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Related Concept Videos

Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Design Example: Vintage Mixing Console01:17

Design Example: Vintage Mixing Console

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A sound engineer at a music company recently encountered a problem. The output from their newly acquired studio's vintage mixing console was too low for the requirements of modern recording equipment. To rectify this situation, the engineer decided to design an audio pre-amplifier using an operational amplifier (op-amp) to boost the signal level.
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Cascaded Op Amps01:16

Cascaded Op Amps

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Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
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Parallel Resonance01:23

Parallel Resonance

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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Neural Network-Assisted DPD of Wideband PA Nonlinearity for Sub-Nyquist Sampling Systems.

Mengqiu Liu1, Xining Yang2, Jian Gao2

  • 1Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China.

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|February 26, 2025
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Summary

This study introduces a novel neural network-assisted digital predistortion method for power amplifiers in sub-Nyquist sampling systems. The technique effectively addresses undersampled signals, improving performance metrics like power spectrum and error vector magnitude.

Keywords:
attention mechanism modeldigital predistortion (DPD)memory polynomialundersampling

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Communications Engineering

Background:

  • Conventional digital predistortion (DPD) requires high sampling rates, posing challenges for sub-Nyquist sampling systems.
  • Undersampled signals in these systems introduce ambiguity, complicating DPD design.

Purpose of the Study:

  • To propose a neural network (NN)-assisted wideband power amplifier (PA) DPD method for sub-Nyquist sampling systems.
  • To address the challenges of designing DPD for undersampled signals and improve PA performance.

Main Methods:

  • A dual-stage architecture is employed for handling subsampled signals.
  • The first stage uses time-delayed polynomial reconstruction for coarse nonlinearity estimation.
  • The second stage utilizes an NN for virtual DPD training, leveraging pre-estimated models and input data.

Main Results:

  • The proposed method effectively tackles wideband PA nonlinearity in sub-Nyquist systems.
  • It significantly reduces the required training sequence length.
  • Outperforms conventional methods in power spectrum, error vector magnitude, and bit error rate.

Conclusions:

  • The NN-assisted DPD method is efficient and effective for sub-Nyquist sampling systems.
  • This approach offers a viable solution for improving PA performance under undersampling conditions.