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.

依赖无线电频谱的移动通信、物联网（IoT）等技术的需求呈指数级增长，获取无线电频谱接入的重要性随之与日俱增。持续膨胀的无线流量与日益稀缺的可用频谱资源，亟需对无线电频谱实施高效管理。与此同时，机器学习（ML）凭借识别模式、辅助决策的能力，正变得无处不在，并已在众多领域得到广泛应用。近期，机器学习算法被用于解决无线通信领域的诸多挑战，例如无线电频谱感知，且其性能表现优于能量检测等传统感知方法。频谱感知是一种用于检测并识别同一无线电频谱频段内传输的各类无线信号的技术，对优化动态频谱共享至关重要；动态频谱共享有望提升不同无线技术在同一频段内的共享与共存能力，最终改善频谱利用效率。为此，本研究对多种自编码器展开评估，包括深度自编码器、变分自编码器以及长短期记忆（LSTM）自编码器，以识别并区分长期演进（LTE）与Wi-Fi传输信号。本研究的目标是，在包含LTE信号以及Wi-Fi信号（IEEE 802.11ax与IEEE 802.11ac）组合的I/Q数据集上，探究不同自编码器在分类任务中的复杂度、精确率与召回率表现，从而筛选出最优算法。本研究的模型在该分类任务中最高实现了99.9%的精确率与88.1%的召回率。此外，最短训练时长仅约47秒，使得这些模型适用于动态射频（RF）环境下的在线学习与部署。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于机器学习研究的I/Q信号数据集，包含LTE和Wi-Fi（IEEE 802.11ax和IEEE 802.11ac）信号，旨在支持频谱感知和信号分类任务。数据集由两个文件组成：LTE信号通过USRP B210设备捕获，格式为np.complex64；Wi-Fi信号由MATLAB生成，格式为二进制MATLAB文件。研究使用自编码器算法进行信号分类，模型在实验中表现出高精确度（最高99.9%）和较短的训练时间，适用于动态无线环境中的实时应用。