一种用于损伤条件下调制检测的光神经网络
A light neural network for modulation detection under impairments
摘要：
我们提出了一个神经网络架构，能够有效地检测调制方案的一部分的I/Q信号。该网络比其他处理相同或类似任务的最先进架构轻两个数量级。此外，参数的数量不依赖于信号持续时间，这允许处理数据流，并导致信号长度不变的网络。此外，我们已经生成了一个数据集的基础上的传播信道和解调器可以带来记录的I/Q信号的损伤的模拟：随机相移，延迟，滚降，采样率，和频率偏移。我们从这个数据集中受益，训练我们的神经网络对损伤是不变的，并量化其在现实生活条件下解开调制之间的准确性。复制结果的数据和代码是公开的。
英文摘要：
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.
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。
