Open-ICL: Open-Set Modulation Classification via Incremental Contrastive Learning

Chen Yang

doi:10.1109/TNNLS.2024.3414942

Open-ICL: Open-Set Modulation Classification via Incremental Contrastive Learning

2024-01-01

0

PRE

AI

DOI：10.1109/TNNLS.2024.3414942

原文链接

原文求助

摘要

En 中文

Open-set modulation classification (OMC) of signals is a challenging task for handling unknown modulation types that are not included in the training dataset. This article proposes an incremental contrastive learning method for OMC, called Open-ICL, to accurately identify unknown modulation types of signals. First, a dual-path 1-D network (DONet) with a classification path (CLP) and a contrast path (COP) is designed to learn discriminative signal features cooperatively. In the COP, the deep features of the input signal are compared with the semantic feature centers (SFCs) of known classes calculated from the network, to infer its signal novelty. An unknown signal bank (USB) is defined to store unknown signals, and a novel moving intersection algorithm (MIA) is proposed to dynamically select reliable unknown signals for the USB. The unknown instances, together with SFCs, are continuously optimized and updated, facilitating the process of incremental learning. Furthermore, a dynamic adaptive threshold (DAT) strategy is proposed to enable Open-ICL to adaptively learn changing signal distributions. Extensive experiments are performed on two benchmark datasets, and the results demonstrate the effectiveness of Open-ICL for OMC.

Keyword:

Modulation

Contrastive learning

Universal Serial Bus

Task analysis

Feature extraction

Training

Reliability

Adaptive threshold

incremental contrastive learning

modulation classification

moving intersection

open set

signal novelty

Open-ICL: Open-Set Modulation Classification via Incremental Contrastive Learning

摘要

期刊

机构

引用论文