Neural network (NN)-based models are perceived as being accurate models for power amplifier (PA) behavioral modeling and digital predistortion (DPD) applications. However, the complexity of NN’s models in terms of the number of coefficients increases with the memory depth, nonlinearity order, signal bandwidth, and the number of carriers. In this article, a novel augmented convolutional neural network (ACNN)-based DPD is proposed to linearize the concurrent multiband PAs. In the ACNN model, the convolution layer with a fully connected layer serves to model the intermodulation distortion (IMD), cross-modulation distortion (CMD), and nonlinearities of the concurrent multiband PA. The pooling layer in the ACNN reduces the model’s complexity. The measurement results show that the proposed DPD requires significantly fewer coefficients and floating-point operations (FLOPs) than the state-of-the-art DPDs. This model’s complexity reduction arises with the number of carriers in the multiband aggregated signal. The measurement results also show that the proposed DPD has a better linearization performance in terms of metrics, such as normalized mean square error (NMSE), adjacent channel power ratio (ACPR), and error vector magnitude (EVM).

This article investigates the effects of the average power variation and the ambient temperature on the behavioral modeling and predistortion of high-power amplifiers concurrently. The measurement results showed that in addition to the average power variation, the behavior of a high-power PA is a function of ambient temperature as well. This article proposes a novel smart digital predistortion (DPD) model that considers both average power variation and temperature (PTI-DPD). The PTI-DPD is implemented using artificial intelligence-based model and it benefits from sets of data formats containing the signal, its delayed versions, average power variation, and ambient temperature. The developed architecture provides an uninterrupted linearization across the ambient temperature and average power variation span without need for a continues feedback path or multiple models. A series of measurements have been conducted to study the PTI-DPD performance across the temperature and average power range. According to the results, the PTI-DPD achieves an average of −48.5 dBc adjacent channel power ratio across the average power and temperature span.

In modern wireless compact transmitters, Power Amplifier (PA) behavior is considerably affected by the impedance mismatch between the PA’s output and the antenna’s input. This PA’s output mismatch results in a reflection at the PA–antenna interface. In this brief, reflection-aware PA modeling and digital predistortion (DPD) techniques are proposed to mitigate the negative impact of this mismatch on the forward and reverse models of the PA. An Augmented Convolutional neural network model $(\boldsymbol{\Gamma }$ ACNN) is proposed to linearize a Doherty PA under different values of the output mismatch using a single set of coefficients. The developed DPD shows robust performance metrics like normalized mean square error (NMSE), and adjacent channel power ratio (ACPR) under diverse complex output mismatch levels.

Chipless RFID has the potential to bring in a paradigm change in industrial applications, notably in the world of the Internet of Things (IoT). This wireless technology permits distant identification, sensing, and tracking, giving tremendous development prospects. Despite substantial advances over the previous century, designing high-performance chipless RFID for IoT applications remains a problem. This research presents a new chipless planar RFID tag for item tracking and identification in IoT systems. The suggested tag uses numerous T-shaped slow-wave structures as micro-reflecting resonators with different dimensions on a lossy substrate to encode information in the tag’s backscattered signal after illumination. The mutual coupling between the resonators has been minimized to improve tag performance and printing variety. A tag with a coding capacity of 24 bits and a compact size of 60 x 40 mm ² was simulated in CST Microwave Studio to validate the suggested approach. To create tags, several patterned configurations were applied on a Rogers RO4350B substrate, and their radar cross-section responses were studied. When compared to typical multi-resonator tags, the suggested tag design demonstrated excellent downsizing and efficient frequency spectrum usage. Furthermore, the Q-factor and coding strength were both high, showing that the suggested technology is capable of manufacturing high-performance chipless tags.

This article presents two novel digital predistortion (DPD) based architectures that jointly mitigate the inphase/quadrature (IQ) modulator impairments and the power amplifier (PA) nonlinear distortion in wireless transmitters. The proposed architectures are multibit cartesian and complex delta-sigma modulator-based joint DPDs, called CDSM-JDPD and CXDSM-JDPD, respectively, which enable using low-cost digital-to-analog converters (DACs) while offering versatile linearization capabilities to combat the coexisting distortions of the PA and the IQ modulator. The proposed approach alleviates the need for reverse modeling and implementation of extra hardware to separately deal with frequency-dependent IQ impairments. Moreover, the CXDSM-JDPD enhances the linearization performance and relaxes the high oversampling ratio (OSR) requirement by quantizing the signal more efficiently. Furthermore, the presented concepts inherently support the use of low-resolution DACs, which offers a tremendous advantage in designing and implementing low-cost and energy-efficient radio transmitters. Extensive set of hardware-in-the-loop RF verification measurements with a commercial PA are provided, including two timely 5G New Radio (NR) scenarios at NR bands n3 and n78, while covering channel bandwidths up to 100 MHz and varying the OSR and the DAC bit resolution. The obtained results demonstrate the excellent linearization capabilities of the proposed solutions and their superiority compared to other DSM-based DPD approaches.