Composite Neural Network Digital Predistortion Model for Joint Mitigation of Crosstalk, $I/Q$ Imbalance, Nonlinearity in MIMO Transmitters

Multi-input multi-output (MIMO) is anticipated to be a prominent technique proposed in the wireless communications to improve the system capacity and data rates of the wireless networks. However, the MIMO transmitter suffers from imperfections, such as crosstalk, power-amplifier (PA) nonlinearity, in-phase and quadrature (I/Q) imbalance, and dc offset. Investigating these effects, this paper proposes neural network (NN)-based digital predistortion (DPD) as an integral solution to compensate for crosstalk, PA nonlinearity, I/Q imbalance, and dc offset imperfections simultaneously in MIMO transmitters. The proposed NN DPD model provides a one-step single-model digital mitigation solution to multibranches of MIMO transmitters. With the increase in the dimensions of MIMO transmitter, the proposed NN-based DPD model provides a better compensation for transmitter imperfections and also reduces the complexity as compared to the state-of-the-art DPD methods. The proof-of-concept is provided with the 2×2 and 3×3 MIMO transmitters in the presence of strong PA nonlinearity, crosstalk, I/Q imbalance, and dc offset for homodyne as well as heterodyne transmitters' cases.