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Maximum Power Transfer01:16

Maximum Power Transfer

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
811
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

720
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Propagation Speed of Electromagnetic Waves01:30

Propagation Speed of Electromagnetic Waves

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Electromagnetic waves are consistent with Ampere's law. Assuming there is no conduction current Ampere's law is given as:
4.6K
Carrier Generation and Recombination01:22

Carrier Generation and Recombination

1.2K
Carrier generation is the process by which electron-hole pairs (EHPs) are created within the semiconductor. In direct-bandgap semiconductors, such as gallium arsenide (GaAs), this occurs efficiently when energy absorption prompts valence electrons to leap into the conduction band, leaving behind holes.
This process is given by the generation rate G and is efficient due to the conservation of momentum between the valence band maximum and conduction band minimum.
Indirect generation involves an...
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Diode: Forward bias01:20

Diode: Forward bias

2.0K
In semiconductor devices, diodes play a crucial role in directing current flow, and its operation is primarily categorized into forward bias and reverse bias. A diode is said to be forward-biased when its p-type region is connected to the positive terminal of a battery and its n-type region is linked to the negative terminal. This configuration reduces the potential barrier within the diode, allowing current to flow easily from the p to the n-type region.
The behavior of a diode in forward bias...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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相关实验视频

Updated: Jan 11, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

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具有学习信念传播解码器的端到端DAE-LDPC-OFDM收发器,用于稳固和节能的无线通信.

Mohaimen Mohammed1, Mesut Çevik1

  • 1Electrical and Computer Engineering, Altinbas University, 34217 Istanbul, Turkey.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括

本研究介绍了一种深度自编码器-LDPC-OFDM系统,配有学习信念传播解码器,用于自适应无线通信. 它在具有挑战性的通道条件下实现了卓越的性能,能源效率和稳定性.

关键词:
5G/6G通信系统的通信系统低密度平价检查 (LDPC) 是一种方法.自动编码器 (AE) 是一个自动编码器.比特错误率 (BER) 是一个比特错误率.区块错误率 (BLER) 区块错误率 (BLER) 区块错误率 (BLER) 区块错误率 (BLER) 区块错误率 (BLER) 区块错误率 (BLER) 区块错误率 (BLER) 区块错误率 (BLER)终端到终端优化优化学习信念传播 (BP) 解码器解码器正角频率分割多重复合 (OFDM) 是一种频率分割多重复合技术.峰值与平均功率比率 (PAPR) 是指

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相关实验视频

Last Updated: Jan 11, 2026

Quasi-light Storage for Optical Data Packets
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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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科学领域:

  • 无线通信工程 无线通信工程
  • 机器学习用于通信.
  • 信号处理 信号处理

背景情况:

  • 传统的无线系统通常会独立处理编码,调制和解码.
  • 现有的系统难以动态适应变化的通道和噪声条件.
  • 未来网络需要强大,节能和适应性的通信架构.

研究的目的:

  • 提出一个新的深度自编码器-LDPC-OFDM收发器架构.
  • 整合一个学习的信念传播解码器,以提高性能.
  • 为了实现强大,节能和适应性的无线通信.

主要方法:

  • 编码,调制和解码组件的端到端联合优化.
  • 集成一个学习的信念传播 (BP) 解码器与可训练的参数.
  • 代的消息传递过程,用于对日志概率 (LLR) 统计的自适应性改进.

主要成果:

  • 在10dB SNR时,实现了1.72%的比特错误率 (BER) 和2.95%的区块错误率 (BLER).
  • 超越了最先进的模型 (变压器-OFDM,CNN-OFDM,GRU-OFDM) 的性能为25-30%.
  • 在数据集中,与传统的LDPC-OFDM系统相比,表现出38-42%的优异性能.
  • 实现了26.6%的峰值到平均功率比率 (PAPR) 降低和低推理延迟 (3.9毫秒).

结论:

  • 拟议的Deep Autoencoder-LDPC-OFDM架构提供了高性能,高功率效率和可扩展性.
  • 它提供了卓越的可靠性和低延迟通信,适合6G及以上.
  • 该系统在现实的无线环境中表现出稳健性,包括时间变化和多路径色通道.