Graph Reinforcement Learning for Radio Resource Allocation

• URL: http://arxiv.org/abs/2203.03906v2
• Date: Sat, 23 Sep 2023 14:23:13 GMT
• Title: Graph Reinforcement Learning for Radio Resource Allocation
• Authors: Jianyu Zhao and Chenyang Yang
• Abstract summary: 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.
• Score: 13.290246410488727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep reinforcement learning (DRL) for resource allocation has been investigated extensively owing to its ability of handling model-free and end-to-end problems. Yet the high training complexity of DRL hinders its practical use in dynamic wireless systems. To reduce the training cost, we resort to graph reinforcement learning for exploiting two kinds of relational priors inherent in many problems in wireless communications: topology information and permutation properties. To design graph reinforcement learning framework systematically for harnessing the two priors, we first conceive a method to transform state matrix into state graph, and then propose a general method for graph neural networks to satisfy desirable permutation properties. To demonstrate how to apply the proposed methods, we take deep deterministic policy gradient (DDPG) as an example for optimizing two representative resource allocation problems. One is predictive power allocation that minimizes the energy consumed for ensuring the quality-ofservice of each user that requests video streaming. The other is link scheduling that maximizes the sum-rate for device-to-device communications. Simulation results show that the graph DDPG algorithm converges much faster and needs much lower space complexity than existing DDPG algorithms to achieve the same learning performance.

Related papers

• Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services [55.0337199834612]
  Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
  arXiv  Detail & Related papers  (2024-11-03T07:01:13Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• Federated Reinforcement Learning for Resource Allocation in V2X Networks [46.6256432514037]
  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.
  arXiv  Detail & Related papers  (2023-10-15T15:26:54Z)
• Graph Signal Restoration Using Nested Deep Algorithm Unrolling [85.53158261016331]
  Graph signal processing is a ubiquitous task in many applications such as sensor, social transportation brain networks, point cloud processing, and graph networks. We propose two restoration methods based on convexindependent deep ADMM (ADMM) parameters in the proposed restoration methods are trainable in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-06-30T08:57:01Z)
• 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)
• Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning [22.725452912879376]
  Heterogeneous Ultra-Dense Network (HUDN) can enable higher connectivity density and ultra-high data rates. This paper proposes a novel idea for resolving the joint user association and power control problem. We train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning.
  arXiv  Detail & Related papers  (2021-03-29T06:39:51Z)
• Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks [0.0]
  In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands. We propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the problem. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against state-of-the-art conventional methods.
  arXiv  Detail & Related papers  (2020-11-27T09:49:38Z)
• 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)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)
• Graph Ordering: Towards the Optimal by Learning [69.72656588714155]
  Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, prediction, and community detection. However, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks. In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach.
  arXiv  Detail & Related papers  (2020-01-18T09:14:16Z)

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.