ActionScript Job by rahuluppala

In this programming assignment you will try out and test different equalizers for intersymbol interference

(ISI) channels and check their performance in terms of the probability of symbol error.

Consider a discrete-time baseband-equivalent channel model with input-output (I/O) relationship given by

vn =



fkInk + n, where the symbols In are drawn from a binary PAM constellation and n denotes

additive white Gaussian noise (AWGN) with variance N0. We will test two ISI channels with corresponding

taps [0:227;0:45;0:7;0:45;0:227] (channel 1) and [0:41;0:815;0:41] (channel 2). Since for both channels ∑




2 = 1, the received signal-to-noise-ratio (SNR) is

= 1=N0. Unless specified otherwise, the linear and

decision-feedback (DF) equalizers to be tested will have 41 taps.

Test Scenario 1: Implement the zero-forcing (ZF) linear equalizer for channel 2. Plot the symbol error rate

(SER) curve versus SNR (use semilog) for the SNR values

= 0 : 2 : 18dB, by generating 108


In addition to the simulated SER, plot the theoretical SER performance of the linear ZF equalizer (use the

corresponding formulas from the book or the notes). In a different figure, compare the theoretical SER

curves for equalizer lengths 13, 23 and 33. Comment briefly on their relative performance.

Test Scenario 2: Implement the linear minimum mean-square error (LMMSE) equalizer for channels 1 and

2. Plot the two simulated SER semi-log curves as in Test Scenario 1 along with the (approximate) theoretical

SER for each channel (use formula [url removed, login to view]). Comment on the difference in performance between the two


Test Scenario 3: Implement the MMSE-DF equalizer for channel 2 and plot the simulated SER versus

SNR semi-log curve along with its (approximate) theoretical SER [Find Jmin, then calculate SNR


(1 Jmin)=(Jmin) and finally find Pe  Q(


)]. Compare in a different plot, the theoretical BER vs. SNR

curves of the linear ZF, LMMSE, and MMSE-DF for the same channel 2.

Test Scenario 4: Implement the Viterbi Algorithm (VA) for maximum likelihood sequence estimation

(MLSE). In addition to the simulated BER versus SNR curve, plot the matched-filter (MF) lower bound

for both channels 1 and 2, and comment on the MLSE-MF gap. Finally, compare on a separate graph the

performance of MMSE-DF and MLSE equalization schemes for channel 1 only.


 Your report should explain what the code does step-by-step

Beceriler: ActionScript

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İşveren Hakkında:
( 0 değerlendirme ) Boston, United States

Proje NO: #4427475

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