Deep Learning for Radio Resource Allocation with Diverse
Quality-of-Service Requirements in 5G
- URL: http://arxiv.org/abs/2004.00507v1
- Date: Sun, 29 Mar 2020 04:48:22 GMT
- Title: Deep Learning for Radio Resource Allocation with Diverse
Quality-of-Service Requirements in 5G
- Authors: Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, and Branka
Vucetic
- Abstract summary: 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.
- Score: 53.23237216769839
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To accommodate diverse Quality-of-Service (QoS) requirements in the 5th
generation cellular networks, base stations need real-time optimization of
radio resources in time-varying network conditions. This brings high computing
overheads and long processing delays. In this work, we develop a deep learning
framework to approximate the optimal resource allocation policy that minimizes
the total power consumption of a base station by optimizing bandwidth and
transmit power allocation. We find that a fully-connected neural network (NN)
cannot fully guarantee the QoS requirements due to the approximation errors and
quantization errors of the numbers of subcarriers. To tackle this problem, we
propose a cascaded structure of NNs, where the first NN approximates the
optimal bandwidth allocation, and the second NN outputs the transmit power
required to satisfy the QoS requirement with given bandwidth allocation.
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. Simulation results
validate that the cascaded NNs outperform the fully connected NN in terms of
QoS guarantee. In addition, deep transfer learning can reduce the number of
training samples required to train the NNs remarkably.
Related papers
- Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud
Computing [73.7522199491117]
Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources.
The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning.
We propose a resource allocation model to provision quantum computing and networking resources.
arXiv Detail & Related papers (2023-07-25T00:38:46Z) - Learning k-Level Structured Sparse Neural Networks Using Group Envelope Regularization [4.0554893636822]
We introduce a novel approach to deploy large-scale Deep Neural Networks on constrained resources.
The method speeds up inference time and aims to reduce memory demand and power consumption.
arXiv Detail & Related papers (2022-12-25T15:40:05Z) - Tactile Network Resource Allocation enabled by Quantum Annealing based
on ILP Modeling [0.0]
We propose a new framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models.
We map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization problem, which is solvable on quantum annealer (QA)
arXiv Detail & Related papers (2022-12-14T14:12:03Z) - On-Demand Resource Management for 6G Wireless Networks Using
Knowledge-Assisted Dynamic Neural Networks [13.318287511072354]
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.
arXiv Detail & Related papers (2022-08-02T23:40:03Z) - Deep Reinforcement Learning Based Multidimensional Resource Management
for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency.
In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
arXiv Detail & Related papers (2021-09-17T08:55:48Z) - QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning [2.3857747529378917]
We propose a QA-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks.
In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the satisfaction rate of users served by the network.
arXiv Detail & Related papers (2021-07-14T09:18:39Z) - Cognitive Radio Network Throughput Maximization with Deep Reinforcement
Learning [58.44609538048923]
Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT)
To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment.
In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
arXiv Detail & Related papers (2020-07-07T01:49:07Z) - 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) - Wireless Power Control via Counterfactual Optimization of Graph Neural
Networks [124.89036526192268]
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium.
To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture.
We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
arXiv Detail & Related papers (2020-02-17T07:54:39Z)
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.