基于双分支特征融合卷积神经网络的通道代码的盲目识别
Yuwei Ma1, Yingke Lei2, Changming Liu3
1College of Electronic Engineering, National University of Defense Technology, Hefei, 230037, China.
Scientific reports
|January 13, 2026
概括
认知无线电需要对动态频谱进行盲通道编码识别. 一个新的双分支特征融合卷积神经网络 (DBFCNN) 提高了七种常见编码方案的识别精度.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 认知无线电在动态频谱中运行,具有异质信号.
- 盲道编码的识别对于未知编码方案的非合作通信至关重要.
- 现有的识别方法在各种编码类型中缺乏准确性和稳定性.
研究的目的:
- 开发一个强大的框架,用于细粒度识别多个通道编码方案.
- 解决当前方法在准确性和适用于各种编码类型的限制.
- 通过改进的盲人编码识别来增强认知无线电能力.
主要方法:
- 提出了一个双分支特征融合卷积神经网络 (DBFCNN) 框架.
- 采用多尺度扩展卷曲用于远程依赖提取.
- 使用统计分支来提取代码特定的代数特征 (例如,运行长度,).
主要成果:
- DBFCNN在七个共同的通道编码方案中实现了细粒度的识别.
- 两个分支机构的融合代表性提高了分类准确性.
- 与现有基线相比,识别准确度的绝对改善约为5%.
结论:
- 在认知无线电中,DBFCNN框架有效用于盲道编码识别.
- 双分支架构成功地整合了多个规模的空间和统计特征.
- DBFCNN提供了一种可行且强大的解决方案,用于识别各种道编码方案.
相关概念视频
Force Classification
2.3K
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,...
2.3K
Classification of Signals
1.3K
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...
1.3K
Neural Circuits
2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Uniform Depth Channel Flow
525
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
525
IR Frequency Region: Fingerprint Region
1.8K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.8K
Uniform Depth Channel Flow: Problem Solving
426
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
426
