Explore MATLAB source code curated for "信道估计" with clean implementations, documentation, and examples.
6. In a three-tap Rayleigh channel environment for OFDM systems: (1) Design a specific pilot-assisted channel estimation method, implement simulation using Simulink, and measure bit error rate performance with BPSK modulation. Plot the relationship curves between bit SNR and channel estimation mean square error, and between bit SNR and bit error rate.
This project implements DS-CDMA system simulation in three-path Rayleigh fading channel environment. Key components include: (1) Code-division pilot-assisted channel estimation with equal-gain combining and maximal ratio combining receivers, measuring BPSK BER performance; (2) Single-dwell sliding correlation timing synchronization method with custom S-Function implementation; (3) Frequency synchronization scheme with custom S-Function design. The simulation analyzes relationship between bit SNR and channel estimation MSE, BER performance, timing synchronization accuracy, acquisition probability, and frequency synchronization error.
(1) Design a specific code division pilot-assisted channel estimation method for DS-CDMA systems. Implement both Equal Gain Combining (EGC) and Maximum Ratio Combining (MRC) techniques at the receiver. Perform Simulink simulations to measure BPSK bit error rate performance. Plot the relationship curves between bit SNR vs. channel estimation mean square error and bit SNR vs. BER. Explain the distinct physical implications of both combining schemes when BER reaches 0.01, including implementation considerations for pilot signal generation, correlation-based channel estimation, and combining weight calculations.
Simulation code implementing compressed sensing-based channel estimation with performance comparison against conventional Least Squares (LS) and Minimum Mean Square Error (MMSE) algorithms, featuring MATLAB/Octave implementations of sparse recovery techniques and statistical error analysis
This MATLAB program implements channel estimation using the Least Mean Squares (LMS) algorithm. The signal source generates binary random codes of ±1 values, transmitted through a multipath channel with 3 distinct paths.
Channel estimation utilizing Kalman filtering principles for OFDM channel tracking and state prediction, with implementation insights on recursive estimation algorithms and system modeling.
MATLAB simulation code for OFDM channel impulse response and channel estimation - an excellent reference with implementation insights!
Beamforming channel estimation algorithm applicable for 4G communication simulations including LTE and CDMA systems. Validated for correctness with implementation-ready MATLAB/Python code structures.
This program implements MIMO-OFDM channel estimation using a 2×2 system configuration, designed with modular architecture for easy adaptation to other antenna configurations through parameter modification.
MATLAB simulation suite for wireless communication systems covering synchronization, equalization, channel estimation, and modulation/demodulation with implementation examples