Description: This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
- [QPSK_error] - MATLAB based on the QPSK BER, symbol err
- [eqbereval] - Of linear balanced, decision feedback eq
- [MonteCarlo] - Monte carlo method and its application,
- [eqber_adaptive] - This script runs a simulation loop for e
- [matlab_book] - Foreign classic books MULTIVARIABLE FEED
- [lms] - LMS
- [ofdm] -
- [LMS_Equalizer] - Equalizer for reduce ISI use LMS algorit
- [20090918] - In real-time platform, the Gaussian mix
- [SC0814] - MATLAB-based simulation of single-carrie
File list (Check if you may need any files):
zishiyingjunheng
................\eqberdemo.html
................\eqberdemo.m
................\eqberdemo_05.png
................\eqberdemo_06.png
................\eqberdemo_07.png
................\eqberdemo_08.png
................\eqberdemo_09.png
................\eqberdemo_10.png
................\eqberdemo_11.png
................\eqberdemo_12.png
................\eqberdemo_13.png
................\eqberREADME.txt
................\eqber_adaptive.html
................\eqber_adaptive.m
................\eqber_graphics.m
................\eqber_mlse.html
................\eqber_mlse.m
................\eqber_siggen.html
................\eqber_siggen.m
................\figposition.m
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