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Tu2D-2: An Integrated Doherty Power Amplifier Module Based on an Advanced GaN-on-Si HEMT Technology and a Wideband Power Combiner
In this paper, a highly integrated and wideband Doherty power amplifier (PA) module for 5G mMIMO base station transceivers (BTS) is presented, leveraging the second generation of GaN-on-Si technology ...
Tu2D-3: System-in-Package Doherty Power Amplifier Using Hybrid LDMOS/GaN Line-Up for 5G Macro Driver Applications
This paper presents a compact hybrid LDMOS/GaN Doherty Power Amplifier (DPA) module targeting B1-B3 bands (1.805GHz–2.170GHz). To meet the 5G wide signal bandwidth requirements, this integrated multis...
Tu2D-4: 10 Watt CW Power Handling SPDT RF Switch Using E-Mode p-GaN Dual-Gate HEMT Technology
A compact size 10W continuous wave (CW) power handling single-pole-double-throw (SPDT) RF switch is demonstrated by using p-GaN gate E-mode GaN on Si HEMT technology. Compared to traditional depletion...
Tu2E-1: An Embedded-Structured Convolutional Neural Network for Efficient RF Device Behavior Model Extraction
In this paper, a novel embedded-structured convolutional neural network based approach to extract nonlinear behavior RF device model is proposed. The model features a unique embedded neural network ar...
Tu2E-2: DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion
Digital Predistortion (DPD) is a promising technique to enhance signal quality in wideband RF power amplifiers (PAs). With increasing bandwidth and data rates, DPD faces significant energy consumption...
Tu2E-3: Enhancing Digital Predistortion Performance Under Load Mismatch Using a VSWR Generative Neural Network Simulator
This work introduces a VSWR generative neural network (GNN) simulator designed to simulate VSWR conditions as experienced by a power amplifier. The VSWR GNN simulator is trained using 20 recordings of...
Tu2E-4: Recurrent Neural Network Modeling of Radio Frequency Amplifiers for System-Level Simulation and Design
We report a recurrent neural network (RNN) architecture with embedded static features for the prediction of the response of a power amplifier (PA) in the time domain. We show prediction of PA behavior...
Tu2E-5: Calibration of Wideband Multiport Junction Receivers Using Memory-Polynomial-Informed Neural Network
Multiport junction receivers experience performance degradation due to frequency-dependent behaviour of passive components and power detectors for wideband modulated signals. To calibrate these effect...
Tu2E-6: AdaAFE-CIM: A Hardware Implementation of Subspace Tracking for Adaptive Radar Data Compression
This paper presents AdaAFE-CIM, a novel architecture that implements subspace tracking for adaptive data compression in array processing hardware. The system combines analog feature extraction with di...
Tu2F-1: A Differential Rectifier Design Based on Impedance Splitting and Compression Technique for Achieving > 70% ηRF-DC Over 13dBm Input Dynamic Power Range
This work proposes the design of a differential rectifier architecture using a novel impedance splitting technique (IST) to achieve a high power conversion efficiency (PCE) performance over a wide dyn...