# Deep Learning in Beyond 5G Networks with Image-based Time-Series Representation

arXiv: https://arxiv.org/abs/2104.08584 · HTML: https://mlsanity.com/paper/2104.08584/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Residual-based Adaptive Huber Loss (RAHL) -- Design of an improved Huber loss for CQI prediction in 5G networks](https://mlsanity.com/paper/2408.14718/) (2024)

## Abstract (arXiv)

Towards the network innovation, the Beyond Five-Generation (B5G) networks envision the use of machine learning (ML) methods to predict the network conditions and performance indicators in order to best make decisions and allocate resources. In this paper, we propose a new ML approach to accomplish predictions in B5G networks. Instead of handling the time-series in the network domain of values, we transform them into image thus allowing to apply advanced ML methods of Computer Vision field to reach better predictions in B5G networks. Particularly, we analyze different techniques to transform time-series of network measures into image representation, e.g., Recurrence Plots, Markov Transition Fields, and Gramian Angular Fields. Then, we apply deep neural networks with convolutional layers to predict different 5G radio signal quality indicators. When comparing with other ML-based solutions, experimental results from 5G transmission datasets showed the feasibility and small prediction errors of the proposed approach.