: This talk discusses AI/ML-based techniques to model and mitigates nonlinear distortion and hardware impairments of Radios intended for 5G and beyond wireless networks. The talk focuses on the neural network (NN) based digital pre-distortion techniques for linearizing multi-band, and multiple-input multiple-output (MIMO) phased array transmitters. We will review the most popular NN-based behavioral models using Deep Neural Networks, Augmented Neural Networks, and Shallow Neural Networks. The talk will concentrate on various aspects of DPD implementation using AI/ML techniques and discuss the evolution of NN-based DPD modeling techniques for dynamic nonlinear systems. We will also present State-of-the-art approaches such as Real-Valued Focused Time-Delay Neural Network. (RVFTDNN) and Convolutional Neural Networks (CNN) and discuss their suitability for in-field applications. These models’ performance will be assessed in terms of their capability to mitigate the transmitter’s distortion and hardware impairments such as antenna’s crosstalk, PA’s nonlinearity, I/Q imbalance, and dc offset for multi-band and MIMO applications for 5G and 6G applications. Co-sponsored by: Staracom Speaker(s): Prof. Fadhel Ghannouchi, Room: M-2110, Bldg: Pavillons Lassonde, 2500 Chemin de Polytechnique, Montreal, Quebec, Canada, H3T1J4