Showing papers by "Kevin A. Morris published in 2023"

Transfer Learning for the Behavior Prediction of Microwave Structures

Abstract: Microwave structure behavior prediction is an important research topic in radio frequency (RF) design. In recent years, deep-learning-based techniques have been widely implemented to study microwaves, and they are envisaged to revolutionize this arduous and time-consuming work. However, empirical data collection and neural network training are two significant challenges of applying deep learning techniques to practical RF modeling and design problems. To this end, this letter investigates a transfer-learning-based approach to improve the accuracy and efficiency of predicting microwave structure behaviors. Through experimental comparisons, we validate that the proposed approach can reduce the amount of data required for training while shortening the neural network training time for the behavior prediction of microwave structures.

Highly Efficient 3-Bit Digital Power Amplifier for OFDM Waveform Amplification

Abstract: In this work, we present a digital power amplifier (DPA) and a signal-optimized control technique suitable for the amplification of orthogonal frequency division multiplexing (OFDM). OFDM is a high peak-to-average power ratio (PAPR) signal that is naturally arising from high spectrum efficiency modulations. A 3-bit DPA is implemented with three power-scaled transistors which are turned on and off based on the signal amplitude, while phase modulation is restored using the radio frequency (RF) input signal. The unavoidable nonlinearities at the DPA output due to PA switching are minimized by accounting for the OFDM signal probability density function (pdf). This pdf is a priori knowledge to design an optimal quantizer that minimizes distortion by distributing the DPA power levels where the signal amplitude is more similar to the original one. Back-off efficiency within $2^{3}=8$ possible states is then optimized by implementing a load-modulating power combiner. Theory and an example design of the combiner network are provided and demonstrated for this DPA. The reported DPA prototype operates at 1.5 GHz with a 3-bit control and achieves a maximum power-added efficiency (PAE) of 64.3% and maintains a drain efficiency greater than 47% over the output power range from 36.6 to 45.2 dBm (8.6-dB range).

A Learning-Based Methodology for Microwave Passive Component Design

Abstract: Microwave passive component design is of particular interest to radio frequency (RF) scholars and engineers. Although a plethora of studies have been carried out over multiple decades, designing high-frequency structures that offer high performance still heavily relies on heuristic methods and even rules of thumb. Thus, the process is often inefficient, and outcomes are not guaranteed. This article proposes a novel cascaded convolutional neural network (CNN) model to speed up the design process of planar microwave passive components. Given target behavior specifications, our proposed neural network model can quickly and accurately suggest proper component structures for single or multiple frequency bands. The feasibility and reliability of our model are validated here by both electromagnetic (EM) simulation and a fully instrumented implementation. The experimental results demonstrate that the proposed model can design planar passive components, including two-port matching networks and three-port power dividers. Moreover, our model provides passive component topologies that are fundamentally different from canonical number-limited templates and, therefore, yields novel architectures for passive microwave components. It also facilitates rapid passive components design flow for targeted electrical behavior within a limited board area. The proposed cascaded CNN model and the associated methodologies in this article are generic and, thus, can be easily extended to the design of any symmetrical planar microwave passive components.