Related Experiment Videos
The use of neural nets to combine equalization with decoding for severe intersymbol interference channels
1Dept. of Electr. Eng., King Saud Univ., Riyadh.
IEEE Transactions on Neural Networks
|January 1, 1994
Summary
This study introduces a novel multilayer neural network to combine channel equalization and decoding, significantly improving performance in channels with severe intersymbol interference.
Area of Science:
- Signal Processing
- Machine Learning
- Communications Engineering
Background:
- Severe intersymbol interference (ISI) poses challenges in communication channels.
- Conventional methods often perform equalization and decoding separately, limiting efficiency.
Purpose of the Study:
- To propose a unified approach for combining equalization and decoding.
- To address the limitations of conventional separate processing methods.
Main Methods:
- A multilayer neural network architecture was designed.
- The neural network performs equalization and decoding simultaneously.
Main Results:
- Experimental results demonstrate substantial performance improvements.
- The proposed method outperforms conventional techniques.
Conclusions:
- Simultaneous equalization and decoding using a multilayer neural network is effective.
- This approach offers a significant advancement for channels with severe ISI.
Related Concept Videos
01:10Reconstruction of Signal using Interpolation
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
01:30Classification of Signals
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...
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...
01:26Linear Approximation in Frequency Domain
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.
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.
01:17Even and Odd Signals
An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
01:15Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
01:21Linear Approximation in Time Domain
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, the...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...