Look-Up Table for Superconductor Digital-RF Predistorter

Most existing behavioral models of the radio frequency (RF) power amplifiers assume that power amplifiers are memoryless devices. But in broadband communication systems, memory effects of the RF power amplifiers are significantly observed. The traditional memoryless models can no longer accurately depict the input-output relationship of power amplifiers. This paper investigates power amplifiers memory effects and a pre-distortion method is proposed for linearizing RF power amplifiers with memory effects. A valid behavioral model based on the Volterra series is suggested to treat memory effects. Since the characteristics of amplifiers change during transmission time, a recursive least squares algorithm with size- fixed observation matrices is developed to update the parameters of the proposed power amplifiers model. The identification algorithm can decrease computational complexity and data storage space and facilitate real-time online identification. Simultaneously, the pre-distortion model is constructed to simulate. Simulation results show that the proposed method can effectively compensate for the nonlinear distortion and memory effects of RF power amplifiers. The model's accuracy is used to evaluate power amplifiers memory effects by the enormalized mean square error (NMSE). In the end, simulation and evaluation of this pre-distortion system proceeded, and the power spectral density estimation was also applied to calculate the adjacent channel power ratio.

The nonlinearity of power amplifiers is one of the principal reasons degrading the transmission quality in the wireless communication systems comprising the single-carrier frequency-division multiple access (SC-FDMA) utilised for the up-link in the LTE system. In this letter, we analysed and predicted the nonlinearity of SC-FDMA signals in spectrum respect. Owing to the nonlinearity of radio frequency (RF) power amplifiers, an analytic model is established on the basis of a third-order intercept point (), which is fit for weakly nonlinear systems and devices. The research results confirm that the spectrum regrowth with RF power amplifier is specified and measured efficiently. This sort of design inherits the advantages of orthogonal frequency division multiplexing (OFDM) and is of great help for both designing RF power amplifiers and further improving the spectrum regrowth with pre-distortion outside channels, extended to other wireless systems based on SC-FDMA modulation.

It has been challenging for a wireless sensor node to realize long-range real-time information feedback in the low power harvesting scenarios, due to the limited power budget. In this article, a sensor node powered by low radio frequency (RF) power harvesting is proposed with long-communication capability without using an RF power amplifier or external power source. The proposed sensor node is capable of rectifying input RF power as low as −20 dBm at 433 MHz, while simultaneously generating a 866 MHz second harmonic during the harvesting process. To address problems of long-range data transmission, the generated harmonic is amplified by a tunnel diode powered by rectified dc power, while carrying sensor data of temperature, humidity, and CO2 concentration. The fabricated sensor node shows an output power of around −14.75 dBm for the second harmonic, even with an injecting RF power as low as −20 dBm. It enables a long range exceeding 40 m for indoor communication scenarios with a low energy consumption of just 20.7 mJ. The sensor node demonstrates a great potential for future internet of things (IoT) applications in low RF power harvesting scenarios.

Two differential radio frequency (RF) power amplifier (PA) architectures are presented in this paper both of which can be used to implement a wideband Doherty PA. Classic PA architecture is compared to a SB (SB) PA in order to determine an architecture that offers the widest input impedance match without external component tuning. Both amplifiers presented in this paper have been designed to output an average of 10.5 dBm of continuous wave (CW) power with 1.8 V supply. The classic PA architecture is tuned to 1.9 GHz central frequency by external components and has a bandwidth of 500 MHz at S11=−10 dB. The SB PA architecture has an operating range spanning from 0.8 GHz to 2.2 GHz at S11=−10 dB. SB PA has a higher power added efficiency (PAE) at output compression point (OR-P1dB) in the whole 1.2 GHz–2.4 GHz range by 3 %–7 % with a peak efficiency of 31 %. The presented results indicate that a SB RF PA architecture can be implemented designing a wideband Doherty PA.

The design of a temperature compensating bias circuit applicable to GaAs HBT radio frequency (RF) power amplifiers (PAs) is presented. It is developed by adding a simple voltage-compensation circuit to the existing base bias circuit. It is applied to a stage bypass RF PA and the experimental results show that the quiescent current (Q-current) remains nearly unchanged over a temperature range between −30–85°C, which markedly improves the linearity of the PA at low temperatures.

Linear amplification using nonlinear components (LINC) is a method of vector summing two constant amplitude phase-modulated signals to achieve power amplification. The theoretical efficiency of the LINC power amplifier has been reported as 100% since highly efficient nonlinear constant amplitude amplifiers can be used. However, the 100% efficiency performance is only possible at one or two loads along the power output curve. The bulk of the papers regarding LINC has focused on clever implementations of the signal vector decomposition as well as methods to achieve highly linear signal separation. There has been little regard in the literature to the signal combiner implementation necessary to achieve the high power-added efficiency (PAE) of the LINC radio frequency (RF) power amplifier. Efficiency is not an intrinsic property of the combiner implementations, however, the combiner method is the single biggest contributor to efficient performance of a LINC RF power amplifier. This paper develops an analysis method that determines the efficiency of the LINC power amplifier as a function of the amplitude modulation statistics. This can be employed to design the RF communication system amplitude modulation characteristics and to tradeoff and optimize the RF transmitter PAE.

This paper presents a novel technique of modeling a radio frequency (RF) power amplifier (PAs) using polynomials with infinite impulse response (IIR) bases functions. Non-linear PA behavior, including both short-term and long-term memory effects, is described with very few parameters compared to existing Volterra series models. Moreover, the use of polynomials with IIR bases is far less computationally intensive thereby providing a significant advantage over competing methods. Power amplifiers designed for second generation (EDGE) wireless system are investigated for model validation. Excellent results are obtained, where the error between model prediction and measurements is better than 50 dBc, with the amplifier being driven with an EDGE signal at several power levels.

Dynamic load modulation (DLM) is a technique for increasing radio frequency (RF) power amplifier (PA) efficiency when amplifying amplitude modulated signals. It does this by manipulating a tuneable matching network (TMN) between the PA and the antenna in harmony with the envelope of the transmitted signal. The TMN is usually based on varactor diodes, requiring control voltages with up to 100 V peak-to-peak swing. Conventional off-the-shelf op-amps cannot produce a big enough voltage swing at sufficient speed, especially when amplifying modern communications standards like the 3GPP 4G Long Term Evolution (LTE). The discrete current mirror driver amplifier described here capable of producing 50V peak-to-peak voltage swing. When the driver amplifier was combined with a suitable RF PA and TMN, 46.0% DC-to-RF efficiency was achieved at 33.9 dBm output power (POUT) with a 1.4 MHz LTE signal. The adjacent channel power ratio (ACPR) was -40 dBc, confirming that the current mirror amplifier is suitable for LTE use.

A new dynamic behavioral modelling technique, based on support vector regression (SVR), is presented in this paper. As an advanced machine learning technique, the SVR method provides an effective option for behavioral modeling of radio frequency (RF) power amplifiers (PAs) including the nonlinear and memory effects. In this paper, the basic theory of the proposed SVR modelling method is provided, along with details on model implementation of RF PAs. Experimental test example for a LDMOS PA is presented, revealing that the new modelling methodology provides a very efficient and extremely accurate prediction.

In this work, a new particle swarm optimization (PSO) method with dynamic nonlinear self-adaptive hyperparameters (DNSHs) (PSO-DNSHs) is proposed for radio frequency (RF) power amplifier (PA) design. The theory and advantages of this method are described and analyzed. Based on the proposed PSO-DNSH method, a multioctave PA and a broadband pseudo-Doherty load-modulation balance amplifier (PD-LMBA) are designed. The multioctave single-ended PA achieves an output power exceeding 40.3 dBm, a drain efficiency greater than 60.9%, and a gain of over 10.3 dB between 0.5 and 3.6 GHz. While the broadband PD-LMBA shows a saturated output power of 41.1–43 dBm, a saturation drain efficiency of 57.7%–77.6%, a 6-dB output power back-off (OBO) DE of 47.3%–64.6%, and a 10-dB OBO DE of 51.2%–68.4%. Compared with other existing PSO-based methods, the results demonstrate that the proposed method not only achieves better performance for both PAs, but also shows faster convergence capability. Modulated signal tests along with the digital predistortion (DPD) are also conducted for both PAs, 20-MHz 5G new radio (NR) signal with 6- and 10-dB peak-to-average power ratios (PAPRs) are used for single-ended PA and PD-LMBA, respectively. Excellent linearity is achieved for both PAs.

Utilizing asymmetric Doherty technology, this paper designs a high-efficiency radio frequency (RF) power amplifier (PA) for 5G base station applications. To improve the performance of PA and narrow the gap between simulations and practices, we use compatibility methods to design the circuit, which keeps the layout dynamically adjustable. By incorporating redundant U-shaped microstrip lines, the impedance matching network can be dynamically fine-tuned during debugging based on real-time hardware conditions. Furthermore, independent debugging paths for both main and auxiliary amplifiers are designed to enable the multi-stage debugging strategy. Performing separate debugging for each branch first, followed by combined debugging ensures both amplifiers achieve optimal operation states. The proposed strategy improves debugging efficiency while achieving precise parameter optimization. To verify the feasibility of the scheme proposed in this paper, we use CGHV40030F transistors to design a Doherty PA worked at 3.5 GHz and complete the hardware implementation and tests. Simulations and practice results prove that the architecture of asymmetric Doherty increases the back-off efficiency, and the compatibility design can make debugging easy and align the practice results closely with the simulations. We observe the saturation drain efficiency of 73.5% and the back-off efficiency of 47.5% from measurements, which confirms the effectiveness of the proposed compatibility design approach.

In this work a comparison of three different behavioral models for radio frequency (RF) power amplifier (PA) is done. The used models were a Volterra series model, the memory polynomial model (MPM) and the generalized memory polynomial model (GMPM). A special focus is put on their modeling capabilities in the adjacent and alternate band. It was found that the Volterra model gives the best modeling performance but also has a high amount of parameter. The GMPM has a significantly lower amount of parameter yet a comparable modeling performance. The MPM uses only very few parameter but still achieves a good modeling performance. The modeling performance for each model was highest in the inband, is less in the adjacent band and decrease further in the alternate band. It also is highly dependent on the used order of nonlinearity.KeywordsPower Amplifier (PA)behavioral modelingnonlinear filtersnormalized mean square error (NMSE)adjacent channel error power ratio (ACEPR)

An adaptive, digital, baseband predistortion (PD) algorithm that compensates for the memoryless nonlinearities of radio-frequency (RF) power amplifiers (PAs) for wireless systems using non-constant-envelop modulation schemes is presented. Compared with the conventional, complex-gain predistorters based on lookup tables (LUTs), the proposed direct-learning, multilevel lookup table (ML-LUT) approach assisted by a hardware-efficient loop delay compensation scheme achieves a significant reduction in convergence time and an improvement in linearization accuracy in the presence of an unknown loopback delay. The experimental results in an FPGA prototyping platform show that the fast adaptation speed enables the predistorter to track time-varying PA nonlinearities as fast as in the tens of kilohertz range, constituting a potential solution for highly efficient PAs in mobile handsets.

Wireless sensing and communication for space exploration in areas inaccessible to human often suffer from severe performance degradation due to the cryogenic effects on the transmitters’ circuits. To survive extreme temperatures, programmable radio frequency (RF) power amplifiers (PA) can be built into the transmitter, and intelligent PA controllers need to be integrated into the system to interact with the environment and restore the PA’s functionalities. This problem can be modeled as the controller acts (control the PA) in an environment to maximize the reward (signal quality), and it is most suitable to use reinforcement learning as a solution. This paper presents a cryogenic and energy-efficient reinforcement learning (RL) module on Field Programmable Gate Arrays (FPGA) that can directly program the PA. By characterizing a self-healing PA in a liquid nitrogen environment, we generated an RF signal data set and built an interactive RL environment to model the PA’s behaviors across its configurations and cryogenic temperatures down to −197°C. We developed a deep RL model with a high generalization capability introduced by the neural networks to control the PA and restore its performance. The RL model with fixed-point training and inference is implemented on FPGA to survive the cryogenic conditions and carry out fast and low-power training and inference for PA control. All functionalities of the programmed FPGA operate correctly in the cryogenic testing environment.

In this paper, a novel frequency component controllable digital predistortion (DPD) for radio frequency (RF) power amplifiers (PAs) is proposed. The detailed analysis and experimental results demonstrate that the target band or frequency component to be linearized can be arbitrarily chosen according to user's demand by employing the proposed approach, which makes digital predistortion much more flexible in future wireless communication systems.