Published on Sun Mar 29 2020

Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G

Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

In 5th generation cellular networks, base stations need real-time optimization of resources. A fully-connected neural network (NN) cannot fully guarantee the QoS requirements. To tackle this problem, we propose a cascaded structure of NNs.

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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.

Thu Aug 02 2018
Machine Learning
Deep Learning for Radio Resource Allocation in Multi-Cell Networks
Deep learning (DL) is a powerful tool where amulti-layer neural network can be trained to model a resource management algorithm using network data. Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G) networks will require a paradigm shift from traditional resource allocations.
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Sun Nov 18 2018
Artificial Intelligence
Realtime Scheduling and Power Allocation Using Deep Neural Networks
With the increasing number of base stations (BSs) and network densification in 5G, interference management using link scheduling and power control are vital for better utilization of radio resources. Due to high computation time, previous methods are useful for research purposes but impractical for real time usage.
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Wed Nov 13 2019
Neural Networks
Radio Resource Allocation in 5G New Radio: A Neural Networks Based Approach)
The minimum frequency-time unit that can be allocated to User Equipments(UEs) in the fifth generation (5G) cellular networks is a Resource Block (RB) A RB is a channel composed of a set of OFDM subcarriers for a given time slot.
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Wed Nov 25 2020
Artificial Intelligence
Deep Learning-based Resource Allocation For Device-to-Device Communication
A deep learning (DL) framework for the optimization of the. resource allocation in multi-channel cellular systems with device-to-device.(D2D) communication is proposed. The channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are both optimized.
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Sat May 30 2020
Machine Learning
Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints
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Sat Feb 20 2021
Machine Learning
Deep Learning-based Power Control for Cell-Free Massive MIMO Networks
A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple- input multiple-output system is proposed. The novel unsupervised learning-based approach does not require optimal power allocations to be known during model training.
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