误码率 Resources

Showing items tagged with "误码率"

Digital communication systems employ various modulation schemes, with 16QAM being a widely-used quadrature amplitude modulation technique that combines both amplitude and phase keying. This code simulation demonstrates 16QAM constellation diagrams, symbol error rate (SER), and bit error rate (BER), illustrating their interrelationships through MATLAB implementation. The simulation provides practical insights into QAM performance analysis, covering modulation techniques like 32QAM, 64QAM, and 56QAM, making it valuable for understanding digital communication system optimization.

MATLAB 2260 views Tagged
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Utilizing Simulink simulation tools to analyze binary symmetric channels and evaluate the improvement in bit error rate (BER) through channel coding techniques such as Turbo codes, with implementation insights into encoding/decoding algorithms.

MATLAB 363 views Tagged
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This research implements RS convolutional codes over Gaussian channels, employing both soft and hard decision decoding methods for bit error rate simulation. The system architecture is constructed using Simulink blocks with MATLAB m-file integration for automated result generation and performance analysis.

MATLAB 337 views Tagged
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This study analyzes bit error rates (BER) for 2ASK, 2FSK, 2PSK, and 2DPSK modulation techniques under both coherent and non-coherent demodulation methods in additive white Gaussian noise (AWGN) environments. BER performance is compared with theoretical values and visualized through BER versus signal-to-noise ratio (SNR) curves. Implementation involves Monte Carlo simulations using MATLAB's communication toolbox functions like berawgn for theoretical comparisons and custom modulation/demodulation blocks with error counting algorithms.

MATLAB 314 views Tagged
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The theoretical bit error rate (BER) for BPSK communication systems over additive white Gaussian noise (AWGN) channels can be calculated using either Q(sqrt(2*Eb/N0)) or 0.5*erfc(sqrt(Eb/N0)), where Eb represents bit energy and N0 denotes noise power spectral density.

MATLAB 401 views Tagged
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