Question

Simulation of an Adaptive Equalizer The objective of this problem is to implement an adaptive equalizer based on the LMS algorithm. The channel is modeled as an FIR filter with symbol-spaced values that are given as follows: X = [0.05, 0.063, 0.088, 0.126, -0.25, 0.9047, 0.25, 0.126, 0.038, 0.088]. The MSE equalizer is also an FIR filter with symbol-spaced tap coefficients. Training symbols are transmitted initially to train the equalizer. In the data mode, the equalizer employs the output of the detector in forming the error signal used in the LMS algorithm. The block diagram of the system is shown in Figure CP-10.9. Write a program that performs the simulation of the system in Figure CP-10.9. Use 1000 training (binary) symbols and 10,000 binary data symbols for the FIR channel model previously given. Use Ïƒ^2 = 0.01, 0.1, and 0.2 for the variance of the additive zero-mean Gaussian noise sequence. Compare the measured error rate with that of an ideal channel with no ISI. Input: Detector Iari Output: Training data

          Simulation of an Adaptive Equalizer

The objective of this problem is to implement an adaptive equalizer based on the LMS algorithm. The channel is modeled as an FIR filter with symbol-spaced values that are given as follows: X = [0.05, 0.063, 0.088, 0.126, -0.25, 0.9047, 0.25, 0.126, 0.038, 0.088]. The MSE equalizer is also an FIR filter with symbol-spaced tap coefficients. Training symbols are transmitted initially to train the equalizer. In the data mode, the equalizer employs the output of the detector in forming the error signal used in the LMS algorithm. The block diagram of the system is shown in Figure CP-10.9.

Write a program that performs the simulation of the system in Figure CP-10.9. Use 1000 training (binary) symbols and 10,000 binary data symbols for the FIR channel model previously given. Use Ïƒ^2 = 0.01, 0.1, and 0.2 for the variance of the additive zero-mean Gaussian noise sequence. Compare the measured error rate with that of an ideal channel with no ISI.

Input:
Detector
Iari
Output:
Training data

simulation of an adaptive equalizer the objective of this problem is to implement an adaptive equalizer based on the lms algorithm the channel is modeled as an fir filter with symbol spaced 33338

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