Ghannouchi, FadhelWang, Dongming2016-01-262016-01-262016-01-262016Wang, D. (2016). Modeling and Mitigation of Nonlinear Distortions by using Neural Networks for LTE-A Wireless Transmitters (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26939http://hdl.handle.net/11023/2775A two-box DPD system based on the cascade of a memory predistortion model and a memoryless predistortion model is proposed. The memory predistortion model is designed by using an ARVTDNN, while the memoryless predistortion model is designed by using a memoryless ARVTDNN. Considering the signal at the output of the PA linearized by the proposed two-box DPD system, one will notice that its ACPR is demonstrated by measurement results to have a better performance by 3-5 dB than that of an existing two-box polynomial-based DPD system. Most importantly, the two-box polynomial-based DPD system does not meet the spectrum emission mask of -45 dBc. In addition, the proposed two-box ARVTDNN-based DPD system meets the ACPR requirements for the observation bandwidth of as low as 155 MHz.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.EngineeringEngineering--Electronics and Electricalpower amplifier, modern communication systems, neural networks, digital predistortionmaster thesis10.11575/PRISM/26939