# Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks
## 1 Introduction  
Future wireless communication systems, including the fifth and sixth generation (5G and 6G) technologies, are growing day-by-day, resulting in exponentially high traffic data [1, 2, 3, 4, 5, 6]. To provide a reliable communication system, the overall performances of power amplifiers (PAs) and antenna as active and passive components play important roles [7, 8, 9, 10]. Typically, PAs exist near the antennas to transmit a high-power signal, and are challenging in terms of various specifications, especially linearity [11]. This is a big challenge in radio frequency (RF) designs, and designers must find a well-suited solution for optimizing the overall communication systems to balance the linearity trade-off. The main structure of communication systems consists of concurrent existence of passive and active devices. Hence, due to the various unbalanced effects generated from these devices, additional efforts with strong optimization methods are needed to improve the overall performance of systems [12, 13, 14, 15]. Recently, various papers have studied cointegrated antenna with amplifiers leading to an enhancement of the overall performances, mainly in terms of energy efficiency and bandwidth. In [16], a methodology is explained for designing cointegrated antenna with an amplifier through balanced multiport feeding. The presented feeding with the optimization method improved the bandwidth of the overall system. In another study [17], a PA-aware precoding approach is suggested for investigating the compensation of the PA along with the multiple-input and multiple-output(MIMO) antenna to guarantee transmission quality. To enhance the energy efficiency of arrays under PA designs, an approach is offered in [18] where it the transmit duration with powers is determined. A nonlinear optimization, based on the far-field is proposed in [19] where the radiating part is optimized through an artificial neural network (ANN). In [20], the target is to find the optimal matching networks for enhancing the power transfer efficiency among arbitrary impedances to achieve the optimal transmission. In the domain of radar, a joint synthesis is presented in [21] for ensuring the compatibility with amplifiers. In another study ([22]), a solution for resource allocation is described that includes antenna array and PAs at the user and relay nodes by proposing the low-complexity suboptimal relay selection. In [23], the nonlinearity of PA is modeled through the Saleh model to be used in the satellite-terrestrial network leading to provide optimal beamforming. The sub-optimal algorithm is employed in [24], for optimal MIMO precoding by considering the consumed power from the PA side. Herein, finding an optimal drain impedance matched to the antenna with high performance specification, especially in terms of linearity, requires intelligent-based optimization methods [25]. Recently, machine learning paves the way for signal processing, RF, and very-large-scale integration (VLSI) designs as it is able to model and optimize complex configurations by providing the relationship between input and output specification [26]. In communication systems, due to the simultaneous existence of PAs and antennas, it is not straightforward to achieve the optimal load impedance from antenna side that is connected to the transistor's drain impedance. In the previously reported studies, this problem with the linearity investigation is not considered. Hence, to tackle these drawbacks, we propose modeling of the antenna and PA through deep neural networks (DNNs) for finding the optimal load impedance, and optimizing the linearity specification of whole system based on the 'Multivariate Newton's' Method [27]. To the best of the author's knowledge, a joint methodology for modeling and optimizing cointegrated antenna and PA through DNNs is proposed for the very first time in the literature.