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
- 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) - Dynamic Network-Assisted D2D-Aided Coded Distributed Learning [59.29409589861241]
We propose a novel device-to-device (D2D)-aided coded federated learning method (D2D-CFL) for load balancing across devices.
We derive an optimal compression rate for achieving minimum processing time and establish its connection with the convergence time.
Our proposed method is beneficial for real-time collaborative applications, where the users continuously generate training data.
arXiv Detail & Related papers (2021-11-26T18:44:59Z) - 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) - 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.