Explore MATLAB source code curated for "均衡器" with clean implementations, documentation, and examples.
Describes a visible light communication system utilizing LED technology with receiver-side equalization for improved data rates. Provides spatial distribution maps for bit error rate and signal-to-noise ratio, demonstrating system performance optimization through digital signal processing techniques.
Equalizer parameter settings involve configuring different parameters to generate corresponding equalization waveforms. This process includes frequency band adjustment, gain control, and Q-factor/bandwidth specification for precise audio signal processing.
A comprehensive audio processing interface developed using GUI components, featuring reverb effects and configurable equalizer settings, with modular function implementations included for extensibility.
Custom-developed MATLAB simulation program implementing adaptive filters and equalizers using algorithms including Steepest Descent Method and MMSE (Minimum Mean Square Error) with comprehensive performance analysis capabilities.
An 8-band equalizer with filter adjustment capabilities, featuring real-time spectrum visualization and customizable parameter controls suitable for digital audio processing applications.
In channel equalization applications, the original signal distorted by the channel is used as the input to an adaptive filter, with the desired signal being a time-delayed version of the original signal, as shown in Figure 22(a). Typically, the time-delayed version of the input signal is available at the receiver end in the form of a standard training signal. When the Mean Square Error (MSE) is minimized, it indicates that the adaptive filter has successfully represented the inverse model of the channel (equalizer). Implementation typically involves configuring the adaptive filter with appropriate tap weights and using algorithms like LMS or RLS for iterative optimization.
1. Original signal A is transmitted through a noise-contaminated channel to obtain signal q, which is then processed through ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) equalizers to generate signal U with distribution plotting capabilities. 2. Implementation of LMS (Least Mean Squares), SATO (Sato's algorithm), and CMA (Constant Modulus Algorithm) to derive signal U, incorporating optimal delay calculation and mean square error computation. All results are visualized through graphical representations with MATLAB implementation examples.
Adaptive MMSE Equalizer with Algorithm Explanation and System Implementation
MATLAB code implementation and technical explanation of the Constant Modulus Algorithm (CMA) for adaptive blind equalization in communication systems
Adaptive Filters (Bit Error Rate Analysis with Equalizer Implementations)