As wireless communications and interconnected networks become ubiquitous and relied upon, they must also remain secure. Advanced communication systems that use techniques to improve data throughput and minimize latency lend themselves to physical-layer authentication. The stochastic and dynamic nature of the wireless mobile channel provides features that can be extracted through deep learning. We propose a novel method to authenticate transmitters at the physical layer by leveraging channel state information to predict future channel impulse responses. Specifically, we compare the use of recurrent neural networks (RNNs) using long-short term memory (LSTM) and gated recurrent unit (GRU) cells with variations of a conditional generative adversarial network (CGAN) to authenticate transmitters in a mobile environment. Our evaluation shows that standalone RNNs using LSTM and GRU cells are adept at predicting future channel responses, however a CGAN-trained discriminator using GRU cells is able to match the authentication accuracy of a standalone network without using a predefined channel prediction error threshold. Using a discriminator trained by a CGAN with binary cross entropy loss in the discriminator and mean squared error loss in the generator, the neural network was able to authenticate at a 98.5% rate.

中文翻译：

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使用对抗训练的循环神经网络进行信道预测和发射机验证


随着无线通信和互连网络变得无处不在并受到依赖，它们也必须保持安全。使用技术来提高数据吞吐量和最小化延迟的先进通信系统适合物理层身份验证。无线移动信道的随机性和动态性提供了可以通过深度学习提取的特征。我们提出了一种新颖的方法，通过利用信道状态信息来预测未来的信道脉冲响应，在物理层对发射机进行身份验证。具体来说，我们将使用长短期记忆 (LSTM) 和门控循环单元 (GRU) 单元的循环神经网络 (RNN) 与条件生成对抗网络 (CGAN) 的变体进行比较，以验证移动环境中的发射机。我们的评估表明，使用 LSTM 和 GRU 单元的独立 RNN 擅长预测未来的通道响应，但是使用 GRU 单元的 CGAN 训练鉴别器能够在不使用预定义的通道预测误差阈值的情况下匹配独立网络的身份验证准确性。使用由 CGAN 训练的鉴别器，鉴别器中具有二元交叉熵损失，生成器中具有均方误差损失，神经网络能够以 98.5% 的正确率进行验证。