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

arXiv: https://arxiv.org/abs/2004.00507 · HTML: https://mlsanity.com/paper/2004.00507/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services](https://mlsanity.com/paper/2208.05438/) (2022)
- [Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks](https://mlsanity.com/paper/2201.11799/) (2022)
- [Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications](https://mlsanity.com/paper/2312.14958/) (2023)
- [Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation](https://mlsanity.com/paper/2401.10253/) (2023)
- [A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands](https://mlsanity.com/paper/2311.04600/) (2023)
- [Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks](https://mlsanity.com/paper/2311.04604/) (2023)

## Abstract (arXiv)

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.