# Selection of Waveform Parameters Using Machine Learning for 5G and Beyond

## TL;DR

This paper introduces a machine learning-based method for selecting waveform parameters in 5G and beyond, aiming to enhance system flexibility and customization, supported by a novel dataset generation approach and simulation results.

## Contribution

It presents a new ML-driven selection mechanism for waveform parameters and a simulation-based dataset generation methodology for flexible 5G systems.

## Key findings

- ML-based parameter selection improves flexibility
- Simulation results validate the proposed method
- Dataset generation approach aids ML training

## Abstract

Flexibility is one of the essential requirements in future cellular communications technologies. Providing customized communications solutions for each user and service type cannot be possible without the flexibility in 5G and beyond. Different optimizations need to be done for the flexibility related structures of 5G and beyond systems. In this paper, a novel machine learning (ML) based selection mechanism for the configurable waveform parameters is designed from the flexibility perspective. Moreover, a simulation based dataset generation methodology is proposed for ML systems. Results of computer simulations are presented using the generated dataset.