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In this letter, we address the problem of combating the effect of the nonlinearities of the transmit–receive chain in solid-state radars, which are typically generated by the power amplifier (PA) in transmission. This issue is of particular importance in meteorological radar applications, as they require to transmit pulse featuring very low autocorrelation sidelobes, which are significantly raised due to nonlinearities. In this work, we describe an adaptive predistortion design method based on an iterative approach in the spectral domain. The proposed method has been validated by means of hardware using a customized software radio and driving an amplifier into saturation. Two different ideal, highly performing pulse compression waveforms, expressly designed for weather radar applications, were sent to its input. The experimental results demonstrate the benefits of the proposed approach to design the digital predistortion module. [ABSTRACT FROM AUTHOR]

Summary: The nonlinear power amplifier and the analogue output channel filter with nonconstant group delay add nonlinear and linear distortions to the satellite transmitted signal, respectively. Recently, there has been growing interest in using Wiener predistorters, consisting of separate nonlinear and linear components, to compensate for these distortions in the satellite downlinks. The Wiener predistorter has been shown to effectively deal with signal distortions and has lower complexity compared to other state‐of‐the‐art methods. In this paper, we argue that fully compensating the nonconstant group‐delay distortion degrades the overall performance of the Wiener predistortion systems. This is primarily due to the increased peak‐to‐average power ratio of the signal at the output of the linear predistorter. We show that the overall performance of the Wiener predistorter can be improved by undercompensating the group‐delay distortion. We propose two optimizations to address the PAPR growth problem and show using simulations that our approach leads to significant improvement in predistortion performance. Using our Wiener predistorter, the total degradation gap to the ideal limiter channel can be reduced to merely 0.34 dB for 64‐APSK modulated signals. [ABSTRACT FROM AUTHOR]

Digital chaotic sequence spread spectrum (CSSS) communication systems provide a number of advantages for secure communications due to the apparent bandlimited Gaussian noise-like signal characteristics and the non-repeating nature of the underlying chipping sequence. The Rayleigh magnitude distribution of the signal represents a challenge, however, for transmission through linear high-power amplifiers (HPA), both due to the high peak-to-average power ratio (PAPR) and the impacts of selective signal compression on the tails of the time-domain Gaussian signals, which becomes observable by cyclostationary low probability of detection (LPD) metrics. This paper first provides an analysis of HPA compression effects on a single-carrier CSSS signal to understand the HPA-induced artifacts when pushed into compression. This work also defines and evaluates time-domain pre-distortion techniques for memoryless HPAs, validating those formulations through Matlab simulations and live measurements of predistorted CSSS signals transmitted through physical HPAs. Unlike computationally expensive statistical models used to predistort multi-carrier waveforms, this technique is shown to be a computationally efficient method to minimize the increased likelihood of adversarial detection of single-carrier waveforms. The proposed pre-distortion techniques are measured as achieving an average of 3.35-dB in improvements for linear operating range, enabling transmission of CSSS waveforms at an HPA backoff of 4.80-dB without adversely affecting kurtosis-based signal detectability.

Signal distortion can occur when the gain or attenuation of a component changes non-linearly with frequency, which is referred to as nonlinear frequency response. Common communications components such as filters, amplifiers, and mixers can lead to nonlinear frequency responses, which can cause errors in transmitting and receiving. This article outlines the design and demonstration of a static and dynamic finite impulse response (FIR) digital equalizer circuit. Using predistortion topology with a coupled feedback loop, the adaptive Least-Mean Square (LMS) algorithm was implemented. The FIR filter was simulated in MATLAB and Vivado and then implemented onto an Eclypse Z7 Field Programmable Gate Array (FPGA) evaluation board. Simulations showed that the custom RTL module gave the same frequency response that was produced in MATLAB calculations. The filter was able to dynamically equalize the frequency responses of different nonlinear boards that were used as the devices under test (DUT). Measurements showed that the equalizer was able to compensate for system distortion from 0.2 to 0.8 Nyquist frequency. The phase response remained relatively linear across the band of interest, with a group delay flatness less than 10 ns. [ABSTRACT FROM AUTHOR]

The data rate of the zero-power-consumption visible light communication system is limited due to the solar cell bandwidth limit. A new method has been proposed to improve the data rate of communication systems. The predistortion circuit based on the nonlinear compensation principle of the communication system is added between the transmitter and the receiver to equalize the amplitude–frequency response of the solar cell. Based on the measurement of the original amplitude–frequency response of the system, the zero-pole of the predistortion circuit is calculated, and an accurate equalization circuit is designed by using separate components to expand the 3 dB bandwidth of the system. The test results show that the 3 dB bandwidth of the visible light self-powered communication system is increased from 85 kHz to 750 kHz, and the system bandwidth is expanded by nearly 10 times. [ABSTRACT FROM AUTHOR]

MIMO OFDM is the key technology of 4G network system. MIMO-OFDM system enhances the spectrum efficiency and increases the capacity of the system. The implementation of USRP hardware to MIMO OFDM system has been attracted some researchers to conduct the experiments. So we conduct the experiments in a MIMO OFDM system that applies the predistortion technique. In this experiment, we evaluate performances of the predistortion technique by using the artificial neural network. USRP 2920 hardware which is supported by LabVIEW and Phyton software are used in this experiment. OFDM system uses 128 subcarriers to produce an OFDM symbol, and MIMO system uses 2 antennas at transmitter and receiver side. And no obstacles between Tx and Rx, or line of sight transmission scenarios. The performances of the predistortion technique using the artificial neural network algorithm are shown in symbol constellations or Error Vector Magnitude (EVM) at the receiver. And the texts or characters are used as the input of the system. From the experiment results can be seen that the distance between Tx and Rx affects the Error Vector Magnitude (EVM) and predistortion technique produces the Error vector magnitude (EVM) improvement. More shorter the distance between Tx and Rx can decrease distortions of the received signal, At the transmitter side, the performance of predistortion technique is shown as the linearization improvement of the non-linearity power amplifier. Therefore more wider the linear region of power amplifier results the decreasing in band distortion of transmitted signal, and can be seen as the Error Vector Magnitude (EVM) improvement.

Purpose: Extend fast, two‐dimensional (2D) methods of bound and pore water mapping in bone to arbitrary slice orientation. Methods: To correct for slice profile artifacts caused by gradient errors of half pulse 2D ultra‐short echo time (UTE), we developed a library of predistorted gradient waveforms that can be used to interpolate optimized gradient waveforms for 2D UTE slice selection. We also developed a method to estimate and correct for a bulk phase difference between the two half pulse excitations used for 2D UTE signal excitation. Bound water images were acquired in three healthy subjects with adiabatic inversion recovery prepared 2D UTE, while pore water images were acquired after short‐T2 signals were suppressed with double adiabatic inversion recovery preparation. The repeatability of bound and pore water imaging with 2D UTE was tested by repeating acquisitions after repositioning. Results: The library‐based interpolation of optimized slice select gradient waveforms combined with the method to estimate bulk phase between two excitations provided compact slice profiles for half pulse excited 2D UTE. Quantitative bound and pore water values were highly repeatable—the pooled SD of bound water across all three subjects was 0.38 mol 1$$ {}^1 $$H/L, while pooled SD of pore water was 0.30 mol 1$$ {}^1 $$H/L. Conclusion: Fast, quantitative, 2D UTE‐based bound and pore water images can be acquired at arbitrary oblique orientations after correcting for errors in the slice select gradient waveform and bulk phase shift between the two half acquisitions. [ABSTRACT FROM AUTHOR]

At present, we are the dawn of a new era for wireless communication systems. The new dynamic approach to the operation of cellular networks requires an extension of the essential input–output hardware mechanisms, to include intelligence. Traditional neural network structures can be used to embed artificial intelligence into cellular network base stations; however, standard network structures are not an optimal solution for these use cases. In this article, we present a neural network structure, which is specifically designed to provide a more accurate and computationally efficient solution compared with the previous neural network solutions for predistortion of RF power amplifiers (PAs). The proposed network structure is directly compared with alternative neural network solutions, which have been successfully employed for digital predistortion (DPD). The operation of this network is validated for DPD with experimental measurements with wideband signals using the latest generation of commercially available RF hardware. The novel network structure proposed in this work is demonstrated, in practice, to have better performance for a normalized mean square error (NMSE), an adjacent channel leakage ratio (ACLR), and an error vector magnitude (EVM) compared with the most popular previously published neural network. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) provides opportunities to enable high-efficiency wireless communication to dynamically adapt to the local environments and user demands. In this article, a continual learning digital predistortion (DPD) algorithm is proposed to linearize radio frequency (RF) power amplifier (PA) in 6G AI-empowered wireless communication. It is achieved by employing the ability of knowledge retention and knowledge transfer to merge the new PA operating state into the previous model, which makes the model more intelligent when dealing with multiple states. In addition, an augmented self-sensing model is proposed to accurately capture the common characteristics and key dynamic characteristics among different PA operating states to improve the knowledge transfer ability during the continual learning process. The experiments were carried out on a Doherty PA with dynamic configurations associated with power, bandwidth, and signal type. The experimental results have shown that the proposed technique can continuously learn when faced with unpredicted new PA operating states and so as to linearize all known states. The proposed DPD method performs with very low complexity in the long term, which is very suitable for the emerging AI-empowered dynamic scenario. [ABSTRACT FROM AUTHOR]

In this article, we present a novel decomposed vector rotation (DVR)-based recurrent neural network behavioral model for digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs) in wideband scenarios. By representing memory terms of DVR with recurrent states and redesigning the piecewise modeling, we propose a novel recurrent DVR scheme. To ensure stable operation and enhanced modeling accuracy, we integrate the recurrent DVR into the gated learning mechanism of the modified Just Another NETwork (JANET). Experimental results confirm that the proposed DVR-JANET model provides much improved linearization performance with significantly reduced model complexity compared with the recent existing models. [ABSTRACT FROM AUTHOR]

Digital predistortion (DPD) is an effective linearization technique for RF power amplifiers (PAs), but conventional full sampling (FS) DPD systems use ADCs with three to five times signal bandwidth, and high-speed ADCs are expensive and power-hungry. In this article, we develop a novel band-limited DPD for reducing feedback sampling rate and acquisition bandwidth based on a general framework for semisupervised learning called manifold regularization (MR), which utilizes the geometry of unlabeled data to construct regularization terms for mitigating the overfitting problem. Considering the properties of DPD, we design a basis MR term and introduce it into the classical MR to obtain the extended MR (ExMR) method. To validate the proposed ExMR DPD method, experiments were conducted on two different RF PAs operating at 2.4 and 39 GHz, respectively. The test results demonstrate that the proposed ExMR DPD can linearize the RF PA with a 40-MHz acquisition bandwidth at 100-MHz input. The proposed method significantly reduces the cost and power consumption of the DPD system in comparison with the conventional FS DPD method, which provides a promising solution for broadband communication systems. [ABSTRACT FROM AUTHOR]

The coefficients of digital predistortion (DPD) models are often extracted by the least square (LS) method, which has high computational complexity because of the operations of the large matrix. In this letter, a low complexity coefficient extraction method for instantaneous sample indexed magnitude-selective affine (I-MSA) model is proposed. The proposed method uses the tangent of the midpoint of the piecewise function to fit the curve. Therefore, the LS coefficient extraction only needs a small amount of data near the midpoint, rather than using all the data in the segmented region. Simulation and experimental results have confirmed that compared with the original LS method, the proposed method has similar modeling accuracy and linearization performance, but the computational complexity of coefficient extraction is greatly reduced. [ABSTRACT FROM AUTHOR]

Digital predistortion is widely used to compensate the nonlinear distortion of power amplifiers (PAs). Among the digital predistortion methods, the polynomial or deep neural networks (DNNs) models are only adopted with one specific state. When the operating conditions of PAs change, it is necessary to retrain and update the coefficients of the PA model. The generalization ability of the DNN models cannot be presented. To address this issue, this letter proposes one new modeling method that can build one generalized PA model with multiple states based on DNN. This method embeds a set of coding vectors representing corresponding states to build the generalized model. Compared with the traditional DNN model, experimental results show that the proposed method can construct the PA model containing multiple states while ensuring good modeling performance. [ABSTRACT FROM AUTHOR]

In this letter, two basis function multiplexing-based behavioral modeling methods for digital predistortion (DPD) of RF power amplifiers (PAs) are proposed to reduce the running complexity of DPD. The proposed full basis-propagating selection (FBPS) model and reduced-complexity FBPS (RC-FBPS) model give two reasonable ways to multiplex even-order basis functions, extending the basis-propagating selection (BAPS) model which only uses basis function delay and odd-order basis functions. The experimental results confirm that both the proposed FBPS and RC-FBPS models can achieve a good tradeoff between running complexity and performance. [ABSTRACT FROM AUTHOR]

A significant challenge for dual-beam, codesigned systems, such as for simultaneous radar and communications transmission, has been identified as the spurious beams produced by spatial intermodulation from nonlinearities in the transmitter power amplifier. We demonstrate that tuning the load impedance of the power amplifiers can reverse the elementwise power amplifier nonlinear distortion, reducing the magnitude of the unwanted beam transmissions and restoring the integrity of the dual-beam antenna pattern for application to an active electronically scanned array. A comparison to a recently documented predistortion approach for correcting spurious transmissions is provided, and it is shown that impedance tuning can address both element output power and linearity, whereas predistortion addresses only the linearity issue and causes the amplifier gain in many of the array elements to suffer. For multiple dual-beam angle scenarios, simulation results show that impedance tuning is effective at maximizing beam power in both beam directions, maximizing current gain in the element power amplifiers, and minimizing the size of the unwanted spurious beams. [ABSTRACT FROM AUTHOR]

When using short-wave power amplifiers (PAs) for long-distance communication, the output of the amplifier will have a series of in-band and out-of-band spectral interference problems introduced by harmonic distortion and intermodulation distortion (IMD) because of the nonlinearity and memory effect of the amplifier. The existing harmonic cancellation methods include the use of filter banks for harmonic cancellation and digital harmonic cancellation, but these methods will either increase additional hardware resources or only work for harmonic cancellation, neglecting intermodulation. Based on the analysis of the Volterra series, the harmonic cancellation memory polynomial (HCMP) model is proposed. Experimental results show that at the same model complexity, compared with GMP model, the HCMP model has improved the harmonic cancellation ability at different frequency points, in which the second harmonic improved by about 8 dB and the third harmonic improved by more than 12 dB. Meanwhile, the IMD5 and IMD7 cancellation performance of the HCMP model is about 1.6 and 4 dB better than that of the GMP model. [ABSTRACT FROM AUTHOR]

To solve the problem of a high sampling rate in the dual-frequency power amplifier predistortion system, a dual frequency predistortion adaptive sparse signal reconstruction algorithm is proposed. Firstly, a memory effect compensator based on piecewise polynomial model is adopted. The signal fusion is interpreted as the problem of Compressed Sensing sampling reconstruction. In the predistortion feedback loop, the missing fifth-order and high-order cross-modulation signals are reconstructed accurately by using the adaptive sparse algorithm. The minimum mean square solution of coefficient weight approximates the optimal value, and the acquisition error is reduced to improve the linearization effect. The experimental results show that it is of great significance to reduce the sampling rate of dual band predistortion and improve the linearity of power amplifier.

The use of electrical precompensation for high-speed operation of lumped MZMs is demonstrated for 50 Gb/s PAM4 modulation through experiment and simulation. An accurate equivalent circuit model is fitted to S11 measurements and then used to analyse design trade-offs in terms of system performance, energy consumption and drive voltage requirements. If precompensation is not used, careful selection of source resistance with respect to electrode and packaging inductance is shown to be crucial for high-speed modulation. Increasing the electrode inductance is shown to be beneficial as it introduces inductive peaking, which allows optimum performance with a higher source impedance. Furthermore, combining precompensation with a slight inductive peaking and optimised source resistance is demonstrated to reduce drive voltage requirements and further reduce the energy dissipated in the equivalent circuit.

The complexity of digital predistortion (DPD) for the concurrent multiband (MB) power amplifier (PA) has been a bottleneck for 5G wireless communication. A low-complexity MB DPD model has been proposed to reduce the running time complexity of DPD models for concurrent MB scenarios. The proposed MB model can generate the Volterra format basis functions with odd orders, including in-band, cross-band, and intermodulation (IMD) basis terms for arbitrary carrier configurations with an arbitrary number of bands. The optimal basis functions within the band of interests will be selected using a basis-function search algorithm. The lower cost complexity and high model accuracy of the proposed MB model have been validated by the PA linearization experiments in comparison with the memory polynomial MB model. [ABSTRACT FROM AUTHOR]

Generalized memory polynomial (GMP) model is widely used to build and compensate for the nonlinearity of power amplifier (PA). To reduce the complexity of the GMP model, a pruning method represented by the doubly orthogonal matching pursuit (OMP) algorithm was proposed. Lookup table (LUT) based predistortion blocks for compensating PAs are widely used. The minimum complexity of the GMP model does not represent the minimum complexity of LUT. To address this issue, this letter proposes one new pruning method to reduce the complexity of digital predistortion (DPD) block based on LUT. This method first classifies and selects the basis functions according to the memory effect and then optimizes the structure of LUT. Experimental results show that the proposed method can obtain the low complexity of DPD block based on LUT and ensure high precision compensation for the nonlinearity of PA. [ABSTRACT FROM AUTHOR]

This letter proposes a residual-fitting modeling method for digital predistortion (DPD) of broadband power amplifiers (PAs), and then constructs a residual-fitting model. To avoid directly modeling strong nonlinearity and memory effect, the model is split into a conversion, fitting, and recovery module. In this way, the nonlinearity and memory effect of the output signal of PAs are reduced after the conversion module, and then the fitting module models the converted signal, finally the behavioral characteristics of PAs are recovered by the recovery module. In the experimental test, a 100 MHz orthogonal frequency division multiplexing (OFDM) signal is used as input signal of a Doherty PA. The experimental results show that compared with the existing augmented real-valued time-delay neural network (ARVTDNN), the proposed residual-fitting memory polynomial-ARVTDNN (MP-ARVTDNN) model with much fewer coefficients lowers normalized mean square error (NMSE) and adjacent channel power ratio (ACPR). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A kind of novel general multiband digital predistortion (DPD) model is proposed for the linearization of the multifrequency transmitters. By the theoretical analysis, we extend the existing instantaneous sample indexed magnitude-selective affine (IMSA) function-based model to multidimensional cases, which includes the 2-D-IMSA model and the 3-D-IMSA model for concurrent dual-band and triband scenarios, respectively. The core of the new models is to use the multidimensional IMSA function to replace the complex basis function of the classic multiband model through the method of segmentation and approximation. After segmentation, the proposed models have a simple linear structure in each small area, which greatly reduces the model complexity. Experiments show that the proposed models can give similar normalized mean square error and DPD performance in comparison with existing classic models when a power amplifier is excited by two 20-MHz signals in the concurrent dual-band scenario and by three 20-MHz signals in the concurrent triband scenario. [ABSTRACT FROM AUTHOR]

Lookup table (LUT) is an efficient digital predistortion technique for mitigating the pattern dependent distortion in the field of underwater wireless optical communication (UWOC) system. However, the complexity of LUT grows geometrically when applied with high-order QAM modulation formats with multi-pattern length sequences. The complexity reduction of LUT is essential for practical applications. In this paper, we study the patterning effects of LUT predistortion and propose to reduce the LUT complexity by pruning its table entries aided with multi-modulus weighted strategy. The size of LUT has been reduced significantly in experiments, which shows that the complexity of the proposed MMW-RLUT only accounts for 17.6%, 9.8%, 4.9% and 14% of full-size LUT for 64QAM, normal 32QAM, square geometrical shaping (SGS) 32QAM and 16QAM modulated UWOC system, without degradation of performance. [ABSTRACT FROM AUTHOR]

In this letter, we present an adaptive method for linearizing radio frequency (RF) power amplifier (PA). The input and output error is segmented in light of the signal magnitude to improve the convergence speed of the linearizer’s parameter optimization. The feedback bandwidth and the sampling rate of the feedback loop are only 1/8 of the input signal with the proposed method. Experimental results show that the convergence speed of the proposed method is eight times faster than that of the conventional method, and the adjacent channel power ratio (ACPR) is improved from −35.18 to −46.91 dB. [ABSTRACT FROM AUTHOR]

Frequency-selective (FS) digital predistortion (DPD), which focuses on suppressing the nonlinear distortion at a specific band, has gained increased interest in recent years. However, there is a lack of a general FS DPD approach that is applicable to most scenarios and is suitable for practical implementation. This paper proposes an FS DPD method based on a generalized indirect learning architecture (GILA). Our work on this GILA-based FS DPD method is composed of two contributions. First, an FS Volterra series model structure, which consists of a basic linear part and a partial-band pre-compensation part, is proposed to construct an FS predistorter. Second, a GILA-based identification method is proposed to extract coefficients of the FS predistorter with the FS Volterra series model structure. The proposed GILA-based FS DPD is a general method with low complexity for implementation. Both simulations and experiments verify that the target band of the DPD linearization is arbitrary for the proposed GILA-based FS DPD method. As a special case, when using the proposed method to focus on suppressing the out-of-band distortion, the adjacent channel power ratio performance of the proposed method can be 7.1 dB better than that of the conventional full-band DPD. [ABSTRACT FROM AUTHOR]

The Stretched-FrOnt-Leg (SFOL) pulse is a high-accuracy distance measuring equipment (DME) pulse developed to support alternative positioning and navigation for aircraft during global navigation satellite system outages. To facilitate the use of the SFOL pulse, it is best to use legacy DMEs that are already deployed to transmit the SFOL pulse, rather than the current Gaussian pulse, through software changes only. When attempting to transmit the SFOL pulse in legacy DMEs, the greatest challenge is the pulse shape distortion caused by the pulse-shaping circuits and power amplifiers (PAs) in the transmission unit such that the original SFOL pulse shape is no longer preserved. This letter proposes an inverse-learning-based DME digital predistortion method and presents successfully transmitted SFOL pulses from a testbed based on a commercial legacy DME that was designed to transmit Gaussian pulses. [ABSTRACT FROM AUTHOR]

In this article, we present a novel recurrent neural network (RNN)-based behavioral model to linearize radio frequency (RF) power amplifiers (PAs) under wideband excitations. Based on the lightweight Just Another NETwork (JANET) unit, we propose a new neural network structure that is especially suitable for modeling the complex behavior of the RF PAs. A novel signal preprocessing technique is developed to model the complex interaction between amplitude information and phase information in the digital predistortion (DPD) model. By integrating the preprocessing stage into the RNN model, the complete phase-gated JANET (PG-JANET) model can provide enhanced linearization performance with lower complexity compared to the existing models. [ABSTRACT FROM AUTHOR]

Learning from fewer samples can effectively reduce the computational complexity of the parameter identification in digital predistortion (DPD). We refer to this kind of approach as few-sample learning (FSL). However, FSL is always challenging since the ill-conditioning of the matrix will lead to overfitting. In this letter, we explore a stable parameter identification method for FSL DPD based on generalized ridge regression (GRR) and give two closed-form expressions of GRR for fast implementation. Experiments confirm that the proposed method can achieve better performance than the previous methods without any prior knowledge. [ABSTRACT FROM AUTHOR]

The future intelligent communication systems will dynamically adjust the transmitted signal according to the radio environment and human behavior, which will lead to the rapid change of the characteristics of power amplifier (PA) and bring new challenges for digital predistortion (DPD). In this letter, a novel self-sensing DPD (SS-DPD) technique is proposed to linearize PA driven by fast time-varied signals. By automatically sensing the features of input signal and integrating them into the neural network, the proposed model is capable of linearizing the PA operated in such time-varied scenarios without updating DPD coefficients. Furthermore, the polynomial basis functions are embedded into neural network to reduce the complexity. Experimental results on a Doherty PA driven by the fast time-varied signal show that the proposed method can achieve good performance constantly with low complexity. [ABSTRACT FROM AUTHOR]

In this letter, we present a magnitude scaling-based power scalable model for dynamic power transmission. Benefiting from the regularity of power amplifier’s (PA’s) behavior at different power levels, the proposed power adaptive model only needs to update the model parameters in the low magnitude during digital predistortion (DPD). A power factor is applied to scale the magnitude of the input signal, and a piecewise model is used to realize the segmented extraction of the parameters. Experiments carried out on a gallium nitride (GaN) PA show that the proposed method can reduce the model coefficients needed update by at least 50% with the comparable linearization performance. [ABSTRACT FROM AUTHOR]

In this letter, a novel digital predistortion (DPD) technique based on characteristic detection of power amplifiers (PAs) and flexible tuning box is proposed for linearizing large-scale multiple-input–multiple-output (MIMO) system. In this new method, before linearizing the digital block subarray, the PA characteristics of each transmitting chain are detected first. In this way, all PAs can be linearized dynamically without wasting computation, and the performance can be guaranteed. Experiments are carried out to verify the effectiveness of the structure, an mmWave beamforming transmitter was used for measurement and excitation with two long-term evolution (LTE) input signals of single-carrier 30 MHz and four-carrier 20 MHz. The experimental results show that the proposed technique has lower computational complexity and can achieve excellent linearization performance. [ABSTRACT FROM AUTHOR]

• In this paper, we propose a design method for EI-predistortion and splicing seamless MLA-based integral NED, which combining strict optical design and image preprocessing. The construction of MLA is based on sub-channel optimization and splicing, enabling the realization of both VR-NED and AR-NED designs. • Each sub-channel of NED is calibrated using a perfect lens with an effective focal length equivalent to that of the human eye. To achieve a seamless integral NED with high precision, we employ chief ray tracing and image clipping techniques for the accurate localization and acquisition of EIs. • The predistortion EIAs are obtained through a combination of chief ray tracing and RBF image warping techniques. A seamless MLA-based integral AR-NED with a full-FOV of 60° is realized and verified. • Furthermore, a seamless MLA-based integral multifocal VR-NED prototype is further proposed and implemented by integration of multiple discrete single-focal predistortion EIAs. The effectiveness of the proposed method has been validated through illumination and luminance simulations and experiments. The near-eye display (NED) has long been a challenging and intriguing research focus in the fields of augmented reality (AR) and virtual reality (VR). In this paper, we propose a design method of elemental-image (EI) predistortion and splicing seamless integral NED based on microlens arrays (MLAs), which combines rigorous optical design with image preprocessing. The construction of MLA is based on sub-channel optimization and splicing, enabling the realization of both VR-NED and AR-NED. The calibration of each sub-channel in NED is conducted using a perfect lens that possesses an effective focal length equivalent to that of the human eye. To achieve a high-precision seamless integral NED, the precise location and acquisition of initial EIs are accomplished through chief ray tracing and image clipping. The predistortion EI array (EIA) is further obtained by combining chief ray tracing and radial basis function (RBF) image warping techniques. A seamless MLA-based integral AR-NED with a full field of view (FOV) of 60° is realized and verified. A prototype of a seamless MLA-based integral VR-NED is further implemented by integrating multiple discrete single-focal predistortion EIAs. The effectiveness of the proposed method has been validated through illumination and luminance simulations as well as experiments. [ABSTRACT FROM AUTHOR]

The data rate of the zero-power-consumption visible light communication system is limited due to the solar cell bandwidth limit. A new method has been proposed to improve the data rate of communication systems. The predistortion circuit based on the nonlinear compensation principle of the communication system is added between the transmitter and the receiver to equalize the amplitude–frequency response of the solar cell. Based on the measurement of the original amplitude–frequency response of the system, the zero-pole of the predistortion circuit is calculated, and an accurate equalization circuit is designed by using separate components to expand the 3 dB bandwidth of the system. The test results show that the 3 dB bandwidth of the visible light self-powered communication system is increased from 85 kHz to 750 kHz, and the system bandwidth is expanded by nearly 10 times.

The peak-to-average power ratio (PAPR) reduction and linearization techniques are both effective methods to improve the efficiency of the transmitter in digital video broadcasting (DVB) systems. Traditional methods deploy the PAPR reduction model and the linearization model, respectively, without considering their mutual influence. Therefore, the joint optimizations of PAPR reduction and linearization techniques are proposed. However, these methods train the PAPR reduction model and the linearization model based on the time-division training method. It is difficult to meet the requirements of multiple objectives. To address this issue, this paper proposes a joint PAPR reduction and digital predistortion (DPD) method using the real-valued neural network (RVNN) for Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed method jointly trains the PAPR reduction function and the DPD function with multi-objective optimization, to achieve PAPR reduction and linearization simultaneously. Especially, this method unifies the PAPR reduction function and the DPD function into one model based on RVNN, and no extra processing is required at the receiver. Compared with the traditional methods, the experimental results show that the proposed method has superior performance in PAPR, adjacent channel power ratio (ACPR) and bit error rate (BER), while having lower computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a signal-based digital predistortion (DPD) scheme is proposed for the linearization of power amplifiers (PAs) in broadcasting transmitters. The proposed DPD scheme is called the signal-based DPD to distinguish it from the conventional model-based DPD, where the motivation of the former is to synthesize the predistorted signal and that of the latter is to construct the preinverse function of the PA. Our work on this signal-based DPD scheme mainly consists of two parts: a signal-based predistorter and its identification. The signal-based predistorter is derived according to the typical fixed point approach associated with the contraction mapping theorem (CMT), and it has an online cascade structure that is constructed by reproducing offline iterations of the CMT. Identification methods are proposed by tackling the divergent output problem of the signal-based predistorter with a cascade structure. In particular, three coefficient extraction strategies, which have different complexities and different linearization results, are proposed to identify the signal-based predistorter. The experimental results verify that the proposed signal-based DPD scheme can achieve the best linearization performance compared with all the typical model-based DPD methods, including the indirect learning architecture-based DPD, direct learning architecture-based DPD, and iterative learning control-based DPD. [ABSTRACT FROM AUTHOR]

A novel behavioral modeling technique for digital predistortion of radio frequency power amplifiers (PAs) is proposed. The proposed basis-propagating selection (BAPS) model has an advantage on complexity since its basis functions are constructed as delay or a combination of existing basis functions. The BAPS model effectively optimizes the running time complexity while maintaining the generality of a full Volterra series. A greedy search procedure is applied to select the optimal basis functions. Compared with truncated Volterra models and pruned models obtained by popular pruning techniques, the superior performance of the BAPS model, in terms of accuracy and identification stability, has been fairly justified. The linearization measurements on different types of PAs illustrate that the BAPS model can robustly outperform other models from literature while achieving a low running complexity. [ABSTRACT FROM AUTHOR]

In this article, we study closed-loop digital predistortion (DPD) systems and associated learning algorithms. Specifically, we propose various low-complexity approaches to estimate and manipulate the inverse of the input data covariance matrix (CM) and combine them with the so-called self-orthogonalized (SO) learning rule. The inherent simplicity of the SO algorithm, combined with the proposed solutions, allows for remarkably reduced complexity in the DPD system while maintaining similar linearization performance compared to other state-of-the-art methods. This is demonstrated with thorough over-the-air (OTA) mmW measurement results at 28 GHz, incorporating a state-of-the-art 64-element active antenna array, and very wide channel bandwidths up to 800 MHz. In addition, complexity analyses are carried out, which together with the measured linearization performance demonstrates favorable performance–complexity tradeoffs in linearizing mmW active array transmitters through the proposed solutions. The techniques can find application in systems where the power amplifier (PA) nonlinearities are time-varying and thus frequent or even constant updating of the DPD is required, good examples being mmW adaptive antenna arrays as well as terminal transmitters in 5G and beyond networks. [ABSTRACT FROM AUTHOR]

A novel block-oriented time-delay neural network (BOTDNN) model for dynamic nonlinear modeling and digital predistortion (DPD) of RF power amplifiers (PAs) is proposed. The proposed model consists of a dynamic linear network and a static nonlinear network to characterize dynamic nonlinear systems. The dynamic linear network simulates multiple linear filters using a fully connected layer with the linear activation function. The static nonlinear network is constructed based on vector decomposition and phase recovery mechanism. To validate the proposed model, experiments have been carried out with two different PAs operating at 2.4 and 39 GHz, respectively. The test results demonstrate that the proposed model has better PA modeling and nonlinear compensation capabilities than state-of-the-art PA behavioral models, while with significantly lower model complexity. Furthermore, to reduce the system cost, we investigate the problems that arise when the neural network-based behavioral models are applied to low feedback sampling rate DPD and propose an improved method. The experiments confirm that the proposed low feedback sampling rate DPD method can effectively alleviate the deterioration of linearization performance caused by undersampling. [ABSTRACT FROM AUTHOR]

A Light-Emitting Diode (LED) has a nonlinear characteristic, and it contains fundamental limitations for the performance of Visible Light Communication (VLC) systems in indoor environments when using intensity modulation with Orthogonal Frequency Division Multiplexing (OFDM). In this paper, we investigate this nonlinear characteristic with analysis and proposal. At first, we identified the LED nonlinear characteristics in terms of bit-error performances. After analysis, we propose initial predistortion schemes to mitigate the nonlinearity matters. In the predistortion schemes, the nonlinear distortion compensation model contains predistortion features with the LED inverse characteristics. Considering a Direct-Current-biased Optical OFDM (DCO-OFDM) system, we compared the Bit-Error Rate (BER) performances with and without compensation via simulations. The performance on the LED with the compensation showed LED nonlinearity could significantly improve the bit-error performance. In addition, with consideration that the predistortion model is insufficient to represent LED distortion, we investigated possible opportunities of distortion correction using Bidirectional Long Short-Term Memory (BLSTM), one of the leading deep learning approaches. Its result showed promising improvement of the distortion compensation as well. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) based power amplifier (PA) model has been proven to reduce the model complexity significantly. However, due to the calculation mode of the convolutional structure, the application of the CNN-based predistortion model still faces the problem of high computational complexity. In this letter, we use one lightweight CNN to propose a modeling method of the predistorter with low computational complexity for the wideband PA. This method first decomposes the traditional two-dimensional convolution kernels into two kinds of one-dimensional convolution kernels, to create the predesigned filter layer. These two kinds of convolution kernels are used to successively construct the nonlinear terms and the cross basis function terms required by the digital predistortion (DPD) model, respectively. Then, the unnecessary connections of the fully connected structure are removed using the pruning method based on amplitudes, to further reduce the complexity. Experimental results based on 100 MHz Doherty PA show that this predistortion model can significantly reduce the computational complexity, while ensuring that the linearization effects do not deteriorate. [ABSTRACT FROM AUTHOR]

One promising and competitive solution to keep up with the rapid growth in data traffic while at the same time addressing increasing network cost, is the efficient reuse of legacy optical fiber infrastructure. This is highly desirable as deployed single mode fibers represent a valuable asset in the network while new installations would require high additional investments. Multiband (MB) or ultra-wideband (UWB) systems, combined with high symbol rates and higher-order modulation formats, are promising solutions to capitalize the already existing fiber plants. In this contribution, we experimentally demonstrate S-C-L-band reception with 64 GBd dual-polarization (DP) 64-ary and 32-ary quadrature-amplitude modulation (QAM) while using C-band components off-the-shelf (COTS) such as DP-IQ modulators and coherent receivers. To achieve such broadband operation with components that are not optimized for an out-of-band use, mitigation of the associated penalties is decisive. To this end, we apply an end-to-end electro-optical Volterra-based coherent system identification followed by nonlinear digital predistortion of the transmitter. We achieve 150-nm operation bandwidth of the transmission system by performing only a single identification and predistortion at a reference wavelength of 1500 nm. [ABSTRACT FROM AUTHOR]

This article presents how the emission time constant of deep-level traps is responsible for the achievable linearity (linearizability) degradation of gallium nitride high electron mobility transistor (GaN HEMT)-based power amplifiers. It is shown that the worst case scenario happens when the emission rate is on the order of the signal bandwidth, with substantial improvement when they begin to differ. Moreover, because of the strong temperature dependence of the emission time constant, self-heating is shown to play a major role in that linearizability degradation. To demonstrate the importance of the coupling of these two effects (electrothermal and trapping), two GaN HEMT dies with the same structure but different trap activation energies (which dictate the impact of temperature on trapping) were used. Digital predistortion tests using long-term evolution-like signals showed that these devices present different adjacent channel power ratios and normalized mean squared errors, after linearization with the same procedure, which varied with the signal bandwidth as theoretically predicted. [ABSTRACT FROM AUTHOR]

A predistorted folded substrate integrated waveguide (FSIW) filter is developed for a flat passband. The unloaded ${Q}$ -factor of FSIW cavity is predicted numerically and then applied to the coupling matrix synthesis. A filter prototype is fabricated with the low temperature cofired ceramic technology (LTCC), where the direct and cross couplings are realized in a 3D configuration. The synthesized, simulated and measured results agree well with each other. The insertion loss is 3.48 dB at 5 GHz and the 0.2-dB bandwidth is 338 MHz which is equivalent to a conventional filter with the uniform unloaded ${Q}$ -factor of 870, much higher than the one of 180 provided by the applied FSIW resonator. Two transmission zeros locate at 4.50 and 5.46 GHz to improve the frequency selectivity. The filter size is only $18.04\times 9.2\times 1.4$ mm3 and the occupied area is $0.72\times 0.37\,\,\lambda _{d}^{2}$. [ABSTRACT FROM AUTHOR]

Lossy transfer functions are appealing in the design of filters and electric networks, as they can be exactly implemented by physical passive components. However, lossy techniques relax most of the constraints governing the design and thus offer many degrees of freedom but with unclear effects on realizability. This work describes first an analytical method to check whether a given 2-port matrix transfer function is passive. Moreover, for comparison purposes, a technique to assess the maximum allowed predistortion is proposed, related to the highest required quality factor. [ABSTRACT FROM AUTHOR]

Various canonical piecewise linear (CPWL) function-based models have been widely used for modeling radio frequency (RF) power amplifiers (PAs). In most literatures, the thresholds of CPWL are often selected according to the uniform distribution, and how to select the optimal thresholds is rarely discussed. This letter presents a new iterative method, which updates the thresholds after calculating the model coefficients, and finally searches the optimal thresholds. The experimental results confirmed that the method can keep good linearization performance and significantly reduce the number of thresholds and the number of model coefficients. [ABSTRACT FROM AUTHOR]