In the realm of amateur radio, the effective classification of signals and
the mitigation of noise play crucial roles in ensuring reliable communication.
Traditional methods for signal classification and noise reduction often rely on
manual intervention and predefined thresholds, which can be labor-intensive and
less adaptable to dynamic radio environments. In this paper, we explore the
application of machine learning techniques for signal classification and noise
reduction in amateur radio operations. We investigate the feasibility and
effectiveness of employing supervised and unsupervised learning algorithms to
automatically differentiate between desired signals and unwanted interference,
as well as to reduce the impact of noise on received transmissions.
Experimental results demonstrate the potential of machine learning approaches
to enhance the efficiency and robustness of amateur radio communication
systems, paving the way for more intelligent and adaptive radio solutions in
the amateur radio community.

通过机器学习技术，研究探索在业余无线电运营中应用信号分类和降噪，实验结果证明机器学习方法有潜力提高业余无线电通信系统的效率和鲁棒性。

利用机器学习进行业余无线电信号分类和噪声降低

Utilizing Machine Learning for Signal Classification and Noise Reduction  in Amateur Radio

Graph neural networks (GNNs) have emerged as a powerful tool for tasks such
as node classification and graph classification. However, much less work has
been done on signal classification, where the data consists of many functions
(referred to as signals) defined on the vertices of a single graph. These tasks
require networks designed differently from those designed for traditional GNN
tasks. Indeed, traditional GNNs rely on localized low-pass filters, and signals
of interest may have intricate multi-frequency behavior and exhibit long range
interactions. This motivates us to introduce the BLIS-Net (Bi-Lipschitz
Scattering Net), a novel GNN that builds on the previously introduced geometric
scattering transform. Our network is able to capture both local and global
signal structure and is able to capture both low-frequency and high-frequency
information. We make several crucial changes to the original geometric
scattering architecture which we prove increase the ability of our network to
capture information about the input signal and show that BLIS-Net achieves
superior performance on both synthetic and real-world data sets based on
traffic flow and fMRI data.

基于几何散射变换的 BLIS-Net 是一种新颖的图神经网络，能够捕捉局部和全局信号结构，获得低频和高频信息，并在基于流量和 fMRI 数据的合成和真实数据集上展现出卓越性能。

BLIS-Net: 图上信号的分类与分析

BLIS-Net: Classifying and Analyzing Signals on Graphs

The manifold scattering transform is a deep feature extractor for data
defined on a Riemannian manifold. It is one of the first examples of extending
convolutional neural network-like operators to general manifolds. The initial
work on this model focused primarily on its theoretical stability and
invariance properties but did not provide methods for its numerical
implementation except in the case of two-dimensional surfaces with predefined
meshes. In this work, we present practical schemes, based on the theory of
diffusion maps, for implementing the manifold scattering transform to datasets
arising in naturalistic systems, such as single cell genetics, where the data
is a high-dimensional point cloud modeled as lying on a low-dimensional
manifold. We show that our methods are effective for signal classification and
manifold classification tasks.

本文介绍了一种用于处理定义在黎曼流形上的数据的深度特征提取器 —— 旋转不变散射变换，并提出了一些基于扩散映射理论的实用方案，可以在自然系统中应用于点云数据上进行信号分类和流形分类任务。

高维点云数据的多流形散射变换

The Manifold Scattering Transform for High-Dimensional Point Cloud Data

Dynamic spectrum access (DSA) benefits from detection and classification of
interference sources including in-network users, out-network users, and jammers
that may all coexist in a wireless network. We present a deep learning based
signal (modulation) classification solution in a realistic wireless network
setting, where 1) signal types may change over time; 2) some signal types may
be unknown for which there is no training data; 3) signals may be spoofed such
as the smart jammers replaying other signal types; and 4) different signal
types may be superimposed due to the interference from concurrent
transmissions. For case 1, we apply continual learning and train a
Convolutional Neural Network (CNN) using an Elastic Weight Consolidation (EWC)
based loss. For case 2, we detect unknown signals via outlier detection applied
to the outputs of convolutional layers using Minimum Covariance Determinant
(MCD) and k-means clustering methods. For case 3, we extend the CNN structure
to capture phase shifts due to radio hardware effects to identify the spoofing
signal sources. For case 4, we apply blind source separation using Independent
Component Analysis (ICA) to separate interfering signals. We utilize the signal
classification results in a distributed scheduling protocol, where in-network
(secondary) users employ signal classification scores to make channel access
decisions and share the spectrum with each other while avoiding interference
with out-network (primary) users and jammers. Compared with benchmark
TDMA-based schemes, we show that distributed scheduling constructed upon signal
classification results provides major improvements to in-network user
throughput and out-network user success ratio.

本文提出了一种基于深度学习和信号分类技术的动态频谱接入方案，能够在无线网络中精确检测和分类干扰源，并应用于分布式调度协议中，提高网络吞吐量和用户成功率。