IQ dataset of LTE and Wi-Fi signals

The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important. The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum. At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes. Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection. Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency. To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions. The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm. Our models have achieved up to 99.9\% precision and 88.1\% recall for this classification task. Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.

对依赖于无线电频谱的技术，如移动通信和物联网的需求正呈指数级增长。因此，提供无线电频谱的接入变得日益重要。无线流量不断增长与可用频谱的日益稀缺要求对无线电频谱进行高效管理。与此同时，机器学习（ML）已变得无处不在，并因其识别模式和辅助决策过程的能力而在众多领域得到应用。近期，机器学习算法已被用于解决无线通信领域内的挑战，例如无线电频谱感知，并显示出相较于传统感知方法，如能量检测，更为优越的性能。频谱感知，一种检测和识别在同一无线电频谱波段内传输的不同无线信号的方法，对于提高动态频谱共享至关重要，这有助于增强相同频段内不同无线技术的共享与共存，从而最终提升频谱效率。为此，本研究评估了不同类型的自编码器，例如深度、变分以及长短期记忆（LSTM）自编码器，以识别和区分 LTE 和 Wi-Fi 传输。目标是在一个包含 LTE 和 Wi-Fi 信号组合（IEEE 802.11ax 和 IEEE 802.11ac）的 I/Q 数据集上，从复杂度、精确度和召回率等方面评估不同类型自编码器的性能，以确定最佳算法。我们的模型在此次分类任务中实现了高达 99.9% 的精确率和 88.1% 的召回率。此外，以约 47 秒的最短训练时间，模型适用于在线学习和在动态射频环境中的部署。

搜集汇总

背景与挑战

背景概述

该数据集包含LTE和Wi-Fi信号的IQ数据，用于机器学习和数字信号处理研究，旨在通过自编码器技术提高频谱感知效率。数据集包含实际捕获的LTE信号和模拟生成的Wi-Fi信号，支持动态射频环境中的在线学习和部署。