channel estimation based project
2016-08-23
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In our project, we first build up a wireless communication
simulator including Gray coding, modulation, different
channel models (AWGN, flat fading and frequency selective
fading channels), channel estimation, adaptive equalization,
and demodulation. Next, we test the effect of different
channel models to the data and image in receiver with
constellation and BER (bit error rate) plots under QPSK
modulation. For Image data source, we also compare the
received image quality to original image in different channels.
At last, we give detail results and analyses of the performance
improvement with channel estimation and adaptive
equalization in slow Rayleigh fading channel. For
frequency selective fading channel, we use linear equalization
with both LMS (least mean squares) and RLS (Recursive
Least Squares) algorithms to compare the different
improvements. We will see that in AWGN channel
simulator including Gray coding, modulation, different
channel models (AWGN, flat fading and frequency selective
fading channels), channel estimation, adaptive equalization,
and demodulation. Next, we test the effect of different
channel models to the data and image in receiver with
constellation and BER (bit error rate) plots under QPSK
modulation. For Image data source, we also compare the
received image quality to original image in different channels.
At last, we give detail results and analyses of the performance
improvement with channel estimation and adaptive
equalization in slow Rayleigh fading channel. For
frequency selective fading channel, we use linear equalization
with both LMS (least mean squares) and RLS (Recursive
Least Squares) algorithms to compare the different
improvements. We will see that in AWGN channel
matlab
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