Deep Learning Convolutional Neural Networks for Radio Identification
Deep Learning Convolutional Neural Networks for Radio Identification
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DOI：
10.1109/mcom.2018.1800153
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发表时间：
2018-09-01
影响因子：
11.2
通讯作者：
Chowdhury, Kaushik
中科院分区：
文献类型：
--
作者：
Riyaz, Shamnaz;Sankhe, Kunal;Chowdhury, Kaushik
软件定义无线电（SDR）技术的进步允许对整个处理链进行前所未有的控制，允许修改每个功能块以及对输入波形的变化进行采样。本文描述了一种结合SDR传感能力和机器学习（ML）技术，在名义上类似的设备中唯一识别特定无线电的方法。这种方法的主要好处是ML操作原始I/Q样本，并且仅使用发射器硬件诱导的信号修改来区分设备，这些信号修改作为特定设备的唯一签名。不需要更高级别的解码、特征工程或协议知识，进一步减轻了ID欺骗和共享频谱中多个协议共存的挑战。本文的贡献如下：(i)在模拟研究中修改了典型无线通信处理链中的操作块，以演示我们利用的射频损伤。（ii）利用从sdr实验测试平台编译的空中数据集，提出了优化的深度卷积神经网络架构，并将结果与支持向量机和逻辑回归等替代技术进行了定量比较。描述了增加方法稳健性的研究挑战，以及有效训练的并行处理需要。我们的研究表明，在一个有噪声的多径无线信道上，收发距离在2-50英尺之间变化时，实验精度高达90- 99%。
Advances in software defined radio (SDR) technology allow unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform. This article describes a method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques. The key benefit of this approach is that ML operates on raw I/Q samples and distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher-level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum. The contributions of the article are as follows: (i) The operational blocks in a typical wireless communications processing chain are modified in a simulation study to demonstrate RF impairments, which we exploit. (ii) Using an over-the-air dataset compiled from an experimental testbed of SDRs, an optimized deep convolutional neural network architecture is proposed, and results are quantitatively compared with alternate techniques such as support vector machines and logistic regression. (iii) Research challenges for increasing the robustness of the approach, as well as the parallel processing needs for efficient training, are described. Our work demonstrates up to 90-99 percent experimental accuracy at transmitter-receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel.