DSP in Wireless Communications

Wireless networks have expanded beyond person-to-person communications,

connecting not only users but also machines, devices, and objects. This course will describe the role of digital signal processing in driving wireless communications technologies. The Nyquist Theorem allows bandlimited continuous-time signals to be represented by their discrete-time samples. Consequently, a wireless communications system, including channel impairments like multipath fading and noise, can be analyzed in terms of their discrete-time equivalents. Linear time-invariant (LTI) systems, which are characterized by convolution with an impulse response, can be used to model wireless channels. Deconvolution can be used to equalize the effects of the channel. Upsampling, downsampling, and multirate signal processing allow efficient implementation of pulse shaping at the transmitter and matched filtering at the receiver. This course will present many DSP tools that are relevant to wireless system design, analysis, and optimization. Other practical topics are multiple antenna signal processing (transmit beamforming, spatial multiplexing, and space-time coding), noise-shaping modulation, advanced data converters, fractional-N phase-locked loops, sampling receivers, N-path filters, and pre-distortion linearization.

Course Highlights:

• Review of random processes, noise, and the wireless communications system
• Designing appropriate wireless receiver structures to achieve given design goals
• Modeling wireless channels, and applying channel estimation (estimating an unknown filter response) and equalization (finding a deconvolution filter) techniques
• Estimating carrier frequency offset using parameters of an unknown sinusoid in noise
• Applying antenna selection and signal combining in different multiple antenna diversity systems, including transmit beamforming, spatial multiplexing, and space-time coding
• Review of noise-shaping modulation, advanced data converters, fractional-N phase-locked loops
• Review of sampling receivers, N-path filters, and pre-distortion linearization

Course Learning Outcomes:

• Learn the role of digital signal processing in wireless communications systems
• Analyze wireless channel impairments using discrete-time equivalents
• Model wireless channels as LTI systems, and equalize and mitigate effects of intersymbol interference
• Apply upsampling, downsampling, and multirate signal processing in pulse shaping at the transmitter and matched filtering at the receiver
• Apply antenna selection and signal combining techniques to the major types of MIMO systems
• Understand noise-shaping modulation, advanced data converters, fractional-N phase-locked loops, sampling receivers, N-path filters, and pre-distortion linearization

Software: Matlab and Simulink - Student Version. Software can be purchased from Mathworks.

Course Typically Offered: Online in Fall and Spring quarters.

Prerequisite: ECE-40051 Signals and Systems or equivalent knowledge and experience.

Next Step: Upon completion of this course, consider taking Applied DSP.

Contact: For more information about this course, email unexengr@ucsd.edu.