EE 4022 - Principles of Communications

3 lecture hours 2 lab hours 4 credits

Course Description
In the study of communication systems, students will investigate how they operate and what affects their performance. The course relies heavily on system and signal analysis, both in the time and frequency domains, and on the statistical representation of random signals and noise. Amplitude and angle modulation systems are analyzed, including systems that transfer analog data and systems transferring digital data. Performance comparisons of commonly used digital modulation methods are presented. Signal-processing techniques that are commonly used in systems that transfer digital data are presented. Bit-error rate performance for baseband signal detection in the presence of noise is analyzed. Laboratory experiments reinforce the concepts from the lecture, with an emphasis on communication system functional modules. (prereq: MA 262 or MA 3620, EE 3032)

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Develop the representations of analog AM, FM, and PM communication signals both in the time and frequency domains
Explain the representations of digitally modulated ASK, FSK, and PSK communication signals both in the time and frequency domains
Analyze communication systems and subsystems (both analog and digital) using both time and frequency domain techniques
Explain advantages and disadvantages of various modulation systems under differing circumstances
Determine the performance of digitally modulated amplitude and angle modulation systems with a specified signal-to-noise ratio
Determine required bandwidths and signal-to-noise ratios needed to achieve specified bit-error rates for various digital modulation methods in the presence of noise, at specified bit rates
Design an optimal correlation receiver for baseband and bandpass, binary and M-ary, digital communication systems operating in the presence of noise

Prerequisites by Topic

Calculate the Fourier series coefficients (in trigonometric and exponential forms) for a continuous-time periodic signal
Reconstruct periodic signals from Fourier series coefficients. (This may be done with the aid of a digital computer)
Determine the result of signal and system interaction by convolution
Obtain the Fourier transform of a finite-energy signal, and the inverse-Fourier transform of a spectrum
Properly sample a continuous time signal to create a discrete time signal
Determine the probability that a random variable having a specified density function exceeds a stated threshold
Determine the mean-square value of a random variable having a specified density function

Course Topics

Signal representations (2 classes)
System representations (2 classes)
Analog amplitude modulated (AM) signals and systems (6 classes)
Analog frequency and phase modulated (FM and PM) signals and systems (4 classes)
Digitally modulated amplitude-, phase- and frequency-shift key signals and systems (3 classes)
Random variables, processes, noise, performances with noise, optimal filters (6 classes)
Pulse code modulation and error-correction coding (2 classes)
Problem sessions, reviews, and tests (6 classes)

Laboratory Topics

Spectrum measurements
Multiplication of signals and frequency conversion
Amplitude modulation
Frequency modulation
Sampling, quantization, and PCM
Digital modulation: ASK and FSK
Digital modulation: BPSK and QPSK
Baseband digital channel bit-error rate
Direct sequence spread spectrum and code division multiple access (CDMA)

Coordinator
Dr. Cory Prust

Announcement

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Milwaukee School of Engineering

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EE 4022 - Principles of Communications