This study presents an automated optimization-oriented strategy for designing high power amplifiers (HPAs) using deep neural networks (DNNs). The proposed strategy consists of two optimization phases that are applied sequentially. In the first phase, the circuit topology is optimized by determining the number of passive components in the input and output matching networks using deep learning classification network. In the second optimization phase, component values are estimated using a deep learning regression network with electromagnetic-based Thompson Sampling Efficient Multiobjective Optimization (TSEMO). The proposed approach is compact, in the sense that the optimum solution is automatically generated by the process, opposite to the conventional approaches where manual post-processing is required to prune the process outcomes. It addresses the problem of heavy reliance of the system performance on the designer's experience and automatically generates valid layouts. In the demanding HPA design problem, uses of DNNs have been shown to provide much more accuracy than conventional shallow neural networks. The effectiveness of the proposed method is verified by implementing two designed HPAs, including GaN HEMTs. The efficiency-oriented optimized amplifier reveals higher than 60% drain efficiency, and the gain-oriented optimized amplifier has 17.6-18 dB linear gain in the frequency band of 1.8-2.2 GHz.

In this paper, free space path loss, ray tracing with one and two reflection points and logarithmic distance path loss models are used to determine distance between nodes according to RSSI values. First of all, required parameters to use called models are determined by making RSSI characterization study in corridors. Then determined parameters are used to perform a localization test RSSI characterization and localization tests are carried out for different areas and environments' effects and success of all models are investigated.

The space race has played a significant role in the history of the last half century, and satellites are among the most important parts of space race and technology. More than 6,000 artificial satellites have been deployed since the first, Sputnik, in 1957; some 1,000 are still in operation. When most people not involved in satellite technologies see or hear the word "satellite," they think of satellites weighing several tons. However, developing material, rocket, and mechanical and electrical technologies are rapidly changing the world of artificial satellites.

A high-speed downlink communication system is required to meet various applications for small satellites. The purpose of this research is to develop a high-data-rate (typically over 300Mbps) communication system. Generally, the operation at nonlinear region provides high efficiency for a RF power amplifier. However the amplitude-phase modulated signal, which is an efficient scheme in term of frequency band, requires high linearity. In order to amplify amplitude-phase modulated signal for high data rate, a 2W X Band GaN-HEMT power amplifier and digital pre-distortion technique were developed. In this paper measurements and simulations of the system are presented.