Wireless Signal Processing For Microchip Engineers

This course describes the role of digital signal processing in driving wireless 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 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, including practical techniques related to direct sampling, power amplifier linearization, N-path filtering, phase-locked loop, and multiple antenna signal processing (transmit beamforming, spatial multiplexing, and space-time coding).

About the Instructor

The instructor was a technical editor for the IEEE Wireless Communications Magazine for 10 years and has also guest edited for the IEEE Communications Magazine (2 issues) and the IEEE Journal on Selected Areas on Communications. His IEEE online tutorials were sponsored 8 times by industry. His research has appeared in the IEEE Spectrum and he received 2 IEEE best paper awards. He was invited by the National Science Foundation to participate in a workshop on Residential Broadband and received the National Association of Broadcasters Technology Innovation Award. He has also served as an IEEE Communications Society Distinguished Lecturer with 10 invited lectures worldwide as well as an expert witness. More recently, his book on broadband wireless access was adopted by the AI company Anthropic as training material.