Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods

• URL: http://arxiv.org/abs/2001.07698v1
• Date: Tue, 21 Jan 2020 18:58:56 GMT
• Title: Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods
• Authors: Qi Zhou, Jingjie Zhu, Junwen Zhang, Zhensheng Jia, Bernardo Huberman and Gee-Kung Chang
• Abstract summary: A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management. We verify the capability of the proposed scheme under both fixed and dynamic traffic loads scenarios to achieve 1ms average latency.
• Score: 3.723835690294061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management. We verify the capability of the proposed scheme under both fixed and dynamic traffic loads scenarios to achieve <1ms average latency. The RL agent demonstrates an efficient intelligent mechanism to manage the latency, which provides a promising IBA solution for the next-generation access network.

Related papers

• Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR [30.146175299047325]
  We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival. A deep reinforcement learning (DRL) based centralized dynamic scheduler for time-frequency resources is proposed to learn how to schedule the available communication resources.
  arXiv  Detail & Related papers  (2024-03-27T08:57:15Z)
• 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)
• Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning [3.59419219139168]
  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.
  arXiv  Detail & Related papers  (2023-03-03T14:34:25Z)
• Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach [19.01563068819449]
  Key challenge of ultra-dense mmWave network (UDmmWave) is beam management due to high propagation delay limited beam coverage. In this paper, a novel beam management scheme is presented which can theoretically protect user privacy while reducing handoff cost.
  arXiv  Detail & Related papers  (2022-10-04T01:47:33Z)
• 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)
• An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things [19.277349546331557]
  Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. Non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization.
  arXiv  Detail & Related papers  (2022-07-17T16:52:51Z)
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks [124.89036526192268]
  We propose a state-augmented algorithm for solving resource management problems in wireless networks. We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
  arXiv  Detail & Related papers  (2022-07-05T18:02:54Z)
• 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)
• A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory [8.864453148536057]
  3IoT introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (mMTC) applications. We propose a novel FUG allocation based on support machine scheduler (SVM) Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors.
  arXiv  Detail & Related papers  (2021-08-02T11:33:02Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

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.