Signal detection and modulation classification are two crucial tasks in
various wireless communication systems. Different from prior works that
investigate them independently, this paper studies the joint signal detection
and automatic modulation classification (AMC) by considering a realistic and
complex scenario, in which multiple signals with different modulation schemes
coexist at different carrier frequencies. We first generate a coexisting
RADIOML dataset (CRML23) to facilitate the joint design. Different from the
publicly available AMC dataset ignoring the signal detection step and
containing only one signal, our synthetic dataset covers the more realistic
multiple-signal coexisting scenario. Then, we present a joint framework for
detection and classification (JDM) for such a multiple-signal coexisting
environment, which consists of two modules for signal detection and AMC,
respectively. In particular, these two modules are interconnected using a
designated data structure called "proposal". Finally, we conduct extensive
simulations over the newly developed dataset, which demonstrate the
effectiveness of our designs. Our code and dataset are now available as
open-source (this https URL).

信号检测和调制分类是各种无线通信系统中的两个关键任务，本文通过考虑含有不同调制方案的多个信号在不同载波频率下共存的真实和复杂情景，研究了联合信号检测和自动调制分类 (AMC)，并提出了一个联合框架用于在这种多信号共存环境中进行检测和分类，最后通过广泛的模拟实验验证了设计的有效性。

通过深度学习联合信号检测和自动调制识别

Joint Signal Detection and Automatic Modulation Classification via Deep  Learning

Stereo matching and flow estimation are two essential tasks for scene
understanding, spatially in 3D and temporally in motion. Existing approaches
have been focused on the unsupervised setting due to the limited resource to
obtain the large-scale ground truth data. To construct a self-learnable
objective, co-related tasks are often linked together to form a joint
framework. However, the prior work usually utilizes independent networks for
each task, thus not allowing to learn shared feature representations across
models. In this paper, we propose a single and principled network to jointly
learn spatiotemporal correspondence for stereo matching and flow estimation,
with a newly designed geometric connection as the unsupervised signal for
temporally adjacent stereo pairs. We show that our method performs favorably
against several state-of-the-art baselines for both unsupervised depth and flow
estimation on the KITTI benchmark dataset.

本文提出一种单一和原则性网络，用于联合学习立体匹配和流估计的时空对应关系，通过设计的几何连接作为临近时间内的立体对的无监督信号。结果表明，我们的方法对于 KITTI 基准数据集上的无监督深度和流估计都优于几种最先进的基线方法。