Ghannouchi, Fadhel M.Hasan, Md Mahmud2015-11-192015-11-192015Hasan, M. M. (2015). Implementation of Neural Network Adaptive Digital Pre-distortion for Wireless Transmitters (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26468http://hdl.handle.net/11023/2646An Artificial Neural Network, more precisely Real Valued Spatiotemporal Neural Network (RVSNN) based real time adaptive digital pre-distorter (DPD) is proposed and implemented on FPGA for the linearization of nonlinear dynamic wireless transmitter. Power amplifier is the core component of wireless transmitter, and is the source of all the nonlinearities and distortions. To alleviate these distortions, DPD, designed based on the inverse characteristics of power amplifier, is the key technology in 3G and beyond wireless communications. Though off-line DPD ameliorates system performance considerably, it is still dependent on changes in system temperature, voltage, load mismatch and average signal power. In this regard, real time DPD provides increased stability along with the standard linearity and inter-modulation distortion requirements. But the proposed online RVSNN model is very sensitive to hardware delay and hard to realize, thus offline RVSNN model is implemented on FPGA which provides identical performance to its MATLAB counterpart.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.Engineering--Electronics and ElectricalADAPTIVE DIGITAL PRE-DISTORTIONNEURAL NETWORK PRE-DISTORTIONmaster thesis10.11575/PRISM/26468