Digital Communication Systems

At the end of this course, the students should be able to: 1. explain the concept of random processes and their parameters; 2. discuss the Hilbert transform and Markov processes and their application in digital systems; and 3. discuss the different types of digital modulation techniques and their characteristic features, including spread spectrum schemes;

Review of probability: basic concepts. Conditional and total probability. Distribution and density functions. Random variables: single and multiple variables. Mean variance and moments. Basic concepts, definition, and classification of random processes. Stationary process and independence property. Autocorrelation and correlation functions. Ergodicity. Power density spectrum. Linear systems. Hilbert Transforms. Noise modelling. Linear system response to random signal. Narrowband, bandlimited and bandpass processes. Optimal linear systems: matched filter for white noise and coloured noise, Wiener filters, minimum mean- squared error. Optimisation by parameter selection. Poisson points and renewals. Markov processes. Applications of random signal theory in communications. Digital modulation techniques: ASK, FSK, PSK, DPSK, M-ary modulation, continuous phase FSK, MSK, QAM, DSL Schemes. Line coding, intersymbol interference (ISI), Nyquist wave shaping, eye pattern, adaptive equalisation. Transmission over bandpass channel. Spread spectrum communications: pseudo noise sequences, direct sequence spread spectrum, frequency hopping spread spectrum, CDMA, application examples. 500 Level