Fundamentals of Complex Signal Processing

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Key Points

The aim is to explain the fundamentals of complex signal processing and its extended techniques for real-valued signals.
Introduces Wildeinger differentiation for gradient calculation in complex optimization problems.
Describes generalized linear filters for handling non-proper complex signals.
Demonstrates minimum mean square error estimation and applications to compensation for IQ imbalance in communication systems.
Highlight the utility of Wildeinger differentiation in complex variable optimization.
Showcase the application of generalized linear filters in processing non-proper complex signals.
Present examples of minimum mean square error estimation enhancing communication system performance.

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Demonstrates signal handling methods in the complex domain, indicating practical applications in communication systems.

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Kazunori Hayashi (2026) studied this question.

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