Introduction to Communication Systems
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Showcasing the essential principles behind modern communication systems, this accessible undergraduate textbook provides a solid introduction to the foundations of communication theory. Carefully selected topics introduce students to the most important and fundamental concepts, giving students a focused, in-depth understanding of core material, and preparing them for more advanced study. Abstract concepts are introduced to students 'just in time' and reinforced by nearly 200 end-of-chapter exercises, alongside numerous MATLAB code fragments, software problems and practical lab exercises, firmly linking the underlying theory to real-world problems, and providing additional hands-on experience.
Finally, an accessible lecture-style organisation makes it easy for students to navigate to key passages, and quickly identify the most relevant material. Containing material 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 introductory textbook for undergraduate students studying electrical and computer engineering.
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- Cambridge University Press
- 9781316053768
- 9781107022775
- ePub
- 1
- Upamanyu Madhow
- English
- 2014-11-24
- 10
- 5
- 5
Kaflar
- Cover
- Half-title page
- Title page
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgements
- 1. Introduction
- Chapter plan
- 1.1 Analog or digital?
- 1.1.1 Analog communication
- 1.1.2 Digital communication
- 1.1.3 Why digital?
- 1.1.4 Why analog design remains important
- 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
- 2. Signals and systems
- Chapter plan
- 2.1 Complex numbers
- 2.2 Signals
- 2.3 Linear time-invariant systems
- 2.3.1 Discrete-time convolution
- 2.3.2 Multi-rate systems
- 2.4 Fourier series
- 2.4.1 Fourier-series properties and applications
- 2.5 The Fourier transform
- 2.5.1 Fourier-transform properties
- 2.5.2 Numerical computation using DFT
- 2.6 Energy spectral density and bandwidth
- 2.7 Baseband and passband signals
- 2.8 The structure of a passband signal
- 2.8.1 Time-domain relationships
- 2.8.2 Frequency-domain relationships
- 2.8.3 The complex-baseband equivalent of passband filtering
- 2.8.4 General comments on complex baseband
- 2.9 Wireless-channel modeling in complex baseband
- 2.10 Concept summary
- 2.11 Notes
- 2.12 Problems
- Software Lab 2.1: signals and systems computations using MATLAB
- Software Lab 2.2: modeling carrier-phase uncertainty
- Software Lab 2.3: modeling a lamppost-based broadband network
- 3. Analog communication techniques
- Chapter plan
- 3.1 Terminology and notation
- 3.2 Amplitude modulation
- 3.2.1 Double-sideband (DSB) suppressed carrier (SC)
- 3.2.2 Conventional AM
- 3.2.3 Single-sideband modulation (SSB)
- 3.2.4 Vestigial-sideband (VSB) modulation
- 3.2.5 Quadrature amplitude modulation
- 3.2.6 Concept synthesis for AM
- 3.3 Angle modulation
- 3.3.1 Limiter–discriminator demodulation
- 3.3.2 FM spectrum
- 3.3.3 Concept synthesis for FM
- 3.4 The superheterodyne receiver
- 3.5 The phase-locked loop
- 3.5.1 PLL applications
- 3.5.2 A mathematical model for the PLL
- 3.5.3 PLL analysis
- 3.6 Some analog communication systems
- 3.6.1 FM radio
- 3.6.2 Analog broadcast TV
- 3.7 Concept summary
- 3.8 Notes
- 3.9 Problems
- Software Lab 3.1: amplitude modulation and envelope detection
- Software Lab 3.2: frequency-modulation basics
- 4. Digital modulation
- Chapter plan
- 4.1 Signal constellations
- 4.2 Bandwidth occupancy
- 4.2.1 Power spectral density
- 4.2.2 The PSD of a linearly modulated signal
- 4.3 Design for bandlimited channels
- 4.3.1 Nyquist’s sampling theorem and the sinc pulse
- 4.3.2 The Nyquist criterion for ISI avoidance
- 4.3.3 Bandwidth efficiency
- 4.3.4 Power–bandwidth tradeoffs: a sneak preview
- 4.3.5 The Nyquist criterion at the link level
- 4.3.6 Linear modulation as a building block
- 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 4.1: linear modulation over a noiseless ideal channel
- Appendix 4.A Power spectral density of a linearly modulated signal
- Appendix 4.B Simulation resource: bandlimited pulses and upsampling
- 5. Probability and random processes
- Chapter plan
- 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
- 5.5.1 Expectation for random vectors
- 5.6 Gaussian random variables
- 5.6.1 Joint Gaussianity
- 5.7 Random processes
- 5.7.1 Running example: a sinusoid with random amplitude and phase
- 5.7.2 Basic definitions
- 5.7.3 Second-order statistics
- 5.7.4 Wide-sense stationarity and stationarity
- 5.7.5 Power spectral density
- 5.7.6 Gaussian random processes
- 5.8 Noise modeling
- 5.9 Linear operations on random processes
- 5.9.1 Filtering
- 5.9.2 Correlation
- 5.10 Concept summary
- 5.11 Notes
- 5.12 Problems
- Appendix 5.A Q function bounds and asymptotics
- Appendix 5.B Approximations using limit theorems
- Appendix 5.C Noise mechanisms
- Appendix 5.D The structure of passband random processes
- Appendix 5.D.1 Baseband representation of passband white noise
- Appendix 5.E SNR computations for analog modulation
- Appendix 5.E.1 Noise model and SNR benchmark
- Appendix 5.E.2 SNR for amplitude modulation
- Appendix 5.E.3 SNR for angle modulation
- 6. Optimal demodulation
- Chapter plan
- 6.1 Hypothesis testing
- 6.1.1 Error probabilities
- 6.1.2 ML and MAP decision rules
- 6.1.3 Soft decisions
- 6.2 Signal-space concepts
- 6.2.1 Representing signals as vectors
- 6.2.2 Modeling WGN in signal space
- 6.2.3 Hypothesis testing in signal space
- 6.2.4 Optimal reception in AWGN
- 6.2.5 Geometry of the ML decision rule
- 6.3 Performance analysis of ML reception
- 6.3.1 The geometry of errors
- 6.3.2 Performance with binary signaling
- 6.3.3 M-ary signaling: scale-invariance and SNR
- 6.3.4 Performance analysis for M-ary signaling
- 6.3.5 Performance analysis for M-ary orthogonal modulation
- 6.4 Bit error probability
- 6.5 Link-budget analysis
- 6.6 Concept summary
- 6.7 Notes
- 6.8 Problems
- Software Lab 6.1: linear modulation with two-dimensional constellations
- Software Lab 6.2: modeling and performance evaluation on a wireless fading channel
- Appendix 6.A The irrelevance of the component orthogonal to the signal space
- 7. Channel coding
- Chapter plan
- 7.1 Motivation
- 7.2 Model for channel coding
- 7.3 Shannon’s promise
- 7.3.1 Design implications of Shannon limits
- 7.4 Introducing linear codes
- 7.5 Soft decisions and belief propagation
- 7.6 Concept summary
- 7.7 Notes
- 7.8 Problems
- Software Lab 7.1: belief propagation
- 8. Dispersive channels and MIMO
- Chapter plan
- 8.1 The single-carrier system model
- 8.1.1 The signal model
- 8.1.2 The noise model and SNR
- 8.2 Linear equalization
- 8.2.1 Adaptive MMSE equalization
- 8.2.2 Geometric interpretation and analytical computations
- 8.3 Orthogonal frequency-division multiplexing
- 8.3.1 DSP-centric implementation
- 8.4 MIMO
- 8.4.1 The linear array
- 8.4.2 Beamsteering
- 8.4.3 Rich scattering and MIMO-OFDM
- 8.4.4 Diversity
- 8.4.5 Spatial multiplexing
- 8.5 Concept summary
- 8.6 Notes
- 8.7 Problems
- Software Lab 8.1: introduction to equalization in single-carrier systems
- Software Lab 8.2: simplified simulation model for an OFDM link
- Software Lab 8.3: MIMO signal processing
- Epilogue
- References
- Index