Designing Doherty power amplifiers (PAs) at high millimeter-wave (mm-Wave) range remains a major challenge because most Doherty combiners at this frequency band are inefficient. To resolve this ...bottleneck, we analyze balun response under load variation and subsequently propose the Doherty PA architecture with impedance inverting and scaling baluns that perform active load modulation on differential PAs and delivers combined power to a single-ended load. At the PA output/input stage, instead of utilizing the transformer-based balun commonly used at RF and low mm-Wave frequencies, we explore the coupler-based balun to resonate out the device parasitic capacitance and achieve well-balanced differential to single-ended conversion at high mm-Wave frequencies. Based on the coupled-line theory and our analysis, we derive the Y matrix of the coupler-based balun and originate various solutions for baluns that can invert or scale impedance. From this derivation, we conceive a canonical form of impedance inverting baluns when the electrical length of coupled lines equals 45°. We also propose a close-formed solution for the coupler-based impedance scaling balun that is electrically equivalent to an idealistic transformer in series with a small inductance. We then combine the proposed impedance inverting and scaling balun structures to construct a Doherty network that exhibits desirable Doherty active load modulation and occupies a single-transformer footprint. On the active circuit design, we employ the adaptive biasing circuit for both driver and PA stages of the auxiliary PA to enhance the main and auxiliary cooperation. Using a 45-nm GlobalFoundries CMOS SOI process, we fabricate a proof-of-concept Doherty PA that demonstrates 20.1-dBm P sat , 19.3dBm OP 1,dB , 26% peak power-added efficiency (PAE), and 16.6% PAE at 7-dB power back-off (PBO) from OP 1 dB at 60 GHz. Our results indicate a substantial PAE enhancement at PBO and a state-of-the-art modulation efficiency achieved when transmitting digital pre-distortion (DPD)-free 64-QAM-modulated signals.
Coffea canephora (robusta coffee) is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the ...optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers.
Robusta coffee is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature ranging from 22 to 30°C. We show that robusta has an optimal temperature below 20.5°C, which is much lower, by 1.5–9°C than current estimates. Current assessments, based on robusta having an optimal temperature range over 22°C, may be overestimating its suitable production range.
This work introduces, for the first time, a millimeter-wave imaging system with a "synthetic" ultrawide imaging bandwidth of 98 GHz to provide the ultra-high resolutions required for early-stage skin ...cancer detection. The proposed approach consists of splitting the required ultra-wide imaging bandwidth into four sub-bands, and assigning each sub-band to a separate imaging element, i.e., an antenna radiator. Each of the sub-band antennas transmits and receives signals only at its corresponding sub-band. The captured signals are then combined and processed to form the image of the target. For each sub-band, a Vivaldi tapered slot antenna fed with a combination of substrate-integrated waveguide and coplanar waveguide is designed and microfabricated. Design techniques are also provided for the four similarly-shaped sub-band antennas for achieving excellent impedance matches (S 11 <; -10 dB) and nearly constant gains of 10 dBi over the entire 12- 110 GHz bandwidth. The design procedure is validated by comparing the simulated results with measurements performed on the fabricated prototypes. Excellent agreements are obtained between simulations and measurements. Finally, the feasibility of detecting early-stage skin tumors in three dimensions is experimentally verified by employing the sub-band antennas in a synthetic ultra-wideband imaging system with a bandwidth of 98 GHz. Two separate setups, each comprising a dispersive skin-mimicking phantom as well as two dispersive spherical tumors, are constructed for imaging experiments. Lateral and axial resolutions of 200 μm are confirmed, and a successful reconstruction of the spherical tumors is achieved in both cases.
This paper presents a Doherty radiator architecture that explores multi-feed antennas to achieve an on-antenna Doherty load modulation network and demonstrates high-speed high-efficiency transmission ...of wideband modulated signals. On the passive circuits, we exploit the multi-feed antenna concept to realize compact and high-efficiency on-antenna active load modulation for close-to-ideal Doherty operation, on-antenna power combining, and millimeter-wave (mm-Wave) signal radiation. Moreover, we analyze the far-field transmission of the proposed Doherty radiator and demonstrate its wide field of view (FoV). On the active circuits, we employ a gigahertz-bandwidth adaptive biasing at the Doherty auxiliary power amplifier (PA) path to enhance the main/auxiliary Doherty cooperation and appropriate turning-on/-off of the auxiliary path. A proof-of-concept Doherty radiator implemented in a 45-nm CMOS silicon on insulator (SOI) process over 62-68 GHz exhibits a consistent 1.45<inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula>-1.53<inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> PAE enhancement at 6-dB power backoff (PBO) over an idealistic class-B PA with the same PAE at <inline-formula> <tex-math notation="LaTeX">P_{\mathrm {1\,dB}} </tex-math></inline-formula>. The measured continuous-wave performance at 65 GHz demonstrates 19.4/19.2-dBm <inline-formula> <tex-math notation="LaTeX">P_{\mathrm {SAT}}/P_{\mathrm {1\,dB}} </tex-math></inline-formula> and achieves 27.5%/20.1% PAE at peak/6-dB PBO, respectively. For single-carrier 1 Gsym/s 64- quadratic-amplitude modulation (QAM), the Doherty radiator shows average output power of 14.2 dBm with an average 20.2% PAE and −26.7-dB error vector magnitude (EVM) without digital pre-distortion. Consistent EVMs are observed over the entire antenna FoV, demonstrating spatially undistorted transmission and constant Doherty PBO efficiency enhancement.
This paper presents a packaged 28-GHz Chireix outphasing transmitter (TX) for power amplifier (PA) efficiency enhancement at both peak and back-off output power (PBO). The main objective is to ...investigate the possibility of employing a multi-feed antenna as a multi-port passive network to perform outphasing active load modulation. As a proof of concept, an outphasing TX architecture is proposed by leveraging a dual-feed loop antenna (DLA) to achieve high efficiency transmission of wideband complex modulated signals. Theoretical analysis and simulation results prove that the proposed DLA is electrically equivalent to an ideal differential series non-isolating power combiner, enabling superior on-antenna outphasing operation. Multi-functional signal processing is therefore achieved in a single loop-antenna footprint, including high-efficiency on-antenna power combining, outphasing active load modulation, and millimeter-wave (mm-Wave) signal radiation. Moreover, unlike conventional spatial vectorial combining techniques (e.g., spatial IQ or spatial outphasing), the antenna-level signal processing preserves the wide antenna unit field of view (FoV) and is particularly suitable for array or multiple-input-multiple-output (MIMO) applications. The proposed Chireix TX contains two phase-shifted sub-TXs simultaneously driving a DLA on package. The measurements demonstrate a 53%/43% PA drain/power-added efficiency (DE/PAE) at 17.1-dBm saturated output power (<inline-formula> <tex-math notation="LaTeX">P_{\mathrm {sat}} </tex-math></inline-formula>) and 36%/23% DE/PAE at 6-dB PBO. The high-efficiency Chireix TX supports up to 15 Gb/s 64-QAM with 10.4-dBm average output power and 34% average PA DE (19.2% average PAE) while maintaining EVM/ACLR better than −27.5 dBc/−28 dBc in a wireless radiation testing environment.
We propose a framework to analyze mm-wave baluns directly from physical parameters by adding a dimension of Machine Learning (ML) to existing electromagnetic (EM) methods. From a generalized physical ...model of mm-wave baluns, we train physical-electrical Machine Learning models that both accurately and quickly compute the electrical parameters of mm-wave baluns from physical parameters, reducing the need for full-wave simulations and advancing several aspects of mm-wave designs. One of the advancements is a fully automated design process that accurately generates full EM designs of mm-wave baluns when given an electrical specification and a metal option. The automated technique only takes several seconds to complete, compared to hours-weeks of the current trial-and-error methods, and notably the approach can optimize mm-wave baluns directly for the lowest metal loss. Another advancement is the theoretical interpretation of several high-level and abstract questions concerning mm-wave designs, in which we quantify the optimum transistor sizes for the last stage of a class-AB differential power amplifier on an on-chip process and derive the rule of thumb describing the inverse relationship between the optimum device sizes and mm-wave frequencies.
A computational approach to automate mm-wave Directional Couplers can greatly improve upon the current techniques that rely on trial-and-error. To build such an automated framework, we propose a ...Machine Learning technique that, within a computational time of seconds, can fully automate the physical realizations of mm-wave Directional Couplers over a wide range of electrical specifications and metal options. Our proposed technique includes the training of fully connected neural networks that accurately learn the physical-electrical relationships of coupled lines and includes the multi-head gradient-based optimizers that optimally compute the physical dimensions for given design specifications. By demonstrating the automated designs of mm-wave Directional Couplers with numerous coupling factors and dielectric thicknesses, we verify the effectiveness of our proposed computational method.
A novel W-band (75-110 GHz) surface micromachined Vivaldi antenna fed with a new combination of coplanar waveguide and substrate-integrated waveguide is proposed. The design procedure of the feeding ...structure and the effects of the antenna's key structural features on its return loss and gain are studied in detail. The selected exponential taper with optimized feeding parameters results in a significantly higher gain (gain > 10 dBi) compared to similar tapered slot antennas reported in the literature over the entire bandwidth of operation. The antenna is fabricated using microfabrication processes, and high correlations between simulation and measurement results confirm its low-loss performance and symmetric and stable radiation pattern over the entire frequency range of 75-110 GHz.
Optimal utilisation of the sun's freely available energy to generate electricity requires efficient predictive models of global solar radiation (GSR). These are necessary to provide solar energy ...companies an early and effective market entry to support renewable energy integration into electrical grids. We propose a hybrid deep learning CNN-REGST method where a Convolutional Neural Network is integrated with a dual-stage Stacked Regression (Level-O Learner and Level-O predictor) followed by a Support Vector Machine (Level-1 Learner) with its hyperparameters optimised using the HyperOpt function to predict the daily GSR with high accuracy. Six solar energy farms in Queensland, Australia, are selected as testing sites and the predictive features from Global Climate Models and observations, derived using marine predator algorithm, are employed to build the CNN-REGST prediction model. We include a feature selection process based on meta-heuristic methods to select the optimal predictors used as inputs for the resulting CNN-REGST model. Our hybrid model is rigorously evaluated to analyze its performance over a yearlong, and all four season data. We also compare the proposed CNN-REGST model with several deep learning (i.e., CNN, Long-term Short-term Memory Network LSTM, Deep Neural Network DNN) and conventional ML approaches (Extreme Learning Machine ELM, Stacked Regression REGST, Random Forest Regression RFR, Gradient Boosting Machine GBM, Multivariate Adaptive Regression Splines MARS) using the same test datasets. The simulations carried out show that the proposed hybrid model is significantly accurate in GSR predictions compared with the deep learning and the ML models as well as a commonly used persistence model. We conclude that the CNN-REGST prediction model could be a useful scientific ploy incorporated in modern solar energy monitoring technologies to utilize a greater proportion of sustainable energy resources captured from the sun into consumer electricity for conventional-renewable hybrid energy grid systems.
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