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Aliasing01:18

Aliasing

117
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...
117
Upsampling01:22

Upsampling

195
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...
195
Bandpass Sampling01:17

Bandpass Sampling

159
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....
159
Active Filters01:25

Active Filters

710
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
710

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Related Experiment Video

Updated: May 31, 2025

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Adaptive Filtering for Channel Estimation in RIS-Assisted mmWave Systems.

Shuying Shao1, Tiejun Lv1, Pingmu Huang2

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary

New adaptive algorithms improve channel estimation for millimeter-wave (mmWave) massive MIMO systems with reconfigurable intelligent surfaces (RIS). These methods enhance accuracy and speed, overcoming RIS signal processing limitations for better wireless communication.

Keywords:
adaptive filteringchannelestimation (CE)reconfigurable intelligent surfaces (RISs)sparse mmWave systems

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Information Theory

Background:

  • Millimeter-wave (mmWave) massive MIMO systems integrated with reconfigurable intelligent surfaces (RIS) promise enhanced wireless communication.
  • Channel estimation (CE) in these systems faces challenges due to RIS signal processing limitations.
  • Existing CE methods struggle to meet the demands of advanced wireless networks.

Purpose of the Study:

  • To propose an adaptive channel estimation framework for mmWave massive MIMO-RIS systems.
  • To develop novel algorithms that improve CE accuracy and convergence speed.
  • To reduce the computational complexity of channel estimation in these advanced systems.

Main Methods:

  • Developed two adaptive algorithms: Log-Sum Normalized Least Mean Squares (Log-Sum NLMS) and Hybrid Normalized Least Mean Squares-Normalized Least Mean Fourth (Hybrid NLMS-NLMF).
  • Leveraged the sparse nature of mmWave channels for improved estimation.
  • Incorporated a log-sum penalty in Log-Sum NLMS for faster convergence and a mixed error function in Hybrid NLMS-NLMF for varied SNR conditions.

Main Results:

  • The proposed Log-Sum NLMS and Hybrid NLMS-NLMF algorithms demonstrated significant improvements in channel estimation accuracy.
  • Both algorithms exhibited faster convergence speeds compared to existing methods like NLMS, SEFWLMS, and SHAFA.
  • The new algorithms showed lower computational complexity.

Conclusions:

  • The proposed adaptive channel estimation framework effectively addresses CE challenges in mmWave massive MIMO-RIS systems.
  • Log-Sum NLMS and Hybrid NLMS-NLMF offer superior performance in accuracy and convergence speed.
  • These algorithms represent a significant advancement for future wireless communication technologies.