%, and 300% improvements in the average initial reward value, convergence rate, and number of converged scenarios respectively.
  arXiv  Detail & Related papers  (2023-09-01T14:30:04Z)
• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)
• Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction [72.59079526765487]
  The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services. The main challenge is posed by the uncertainty in the process of URLLC packet generation. We introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor.
  arXiv  Detail & Related papers  (2023-02-15T14:09:55Z)
• Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework [47.57576667752444]
  We jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent steering application in open RAN (O-RAN) Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iv) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization
  arXiv  Detail & Related papers  (2023-02-06T11:37:06Z)
• Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN [11.464582983164991]
  New open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. This paper introduces a novel framework able to manage the network slices through provisioned resources intelligently.
  arXiv  Detail & Related papers  (2022-08-30T17:00:53Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities [19.723551683930776]
  We develop a network slicing model based on a cluster of fog nodes (FNs) coordinated with an edge controller (EC) For each service request in a cluster, the EC decides which FN to execute the task, locally serve the request at the edge, or to reject the task and refer it to the cloud. We propose a deep reinforcement learning (DRL) solution to adaptively learn the optimal slicing policy.
  arXiv  Detail & Related papers  (2020-10-19T23:30:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.