Bài giảng Truyền thông số - Digital Communication

TRUYỀN THÔNG SỐDIGITAL COMMUNICATION 30 tiết, 2 tín chỉ * Mục tiêu: Học phần này, tiếp theo học phần Các hệ thống truyền thông, có mục tiêu cung cấp cơ sở cho các học phần Truyền thông không dây, Truyền thông di động. Đánh giá kết quả học tập: Bài tập ở nhà nộp cho giảng viên: 30% tổng điểm Thi hết môn học: 70% Tài liệu tham khảo: Bài giảng (Dựa trên bài giảng của Uppsala University và cuốn 1) 1. Digital Communications Fundamentals and Applications, Bernard Sklar 2. “Digital Communications”, Jonh G. Proakis, McGraw-Hill, 2001 Internet * What is Digital Communication? Digital Communication is any message passed through digital devices Digital Communication can be easy and quick to use E.g. of digital communication are: E-mailing - Computers Texting - Cell Phones * Receiver Transmitter A basic digital communication system Source Encoder Source Decoder Channel Encoder Channel Decoder Demodulator Data Sink Data Source Modulator channel * Bit Error Rate (BER) / Symbol Error Rate (SER) Nội dung môn họcContents of the course Source coding / Mã hóa nguồn Channel coding / Mã hóa kênh Modulation (Baseband and passband signaling) / Điều biến * Week 1: Need to know before studying a DCS Classification of signals Random process Autocorrelation Power and energy spectral densities Noise in communication systems Signal transmission through linear systems Bandwidth of signal * * Key blocks: Formatting: transforms the source information into bits. Modulation: the process by which message symbols or channel symbols are converted to waveforms that are compatible with the requirements imposed by the transmission channel. Pulse modulation: transform each symbol from a binary representation to a baseband waveform. * Key blocks: Bandpass modulation: basepand waveform gi(t) is frequency translated by a carrier wave to a frequency that is much larger than gi(t), called bandpass waveform si(t), i = 1, … , M. (M-ary pulse waveform types). Source coding: converts A/D (for analog souces) and removes redundant information  formatting transformation (for digizing). Channel coding, for a given data rate, can deduce the probability of error, PE, at the expense of transmission BW or decoder complexity. * Classification of signals Deterministic and random signals Deterministic signals: whose values are completely specified for any given time. Thus, a deterministic signal can be modeled by a known function of time. E.g. x(t) = 5.cos10t Random signals: also called non deterministic signals are those signals that take random values at any given time and must be characterized statistically . E.g. Noise in electronic circuits * * Deterministic signals Random signal Classification of signals … Periodic and non-periodic signals Analog and discrete signals * Classification of signals .. Energy and power signals: as ways to measure a signal. A signal is an energy signal (tín hiệu năng lượng) if, and only if, it has nonzero but finite energy for all time: A signal is a power signal (tín hiệu công suất) if, and only if, it has finite but nonzero power for all time: General rule: Periodic and random signals are power signals. Signals that are both deterministic and non-periodic are energy signals. * - - * The energy of this signal is the shaded region A simple, common signal with infinite energy Bài tập Phân loại tín hiệu: energy signals hay power signals * Autocorrelation Correlation = a matching process. Autocorrelation = matching of a signal with a delayed version of itself Autocorrelation function of a real-valued energy signal x(t): The autocorrelation function provides a measure of how closely the signal matches a copy of itself as the copy is shifted  units in time. Autocorrelation of a power signal For a periodic signal: Autocorrelation of a random signal For a WSS process: * Remind: Cross-correlation The cross-correlation function describes the general dependency of x(t) with another random process y(t+), delayed by a time delay,  * Random process The collection of random signals is called a random process. Each signal in this collection is referred to a realization or sample function of the process. Sample functions or realizations (deterministic function) Random variables * Random process … Wide sense stationary (WSS): If the mean and autocorrelation function do not change with a shift in the origin time. Ergodic process: A random process is ergodic in mean and autocorrelation, if and * Spectral density Energy signals: Energy spectral density (ESD): Power signals: Power spectral density (PSD): Random process: Power spectral density (PSD): * Fig. Autocorrelation and Power Spectral Density * Bài tập Tìm Ex hay Px tương ứng * Noise in communication systems Thermal noise is described by a zero-mean Gaussian random process, n(t). Its PSD is flat, hence, it is called white noise. Probability density function Power spectral density of white noise Autocorrelation function of white noise * Thermal noise is present in all comm. systems  The thermal noise characteristics – additive white Gaussian noise AWGN – are most often used to model the noise in comm. systems. Input - output relationships : Relation between spectral density of output and spectral density of input : |H(f)|2 is a transfer function, frequency response function Signal transmission through linear systems * Ideal filters: Realizable filters: RC filters Butterworth filter * * Bandwidth Recall: In Fourier Analysis, signals cannot be limited both time and frequency(band) For real-world (time-limited) signals this means BW can be difficult to define Baseband Bandpass * Baseband and Bandpass X(f) fm Xc(f) 2fm Double sided bandwidth fc Bandwidth of signal Baseband vs. bandpass: * Bandwidth of signal … Different definition of bandwidth: * Half-power BW: Gx(f) drop to half-power or -3 dB Noise equivalent BW: Wn = Px/Gx(fc); Px is total signal power over all freq. Null-to-null BW: the most popular measure of BW for digital communications is the width of the main spectral lobe, where most of the power is contained. Fractional power containment BW: the band leaves 0.5% of the signal power above the upper band limit and 0.5% of the signal power below the lower band limit. Bounded power spectral density: the band leaves a certain stated level. Typical attenuation levels = 35 or 50 dB. Absolute bandwidth = infinite. * Conclusion Important features of digital communication systems Some basic concepts and definitions as signal classification, spectral density, random process, linear systems and signal bandwidth. *