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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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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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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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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Related Experiment Video

Updated: Sep 16, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor.

Azam Isam Aladwani1, Tarik Adnan Almohamad1, Abdullah Talha Sözer1

  • 1Electrical and Electronics Engineering Department, Faculty of Engineering, Karabuk University, Karabuk 78050, Türkiye.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

A new tree-based regression model offers efficient hybrid channel estimation for wireless sensor networks (WSNs) using generalized frequency division multiplexing (GFDM). This model prioritizes speed and low computational cost for real-time applications over marginal accuracy gains.

Keywords:
channel estimationgeneralized frequency division multiplexing (GFDM)hybrid channelradio frequency (RF)tree-based machine learningvisible light communication (VLC)wireless sensor networks (WSNs)

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Area of Science:

  • Wireless Communication Systems
  • Signal Processing
  • Machine Learning Applications

Background:

  • Hybrid channel estimation in wireless sensor networks (WSNs) using generalized frequency division multiplexing (GFDM) over visible light communication (VLC) and radio frequency (RF) links is crucial.
  • Realistic hybrid channels involve additive white Gaussian noise (AWGN) and Rayleigh fading, posing challenges for traditional estimators like MMSE and LMMSE due to their rigidity in nonlinear conditions.
  • Existing methods struggle with the heterogeneous and nonlinear nature of combined VLC/RF channels, necessitating novel approaches for accurate and efficient estimation.

Purpose of the Study:

  • To propose a novel tree-based regression model for hybrid channel estimation in GFDM-enabled WSNs.
  • To address the limitations of traditional estimators in complex, realistic channel environments.
  • To develop a data-driven solution that balances accuracy with computational efficiency for resource-constrained WSNs.

Main Methods:

  • A decision tree regressor was developed and trained using a dataset of 18,000 signal samples across 36 signal-to-noise ratio (SNR) levels.
  • The model was evaluated against Support Vector Machine (SVM) and Random Forest algorithms for hybrid channel estimation.
  • Performance metrics included accuracy, Bit Error Rate (BER), and inference time on a test dataset.

Main Results:

  • The proposed tree model achieved competitive accuracy (90.83% at 10 dB, 97.63% at 30 dB) and low BER (0.0917 at 10 dB, 0.0237 at 30 dB).
  • Inference efficiency was a key advantage, with the tree model completing predictions in 45.53 seconds, significantly faster than Random Forest (140.09s) and SVM (189.35s).
  • A trade-off was observed: the tree model offers substantial computational savings at the cost of slightly lower predictive performance compared to ensemble methods.

Conclusions:

  • The tree-based regression model provides an efficient solution for hybrid channel estimation in WSNs, particularly for real-time and low-power applications.
  • Its rapid inference time makes it suitable for latency-sensitive wireless systems where computational overhead is a critical concern.
  • The model represents a practical approach for scenarios prioritizing speed and resource efficiency over marginal improvements in estimation accuracy.