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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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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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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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Sampling Theorem01:15

Sampling Theorem

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
822
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

394
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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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...
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A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems.

Zeliang Ou1, Xiaofeng Liu2, Hongwen Yang1

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Summary

Future 6G networks require low-cost Internet of Things (IoT) devices. This study introduces a receiver design reducing analog-to-digital converter (ADC) sampling rates, significantly cutting costs and energy use for 6G IoT terminals.

Keywords:
OFDM systemlow sampling ratezero-forcing precoding

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Area of Science:

  • Wireless communication systems
  • Signal processing
  • Green communication technologies

Background:

  • The proliferation of Internet of Things (IoT) devices in future 6G networks necessitates low-cost, energy-efficient terminals.
  • High sampling rates in analog-to-digital converters (ADCs) increase terminal energy consumption and hardware costs.
  • Conventional Orthogonal Frequency Division Multiplexing (OFDM) systems require receivers to sample at the Nyquist rate, even when users utilize only a fraction of the bandwidth.

Purpose of the Study:

  • To propose a low sampling rate receiver design for multi-antenna, multi-user OFDM systems.
  • To reduce the energy consumption and hardware cost of terminals in future mobile networks.
  • To maintain acceptable system performance despite reduced sampling rates.

Main Methods:

  • Implementation of a low sampling rate receiver architecture.
  • Utilizing zero-forcing precoding to enable sampling rate reduction.
  • Analysis of multi-antenna, multi-user OFDM system configurations.

Main Results:

  • The proposed design successfully reduces the receiver sampling rate to 1/K of the Nyquist rate, where K is the number of users.
  • Simulation results demonstrate insignificant and acceptable performance degradation.
  • Key performance indicators such as bit error rate, mutual information, and peak-to-average power ratio remain within acceptable limits.

Conclusions:

  • The developed low sampling rate receiver design is effective for multi-user OFDM systems.
  • This approach offers a viable solution for reducing cost and energy consumption in 6G IoT terminals.
  • The trade-off between sampling rate reduction and performance is favorable for practical implementation.