We conduct an in depth study on the performance of deep learning based radio
signal classification for radio communications signals. We consider a rigorous
baseline method using higher order moments and strong boosted gradient tree
classification and compare performance between the two approaches across a
range of configurations and channel impairments. We consider the effects of
carrier frequency offset, symbol rate, and multi-path fading in simulation and
conduct over-the-air measurement of radio classification performance in the lab
using software radios and compare performance and training strategies for both.
Finally we conclude with a discussion of remaining problems, and design
considerations for using such techniques.

深度学习在无线电通信信号分类方面的性能与基线方法比较研究，研究了不同配置和信道失真情况下的分类性能，探讨了载波频率偏移、符号速率和多径衰落等因素对性能的影响，并使用软件无线电进行了室内分类性能的实测，最终讨论了该技术的应用存在的问题和设计考虑。

基于无线信号的深度学习分类技术

Over the Air Deep Learning Based Radio Signal Classification

The learning of domain-invariant representations in the context of domain
adaptation with neural networks is considered. We propose a new regularization
method that minimizes the discrepancy between domain-specific latent feature
representations directly in the hidden activation space. Although some standard
distribution matching approaches exist that can be interpreted as the matching
of weighted sums of moments, e.g. Maximum Mean Discrepancy (MMD), an explicit
order-wise matching of higher order moments has not been considered before. We
propose to match the higher order central moments of probability distributions
by means of order-wise moment differences. Our model does not require
computationally expensive distance and kernel matrix computations. We utilize
the equivalent representation of probability distributions by moment sequences
to define a new distance function, called Central Moment Discrepancy (CMD). We
prove that CMD is a metric on the set of probability distributions on a compact
interval. We further prove that convergence of probability distributions on
compact intervals w.r.t. the new metric implies convergence in distribution of
the respective random variables. We test our approach on two different
benchmark data sets for object recognition (Office) and sentiment analysis of
product reviews (Amazon reviews). CMD achieves a new state-of-the-art
performance on most domain adaptation tasks of Office and outperforms networks
trained with MMD, Variational Fair Autoencoders and Domain Adversarial Neural
Networks on Amazon reviews. In addition, a post-hoc parameter sensitivity
analysis shows that the new approach is stable w.r.t. parameter changes in a
certain interval. The source code of the experiments is publicly available.

本研究提出了一种新的正则化方法，通过匹配概率分布的高阶中心矩的差异来学习域不变的特征表示，实现域自适应。结果表明，这种方法在两个不同的基准数据集上均取得了最新的性能，并显示出稳定性。