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相关概念视频

Properties of Fourier Transform I01:21

Properties of Fourier Transform I

182
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
182
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

443
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
443
Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
883
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
280
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

162
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
162
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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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....
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一种基于变压器的通道估计方法用于OTFS系统.

Teng Sun1, Jiebiao Lv2, Tao Zhou2

  • 1The 54th Research Institute of CETC, Shijiazhuang 050081, China.

Entropy (Basel, Switzerland)
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了基于变压器的频道估计对直角时间频率空间 (OTFS) 系统,提高了高流动性场景的准确性. 新方法提高了可靠性,并优于现有的深度学习技术.

关键词:
这就是OTFS的OTFS.频道估计 频道估计深度学习是一种深度学习.变压器的变压器是一个变压器.

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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:

  • 坐标时频空间 (OTFS) 调制在高流动性环境中提供了强大的性能.
  • 准确的通道估计对于在OTFS系统中保持通信可靠性至关重要.
  • 现有的通道估计方法与高流动性场景的复杂性作斗争.

研究的目的:

  • 为OTFS系统提出一种新的基于变压器的通道估计方法.
  • 为了利用时间相关性来增强道响应预测.
  • 提高高流动性无线通信中通道估计的准确性和效率.

主要方法:

  • 使用值方法进行初始通道估计.
  • 开发了一种利用时间通道相关性的通道响应预测方法.
  • 采用了专门的变压器神经网络来改进初步估计.
  • 与传统和基于深度学习的方法进行性能比较.

主要成果:

  • 拟议的基于变压器的方法明显优于值方法和其他深度学习方法.
  • 在正常化平均平方误差 (NMSE) 和比特误差率 (BER) 方面取得了优异的结果.
  • 在高精度和可接受的计算复杂性之间展示了有利的平衡.

结论:

  • 基于变压器的通道估计方法为OTFS系统提供了重大进步.
  • 这种方法有效地解决了高流动性环境中通道估计的挑战.
  • 为未来可靠的无线通信系统提供了有希望的解决方案.