Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, the DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other data domains such as computer vision (in the form of transfer learning) without accounting for the unique characteristics of wireless signals. Self-supervised learning enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available. We present the first self-supervised RF signal representation learning model and apply it to the automatic modulation recognition (AMR) task by specifically formulating a set of transformations to capture the wireless signal characteristics. We show that the sample efficiency (the number of labeled samples required to achieve a certain accuracy performance) of AMR can be significantly increased (almost an order of magnitude) by learning signal representations with self-supervised learning. This translates to substantial time and cost savings. Furthermore, self-supervised learning increases the model accuracy compared to the state-of-the-art DL methods and maintains high accuracy even when a small set of training data samples is used.
翻译:深度学习( DL) 在无线域找到丰富的应用,以提高频谱意识。 通常, DL 模型要么在统计分布后随机初始化,要么在不考虑无线信号独特性特点的情况下,对诸如计算机视觉(传输学习形式)等其他数据领域的任务进行预先培训,而不考虑无线信号的独特性。 自我监督学习有助于学习无线电频率(RF)的有用表达方式,即使只有有限的带有标签的培训数据样本,也能够进行自我监督的RF信号代表学习模式。 我们展示了第一个自我监督的RF信号代表学习模式,并将它应用到自动调节识别(AMR)任务中,具体制定一套转换方法来捕捉无线信号特征。 我们显示,通过学习带有自监督学习的学习的信号表达方式,AMR的样本(达到某种准确性性能所需的标签样本数量)的样本效率(几乎是一定的量)可以大大提高。 这可以节省大量的时间和费用。 此外,自我监督学习提高了模型的准确性,与最先进的DL 方法相比,并保持很高的准确性。