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Description
In this project we implement adaptive linear and decision-feedback equalizers through MATLAB.
Simulation setting and parameters • Every component is simulated in the baseband at the symbol sampling rate (one sample per symbol); • Use the two channels that are symbol-spaced baseband channels, Channel A and Channel B, on page 551 of the textbook; • You need to generate 5000 random and complex QPSK symbols for each case in your simulation; • Pass the symbols through the two channels, respectively. The complex symbols or channels are for passband processing. Their baseband equivalent processing was covered in ECE626 – Communication Systems, with lecture notes still available on Blackboard, see the end of Lecture Notes #1; • Add pseudo-random white Gaussian noise at the channel output, perform your simulation for per bit SNR of 5 dB and 20 dB; You need to compute the relative scaling between the signal and the noise that these SNRs correspond to; • Carrier synchronization and symbol synchronization are assumed perfect; • Implement symbol-spaced adaptive LMS equalizers: (a) Linear ZF; (b) Linear MMSE; (c) MMSE-DFE; The equalizers should be FIR, with 32 taps (for both precursor and post-cursor equalizers in the DFE case); • Use 1000 initial training symbols, then switch to the decision-directed mode; Choose your step size to compromise between fast convergence speed vs. low residual MSE for each case separately; Your best step size will be different for different cases, which can be achieved by experimenting each case. Requirements in your simulation and report: 1. If not specified, you need to repeat for all channels, all SNRs, and all equalizers; 2. Make the following plots, use your discretion on how many curves to group into each plot, for best presentation of relevant results: © Signal constellation without equalization, and signal constellation with equalization (excluding the training symbols) for SNR of 20 dB; © Learning curves (parameter values vs. time of all 5000 samples) of the first a few (at least 3) equalizer coefficients; © BER vs. SNR (excluding the training symbols); 3. Write your report, give details of all steps including descriptions, block diagrams, equations, computation, adaptive algorithm description, choices of the step size, etc.; Provide observations, discussions, and conclusions.
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Bangalore,
IN
location |
US$150
bid amount |

         
(2
reviews)
feedback |

7 day(s)
delivery time |

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Hi
I will be able to finish this project for you. please check PM.
Regards
Dhyan
Bid Time: 06-01-2008 02:07
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beijing,
CN
location |
US$150
bid amount |

         
(2
reviews)
feedback |

7 day(s)
delivery time |

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hi, I am interested in the project. do you have relate document about project?
Bid Time: 06-01-2008 19:28
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Novosibirsk,
RU
location |
US$2,000
bid amount |

         
(2
reviews)
feedback |

45 day(s)
delivery time |

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I am physicist and matlab programmer. See link to my experience in Private Message.
Bid Time: 06-01-2008 22:43
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Islamabad,
PK
location |
US$99
bid amount |

(No Feedback
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Hi
Will you provide the name of book or pdf and ECE626 course notes.
"page 551 of the textbook"
"Their baseband equivalent processing was covered in ECE626 – Communication Systems, with lecture notes still available on Blackboard, see the end of Lecture Notes #1;"
If you will provide the above reference than I can do this project by/before next Monday.
Bid Time: 05-31-2008 21:42
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Guangzhou,
CN
location |
US$100
bid amount |

(No Feedback
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7 day(s)
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Hi, Please PM me and need the textbook information. Regards.
Bid Time: 06-06-2008 18:17
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