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Digital Communication I: Modulation and Coding Course-Lecture 4

Receiver structure Impact of AWGN and ISI on the transmitted signal. Optimum filter to maximize SNR. Matched filter receiver and Correlator receiver.Demodulation and sampling: Waveform recovery and preparing the receivedl signal for detection: Improving the signal power to the noise power (SNR) using matched filter Reducing ISI using equalizerl Sampling the recovered waveform. Detection: Estimate the transmitted symbol based on the received sample | Digital Communications I: Modulation and Coding Course Term 3 - 2008 Catharina Logothetis Lecture 4 Lecture 4 Last time we talked about: Receiver structure Impact of AWGN and ISI on the transmitted signal Optimum filter to maximize SNR Matched filter receiver and Correlator receiver Lecture 4 Receiver job Demodulation and sampling: Waveform recovery and preparing the received signal for detection: Improving the signal power to the noise power (SNR) using matched filter Reducing ISI using equalizer Sampling the recovered waveform Detection: Estimate the transmitted symbol based on the received sample Lecture 4 Receiver structure Frequency down-conversion Receiving filter Equalizing filter Threshold comparison For bandpass signals Compensation for channel induced ISI Baseband pulse (possibly distored) Sample (test statistic) Baseband pulse Received waveform Step 1 – waveform to sample transformation Step 2 – decision making Demodulate & Sample Detect Lecture 4 Implementation of . | Digital Communications I: Modulation and Coding Course Term 3 - 2008 Catharina Logothetis Lecture 4 Lecture 4 Last time we talked about: Receiver structure Impact of AWGN and ISI on the transmitted signal Optimum filter to maximize SNR Matched filter receiver and Correlator receiver Lecture 4 Receiver job Demodulation and sampling: Waveform recovery and preparing the received signal for detection: Improving the signal power to the noise power (SNR) using matched filter Reducing ISI using equalizer Sampling the recovered waveform Detection: Estimate the transmitted symbol based on the received sample Lecture 4 Receiver structure Frequency down-conversion Receiving filter Equalizing filter Threshold comparison For bandpass signals Compensation for channel induced ISI Baseband pulse (possibly distored) Sample (test statistic) Baseband pulse Received waveform Step 1 – waveform to sample transformation Step 2 – decision making Demodulate & Sample Detect Lecture 4 Implementation of matched filter receiver Bank of M matched filters Matched filter output: Observation vector Lecture 4 Implementation of correlator receiver Bank of M correlators Correlators output: Observation vector Lecture 4 Today, we are going to talk about: Detection: Estimate the transmitted symbol based on the received sample Signal space used for detection Orthogonal N-dimensional space Signal to waveform transformation and vice versa Lecture 4 Signal space What is a signal space? Vector representations of signals in an N-dimensional orthogonal space Why do we need a signal space? It is a means to convert signals to vectors and vice versa. It is a means to calculate signals energy and Euclidean distances between signals. Why are we interested in Euclidean distances between signals? For detection purposes: The received signal is transformed to a received vectors. The signal which has the minimum distance to the received signal is estimated as the transmitted signal. Lecture 4 Schematic example of a .