通过LSTM自动编码器的实时无线电技术和调制分类
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder
摘要：
基于检测到的无线电信号来识别通信技术和/或调制方案的类型是在包括频谱分配和无线电干扰减轻的各种应用中遇到的具有挑战性的问题。由于越来越多的发射器类型和现实世界信道对无线电信号的不同影响，它们变得困难。现有的频谱监测技术能够使用部署在各种设置中的紧凑型传感器来获取大量的无线电和实时频谱数据。然而，使用这样的数据来分类发射器类型和检测通信方案的现有技术方法难以以允许它们在低成本计算平台上实现的计算效率实现所需的准确度水平。在本文中，我们提出了一个基于LSTM去噪自动编码器的学习框架，该框架旨在从嘈杂的无线电信号中自动提取稳定和鲁棒的特征，并使用学习到的特征推断调制或技术类型。该算法利用了一个紧凑的神经网络架构，易于实现在一个低成本的计算平台，同时超过国家的最先进的精度。现实的合成以及空中无线电数据的结果表明，所提出的框架可靠，有效地分类接收到的无线电信号，往往表现出优越的性能相比，国家的最先进的方法。
英文摘要：
Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation. They are rendered difficult due to a growing number of emitter types and varied effects of real-world channels upon the radio signal. Existing spectrum monitoring techniques are capable of acquiring massive amounts of radio and real-time spectrum data using compact sensors deployed in a variety of settings. However, state-of-the-art methods that use such data to classify emitter types and detect communication schemes struggle to achieve required levels of accuracy at a computational efficiency that would allow their implementation on low-cost computational platforms. In this paper, we present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals, and infer modulation or technology type using the learned features. The algorithm utilizes a compact neural network architecture readily implemented on a low-cost computational platform while exceeding state-of-the-art accuracy. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals, often demonstrating superior performance compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
