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相关概念视频

Linear time-invariant Systems01:23

Linear time-invariant Systems

297
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
297
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

107
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
107
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

282
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
282
Reinforcement Schedules01:24

Reinforcement Schedules

208
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
208
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

240
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
240
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

115
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
115

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Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
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强化学习辅助道估计器在时间变化的MIMO系统中.

Tae-Kyoung Kim1, Moonsik Min2

  • 1Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括

本研究介绍了动态MIMO系统的强化学习通道估计器. 它有效地选择数据符号,以提高频道估计在时间变化的环境中的准确性.

科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 准确的通道估计对于多输入多输出 (MIMO) 系统至关重要,特别是在动态环境中.
  • 传统的数据辅助通道估计方法与现代无线通道的复杂性和时间变化的性质作斗争.

研究的目的:

  • 为时间变化的MIMO系统开发一种新的通道估计器,克服现有方法的局限性.
  • 通过智能选择检测到的数据符号来提高通道估计的准确性和效率.

主要方法:

  • 制定一个优化问题,以尽量减少数据辅助通道估计错误.
  • 使用马尔科夫决策过程开发一个顺序符号选择策略.
  • 关于强化学习算法的建议,用于优化政策计算的状态元素精细化.

主要成果:

  • 拟议的强化学习辅助道估计器显著优于传统方法.
  • 该估计器有效地捕获并适应动态MIMO系统中的通道变化.
  • 在道估计准确度和系统性能方面有明显的改进.

结论:

  • 强化学习为时间变化的MIMO系统中的自适应通道估计提供了一种强大的方法.
关键词:
数据辅助的道估计一级高斯马尔科夫通道模型非代方法的方法.强化学习是一种强化学习.

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  • 拟议的方法为复杂的通道条件提供了计算高效和有效的解决方案.
  • 这项工作提升了在动态环境中运行的无线通信系统的能力.