PID:一个参数高效的隔离域-信号调制分类增量学习框架
IEEE transactions on neural networks and learning systems
|October 6, 2025
概括
本研究引入了用于信号调制分类 (SMC) 的新的域增量学习 (DIL) 方法. 拟议的参数有效隔离 (PID) 方法有效地防止在不断变化的通信环境中发生灾难性遗忘.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 通信工程 通信工程
背景情况:
- 深度神经网络 (DNN) 在信号调制分类 (SMC) 中表现有前途.
- 传统的SMC方法在不断的数据分布转移和灾难性的遗忘中扎.
- 诸如认知无线电和网络防御等现实世界的应用需要适应性SMC.
研究的目的:
- 为信号调制分类 (SMC) 提出第一个域增量学习 (DIL) 范式.
- 开发一个参数效率方法 (PID),使其能够快速适应新的场景,同时保持先前场景的性能.
主要方法:
- 引入了基于参数空间分解的分类器 (PSD),以将模型参数分为基数和系数.
- 结基和微调的低维系数,以减轻灾难性的遗忘.
- 设计了一个场景感知域控制器 (SDC) 来选择每个样本的域特定系数.
主要成果:
- 拟议的参数有效隔离 (PID) 方法显著减少了SMC中的灾难性遗忘.
- 通过PID,SMC模型能够快速适应新的通信场景.
- 在广泛的实验中实现了最先进的 (SOTA) 整体性能.
结论:
- 新的DIL范式和PID方法为在动态环境中适应性SMC提供了有效的解决方案.
- PID成功地将适应新领域与保留以前领域的知识相平衡.
- 该方法在现实世界通信信号分类中表现出卓越的性能和稳定性.
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