Federated Reinforcement Learning for Resource Allocation in V2X Networks

• URL: http://arxiv.org/abs/2310.09858v1
• Date: Sun, 15 Oct 2023 15:26:54 GMT
• Title: Federated Reinforcement Learning for Resource Allocation in V2X Networks
• Authors: Kaidi Xu, Shenglong Zhou, and Geoffrey Ye Li
• Abstract summary: Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning. In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning.
• Score: 46.6256432514037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the inexact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and accelerated by an adaptive step size calculated from their second moments. The developed algorithm, PASM, is proven to be convergent under mild conditions and has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.

Related papers

• Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II [52.083337333478674]
  This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW) The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL.
  arXiv  Detail & Related papers  (2024-07-18T02:46:06Z)
• Semi-Supervised Learning Approach for Efficient Resource Allocation with Network Slicing in O-RAN [5.1435595246496595]
  This paper introduces an innovative approach to the resource allocation problem. It aims to coordinate multiple independent x-applications (xAPPs) for network slicing and resource allocation in the Open Radio Access Network (O-RAN)
  arXiv  Detail & Related papers  (2024-01-16T22:23:27Z)
• Multi-Resource Allocation for On-Device Distributed Federated Learning Systems [79.02994855744848]
  This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively.
  arXiv  Detail & Related papers  (2022-11-01T14:16:05Z)
• Graph Reinforcement Learning for Radio Resource Allocation [13.290246410488727]
  We resort to graph reinforcement learning for exploiting two kinds of relational priors inherent in many problems in wireless communications. To design graph reinforcement learning framework systematically, we first conceive a method to transform state matrix into state graph. We then propose a general method for graph neural networks to satisfy desirable permutation properties.
  arXiv  Detail & Related papers  (2022-03-08T08:02:54Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement [0.7885276250519428]
  We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization based on the Power of Two Choices principle. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-05-14T10:04:17Z)
• Deep Learning-based Resource Allocation For Device-to-Device Communication [66.74874646973593]
  We propose a framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication. A deep learning (DL) framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Our simulation results confirm that near-optimal performance can be attained with low time, which underlines the real-time capability of the proposed scheme.
  arXiv  Detail & Related papers  (2020-11-25T14:19:23Z)
• DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing [20.723527476555574]
  Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. We propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL)
  arXiv  Detail & Related papers  (2020-08-17T20:52:19Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)

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.