Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul

• URL: http://arxiv.org/abs/2211.07466v1
• Date: Mon, 14 Nov 2022 15:48:13 GMT
• Title: Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul
• Authors: Nien Fang Cheng, Turgay Pamuklu, Melike Erol-Kantarci
• Abstract summary: Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Communication (URLLC) and Enhanced Mobile Broadband (eMBB) This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices.
• Score: 4.254099382808598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities into independent components. Network slicing for O-RAN can significantly improve performance. Therefore, an advanced resource allocation solution for network slicing in O-RAN is proposed in this study by applying Reinforcement Learning (RL). This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices. Increasing the throughput for certain network slicing can also benefit the end users with a higher average data rate, peak rate, or shorter transmission time. The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.

Related papers

• Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning [7.473473066047965]
  This paper introduces a novel approach to ORAN slicing using distributed deep reinforcement learning (DDRL) Simulation results demonstrate significant improvements in network performance, particularly in reducing violations. This emphasizes the importance of using the prediction rApp and distributed actors' information jointly as part of a dynamic xApp.
  arXiv  Detail & Related papers  (2024-01-12T22:43:07Z)
• A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT [16.167107624956294]
  Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes. Standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication.
  arXiv  Detail & Related papers  (2023-11-21T12:22:04Z)
• Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
  Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
  arXiv  Detail & Related papers  (2023-05-15T22:04:49Z)
• Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach [4.834203844100679]
  We propose a multi-agent multi-armed bandit for load balancing and resource allocation (mmLBRA) scheme. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can operate independently in non-realtime RAN intelligent controller (Non-RT RIC) and near-RT RIC, respectively.
  arXiv  Detail & Related papers  (2023-03-25T04:42:30Z)
• Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing [1.7214664783818676]
  This work presents a UAV-assisted 5G network, where the aerial base stations (UAV-BS) are empowered with network slicing capabilities. A first application of multi-agent and multi-decision deep reinforcement learning for UAV-BS in a network slicing context is introduced. The performance of the presented strategy have been tested and compared to benchmarks, highlighting a higher percentage of satisfied users (at least 27% more) in a variety of scenarios.
  arXiv  Detail & Related papers  (2022-11-15T06:50:00Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• 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)
• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)
• Self-play Learning Strategies for Resource Assignment in Open-RAN Networks [3.763743638851161]
  Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs)
  arXiv  Detail & Related papers  (2021-03-03T19:31:29Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)

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.