On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

• URL: http://arxiv.org/abs/2208.01785v1
• Date: Tue, 2 Aug 2022 23:40:03 GMT
• Title: On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
• Authors: Longfei Ma, Nan Cheng, Xiucheng Wang, Ruijin Sun, and Ning Lu
• Abstract summary: We study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. A dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration.
• Score: 13.318287511072354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning.

Related papers

• Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services [5.80147190706865]
  6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies. This paper proposes an online learning framework to optimize the allocation of computational and communication resources to AI services.
  arXiv  Detail & Related papers  (2024-10-20T14:38:54Z)
• DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation [58.62766376631344]
  We propose a customized wireless network intent (WNI-G) model to address different state variations of wireless communication networks. Extensive simulation achieves greater stability in spectral efficiency and variations of traditional DRL models in dynamic communication systems.
  arXiv  Detail & Related papers  (2024-10-18T14:04:38Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G [15.753216159980434]
  Growing concerns about increased energy consumption in computing and networking. The expected surge in connected devices and resource-demanding applications presents unprecedented challenges for energy resources. We investigate the joint problem of service instance placement and assignment, path selection, and request prioritization, dubbed PIRA.
  arXiv  Detail & Related papers  (2024-02-10T12:05:52Z)
• Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks [11.89735327420275]
  This work investigates the sum-rate of flexible networks without static time scheduling. Motivated by the recent success of Graph Networks Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net.
  arXiv  Detail & Related papers  (2023-01-20T12:49:21Z)
• 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)
• CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing [19.990451009223573]
  Network slicing is proposed as a promising solution for resource utilization in 5G and future networks. We formulate the problem as a Constrained Markov Decision Process (CMDP) without knowing models and hidden structures. We propose to solve the problem using CLARA, a Constrained reinforcement LeArning based Resource Allocation algorithm.
  arXiv  Detail & Related papers  (2021-11-16T11:54:09Z)
• Offline Contextual Bandits for Wireless Network Optimization [107.24086150482843]
  In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context.
  arXiv  Detail & Related papers  (2021-11-11T11:31:20Z)
• Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services [10.718353079920007]
  This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.
  arXiv  Detail & Related papers  (2021-03-18T14:18:34Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)
• Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G [53.23237216769839]
  We develop a deep learning framework to approximate the optimal resource allocation policy for base stations. We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
  arXiv  Detail & Related papers  (2020-03-29T04:48:22Z)

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.