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Implementation of Channel equalizer for OFDM System using Artificial Neural Network

This project received 12 bids from talented freelancers with an average bid price of $670 USD.

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Project Budget
$30 - $250 USD
Total Bids
12
Project Description

NEED A metlab code to complete the task given below
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The main aim of the project is to develop novel artificial neural network equalizer to mitigate the linear and nonlinear distortion like ISI, CCI and burst noise interferences occurs in the communication channel and can provide minimum mean square error and bit-error-rate plot for wide variety of channel condition. All the simulation will be conducted for BPSK,QPSK signals. As a Equalizer is connected in BPSK Network after the channel. Now the output of channel will be input of the equalizer & the output of equalizer will be as per the requirement of output of [url removed, login to view] performance of the equalizer proposed for other forms of modulation like QPSK, MARY-PSK, QAPSK and other modulation forms has been considered.
These equalization techniques for use in recent applications like 2G, 3G communication techniques will be helpful in understanding the problem. Optimal equalizer based on maximum a-posterior probability (MAP) criterion can be implemented using Radial basis function (RBF) network. RBF equalizer mitigation all the ISI, CCI and BN interference and provide minimum BER plot. But it has one draw back that if input is increased the number of centres of the network increases and makes the network more complicated.

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