Upamanyu Madhow is Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. His research interests broadly span communications, signal processing and networking, with current emphasis on next-generation wireless and bio-inspired approaches to networking and inference. He is a recipient of the NSF CAREER Award and the IEEE Marconi prize paper award in wireless communication, author of the graduate-level textbook Fundamentals of Digital Communication (2008) and a Fellow of the IEEE.

Showcasing the principles behind modern communication systems, this accessible undergraduate textbook introduces only the most important fundamental concepts. Suitable for a one- or two-semester course, and accompanied online by a password-protected solutions manual and supporting instructor resources, this is the perfect textbook for undergraduate students studying electrical and computer engineering.

Part I. Introduction: 1.1 Analog or digital?; 1.2 A technology perspective; 1.3 The scope of this textbook; 1.4 Why study communication systems?; 1.5 Concept summary; 1.6 Notes; Part II. Signals and Systems: 2.1 Complex numbers; 2.2 Signals; 2.3 Linear time-invariant systems; 2.4 Fourier series; 2.5 The Fourier transform; 2.6 Energy spectral density and bandwidth; 2.7 Baseband and passband signals; 2.8 The structure of a passband signal; 2.9 Wireless-channel modeling in complex baseband; 2.10 Concept summary; 2.11 Notes; 2.12 Problems; Software labs; Part III. Analog Communication Techniques: 3.1 Terminology and notation; 3.2 Amplitude modulation; 3.3 Angle modulation; 3.3.1 Limiter-discriminator demodulation; 3.4 The superheterodyne receiver; 3.5 The phase-locked loop; 3.6 Some analog communication systems; 3.7 Concept summary; 3.8 Notes; 3.9 Problems; Software labs; Part IV. Digital Modulation: 4.1 Signal constellations; 4.2 Bandwidth occupancy; 4.3 Design for bandlimited channels; 4.4 Orthogonal and biorthogonal modulation; 4.5 Proofs of the Nyquist theorems; 4.6 Concept summary; 4.7 Notes; 4.8 Problems; Software lab; Appendices; Part V. Probability and Random Processes: 5.1 Probability basics; 5.2 Random variables; 5.3 Multiple random variables, or random vectors; 5.4 Functions of random variables; 5.5 Expectation; 5.6 Gaussian random variables; 5.7 Random processes; 5.8 Noise modeling; 5.9 Linear operations on random processes; 5.10 Concept summary; 5.11 Notes; 5.12 Problems; Appendices; Part VI. Optimal Demodulation: 6.1 Hypothesis testing; 6.2 Signal-space concepts; 6.3 Performance analysis of ML reception; 6.4 Bit error probability; 6.5 Link-budget analysis; 6.6 Concept summary; 6.7 Notes; 6.8 Problems; Software labs; Part VII. Channel Coding: 7.1 Motivation; 7.2 Model for channel coding; 7.3 Shannon's promise; 7.4 Introducing linear codes; 7.5 Soft decisions and belief propagation; 7.6 Concept summary; 7.7 Notes; 7.8 Problems; Part VIII. Dispersive Channels and MIMO: 8.1 The single-carrier system model; 8.2 Linear equalization; 8.3 Orthogonal frequency-division multiplexing; 8.4 MIMO; 8.5 Concept summary; 8.6 Notes; 8.7 Problems; Software labs.