一个超轻量级的神经网络用于无人机通信中的自动调制分类.
Mengtao Wang1, Shengliang Fang2, Youchen Fan3
1Graduate School, Space Engineering University, Beijing, 101416, China.
Scientific reports
|September 15, 2024
概括
本研究介绍了一种超轻量级神经网络 (ULNN),用于无人机上的自动调制分类 (AMC). ULNN在最小参数下实现高精度,使其适用于资源有限的无人机通信系统.
科学领域:
- 无线通信无线通信
- 深度学习 (Deep Learning) 是一种深度学习.
- 信号处理 信号处理
背景情况:
- 无人机辅助通信通过使用自动调制分类 (AMC) 提高了传输效率.
- 现有的基于深度学习 (DL) 的AMC方法在无人机上面临挑战,因为计算能力和存储能力有限,在准确性和效率之间产生了权衡.
- 资源有限的场景需要轻量级的DL模型,以便在无人机平台上有效的AMC.
研究的目的:
- 开发一个基于DL的轻量级AMC网络,适应资源有限的无人机平台.
- 为解决无人机基于DL的AMC中的准确性-效率矛盾问题.
- 为改善无人机通信提出一个超轻量级神经网络 (ULNN).
主要方法:
- 提出了一个超轻量级神经网络 (ULNN),集成了轻量级卷积结构,注意力机制和跨通道特征融合.
- 引入了数据增强 (DA),使用信号相位偏移来增强模型概括性并防止过拟合.
- 在RML2016.10A数据集上验证了ULNN.
主要成果:
- 拟议的ULNN实现了平均精度为62.83%,只有8815个参数.
- 在信号与噪声比 (SNR) 为10dB的情况下,达到92.11%的峰值分类精度.
- 在显著减少模型尺寸的情况下,证明了高识别准确性.
结论:
- ULNN有效地平衡了高识别精度与轻量级模型架构.
- 拟议的网络非常适合在具有有限计算资源的无人机平台上部署.
- 这项研究有助于推进无人机的高效无线通信,通过优化基于DL的AMC.
更多相关视频
07:49Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
Published on: November 26, 2019
8.0K
06:00Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
Published on: August 27, 2021
5.2K
相关概念视频
Neural Regulation
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Force Classification
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Neurotransmitters
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Classification of Signals
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
