论文标题
在损伤下进行调节检测的光神经网络
A light neural network for modulation detection under impairments
论文作者
论文摘要
我们提出了一个神经网络体系结构，能够在I/Q信号的一部分中有效检测调制方案。该网络比在相同或相似任务上工作的其他最先进的体系结构要比其他最先进的体系结构更轻。此外，参数的数量不取决于信号持续时间，该信号持续时间允许处理数据流，并导致信号长度不变网络。此外，我们基于损伤的模拟生成了一个数据集，该数据集的传播通道和解调器可以带到记录的I/Q信号：随机相移，延迟，滚动，滚动，采样率和频率偏移。我们从该数据集中受益，可以训练我们的神经网络，以使其成为不变的损害，并量化其在现实生活中的调制条件下在调制之间分解的准确性。复制结果的数据和代码已公开可用。
We present a neural network architecture able to efficiently detect modulation scheme in a portion of I/Q signals. This network is lighter by up to two orders of magnitude than other state-of-the-art architectures working on the same or similar tasks. Moreover, the number of parameters does not depend on the signal duration, which allows processing stream of data, and results in a signal-length invariant network. In addition, we have generated a dataset based on the simulation of impairments that the propagation channel and the demodulator can bring to recorded I/Q signals: random phase shifts, delays, roll-off, sampling rates, and frequency offsets. We benefit from this dataset to train our neural network to be invariant to impairments and quantify its accuracy at disentangling between modulations under realistic real-life conditions. Data and code to reproduce the results are made publicly available.
