tailieunhanh - Digital Communication I: Modulation and Coding Course-Lecture 5

Given the observation vector , perform a mapping from to an estimate of the transmitted symbol, , such that the average probability of error in the decision is vector is deterministic. Elements of noise vector are Gaussian random variables with zeromean and variance . The noise vector pdf is | Digital Communications I: Modulation and Coding Course Term 3 - 2008 Catharina Logothetis Lecture 5 Lecture 5 Last time we talked about: Receiver structure Impact of AWGN and ISI on the transmitted signal Optimum filter to maximize SNR Matched filter and correlator receiver Signal space used for detection Orthogonal N-dimensional space Signal to waveform transformation and vice versa Lecture 5 Today we are going to talk about: Signal detection in AWGN channels Minimum distance detector Maximum likelihood Average probability of symbol error Union bound on error probability Upper bound on error probability based on the minimum distance Lecture 5 Detection of signal in AWGN Detection problem: Given the observation vector , perform a mapping from to an estimate of the transmitted symbol, , such that the average probability of error in the decision is minimized. Modulator Decision rule Lecture 5 Statistics of the observation Vector AWGN channel model: Signal vector is deterministic. . | Digital Communications I: Modulation and Coding Course Term 3 - 2008 Catharina Logothetis Lecture 5 Lecture 5 Last time we talked about: Receiver structure Impact of AWGN and ISI on the transmitted signal Optimum filter to maximize SNR Matched filter and correlator receiver Signal space used for detection Orthogonal N-dimensional space Signal to waveform transformation and vice versa Lecture 5 Today we are going to talk about: Signal detection in AWGN channels Minimum distance detector Maximum likelihood Average probability of symbol error Union bound on error probability Upper bound on error probability based on the minimum distance Lecture 5 Detection of signal in AWGN Detection problem: Given the observation vector , perform a mapping from to an estimate of the transmitted symbol, , such that the average probability of error in the decision is minimized. Modulator Decision rule Lecture 5 Statistics of the observation Vector AWGN channel model: Signal vector is deterministic. Elements of noise vector are Gaussian random variables with zero-mean and variance . The noise vector pdf is The elements of observed vector are independent Gaussian random variables. Its pdf is Lecture 5 Detection Optimum decision rule (maximum a posteriori probability): Applying Bayes’ rule gives: Lecture 5 Detection Partition the signal space into M decision regions, such that Lecture 5 Detection (ML rule) For equal probable symbols, the optimum decision rule (maximum posteriori probability) is simplified to: or equivalently: which is known as maximum likelihood. Lecture 5 Detection (ML) Partition the signal space into M decision regions, . Restate the maximum likelihood decision rule as follows: Lecture 5 Detection rule (ML) It can be simplified to: or equivalently: Lecture 5 Maximum likelihood detector block diagram Choose the largest Lecture 5 Schematic example of the ML decision regions Lecture 5 Average probability of symbol error Erroneous decision: For the transmitted .