Recent Advances on Signal Processing Solutions for Distortion Mitigation Due to Power Amplifier and Non-Ideality of Transmitter System
Meenakshi Rawat, Karun Rawat and Fadhel Ghannouchi
Affiliation: University of Calgary, Calgary, Canada.
Keywords: Predistortion, power amplifier linearization, memory effects, IQ imbalance in modulators, power amplifier behavioral models, neural networks, Wiener-Hammerstein, adaptive filters, power amplifier, modulators, wireless communication systems, FEEDFORWARD TECHNIQUE, transmitted signal, linearization techniques, error amplifier
Recent research in communication is inclined towards modulation and assesses techniques that are more spectrally efficient to support more users per base station to reduce overall network costs and make the services affordable to subscribers. However, these signals are highly envelope varying that invokes distortion in transmitted signal due to transmitter nonlinearity and other linear impairments. Therefore, to extract optimum benefits in terms of cost and performance, transmitter linearization solutions are constantly evolving. This patent review highlights developments in this arena from past decade to current and futuristic trends, focusing on the evolution from bulky analogue to reconfigurable digital predistortion and from memoryless nonlinearity based distortion mitigation to recent trends to compensate for memory and other transmitter line-up imperfections.
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