相关实验视频

Updated: Jul 11, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

使用GNN进行频谱预测的联合多维模式

Xiaomin Wen^1,2, Shengliang Fang², Zhaojing Xu^1,2

¹Graduate School, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)

|November 14, 2023

概括

本研究介绍了TensorGCN-LSTM,这是一个深度学习模型,用于二级用户 (SUs) 高效的无线频谱管理. 它使用空间,频率和时间数据相关性准确预测频谱可用性.

科学领域:

无线通信是一种无线通信.
信号处理信号处理
机器学习是机器学习.

背景情况:

对于认知无线电二级用户 (SUs) 来说,高效的频谱接入至关重要.
来自US的电磁频谱数据显示出跨时间,频率和空间的相关性.
对频谱状态的多维预测是有效利用资源的关键.

研究的目的:

提出一种新的深度学习混合模型,TensorGCN-LSTM,用于认知无线电频谱传感.
通过利用频谱数据中的多维相关性,有效地预测频谱状态.

主要方法:

开发了一个使用张量数据结构的TensorGCN-LSTM模型.
构建了两个图形结构,代表了SU的空间和频率域.
采用图形卷积运算来提取特征,以及LSTM用于融合空间,频率和时间特征.

主要成果:

与基线模型 (LSTM,GCN,GC-LSTM) 相比,TensorGCN-LSTM模型显示出更高的预测性能.
实现了较低的根平均平方误差 (RMSE),表明预测准确度很高.
相关系数R2为0.8753证实了该模型的可行性和有效性.

结论:

拟议的TensorGCN-LSTM模型通过捕获多维相关性来有效预测无线频谱使用情况.
这种方法提高了对认知无线电用户频谱资源分配的效率.
该模型的性能验证了其对现实世界认知无线电应用的潜力.

关键词:

图表卷积神经网络卷积神经网络长期短期记忆长期短期记忆电源频谱预测和预测张量图的张量图是张量图.

更多相关视频

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

相关概念视频

Classification of Signals

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...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Multiple Bar Graph

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...