Digital Predistortion Technique Research Articles

Analyze the impact of nonlinear distortions on OFDM-based visible light communication systems

Visible light communication (VLC) has gained attraction for its use in high-speed wireless connectivity leveraging LED lighting elements. Orthogonal Frequency Division Multiplexing (OFDM) is an attractive modulation scheme due to its spectral efficiency and resilience to multipath distortion. However, the nonlinear electro-optic transfer characteristics of optical components introduce signal clipping and quantization noise which corrupt OFDM signals. This paper provides an in-depth analysis of clipping and quantization noise to quantify the impact of LED nonlinearities on OFDM-based VLC system performance. Detailed mathematical models are derived for clipping distortions caused by LED optical power saturation and quantization errors from ADC/DAC finite precision in the modulator and driver circuitry. This analysis is quantified through simulations of the degradations in terms of error vector magnitude (EVM), signal-to-noise ratio (SNR) loss, and bit error rate (BER) under varying clipping ratios and quantization bit-depths. The 16-QAM OFDM transmission shows that the LED driver should possess at least 12 dB signal linear dynamic range and 3-bit quantization to restrict SNR penalty within 3 dB. Adaptive tone mapping and digital pre-distortion techniques are examined to compensate for intensity distortions enabling high-speed OFDM transmission over VLC links.

A hybrid memory polynomial digital predistortion model for RF transmitters

The power amplifier (PA), a key component of the transmitter system, operates near the saturation region, resulting in nonlinear distortion of the output signal, which affects the quality of the transmitter system. For this, a series of linearization techniques are used to compensate for distortion, one of the most effective and widely applied is the digital predistortion (DPD) technique. The traditional DPD models can be categorized into a single model or multiple models cascade or parallel. In this letter, a hybrid memory polynomial (HMP) model is proposed to further enhance the accuracy of the model, which is composed of multiple memory polynomial (MP) models by cascading and parallelizing. The experimental results show that the HMP model has better accuracy than the traditional MP model at the same complexity.

Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s.

High-Efficiency GaN-Based Power Amplifiers for Envelope Nonlinearities’ Mitigation in VHF Wideband Polar-Mode Transmitters

Space-based communications at the very high frequency (VHF) band for air traffic management is a new technology application under development that requires energy-efficient architectures to mitigate the power limitations of satellite platforms. The usage of high-efficiency radiofrequency (RF) transmitters can help reduce the power consumption, but nonlinearities concerning the amplified signal in wide fractional bandwidth systems are a problem to solve. This paper proposes a high-efficiency (RF) power amplifier (PA) for satellite communications at the VHF band that aims to reduce the envelope distortion inherent to wide fractional bandwidth multicarrier polar-mode transmitters. Its design is based on a solution called hybrid-coupled switching voltage PA in combination with gallium nitride (GaN) high electron mobility transistor (HEMT) technology. The developed VHF PA prototype delivers up to 95 W from a 28 V power supply, with a drain efficiency about 80% within the 118 MHz to 138 MHz operating band. To test its linearity performance, operating in a polar-mode configuration, a GaN-based wideband envelope amplifier (EA) has been developed to modulate the RF PA supply port. This EA improves its power efficiency by combining it with a slow envelope power supply (SEPS). Some measurements have been taken for a 100 W peak envelope power (PEP) and 10 MHz (maximum carrier spacing) four-tone digitally-modulated test signal, where any distortion product is attenuated 46 dB below the average power of the amplified signal without applying any digital predistortion (DPD) technique.

Adversarial Modeling Regularization for Modeling and Linearization of Power Amplifiers

Overfitting problem is very common in the coefficient identification process of digital predistortion (DPD) technique. Especially in some related DPD topics, the overfitting problem seriously limits the linearization performance. In this article, to improve the robustness of coefficient identification, we present the adversarial modeling regularization (AMR) technique. By regarding the linear-in-coefficient power amplifier (PA) behavioral models as the prior knowledge of the PA itself, the proposed regularization constrains the PA model’s coefficients by evaluating the fitting performance of other adversarial models. In addition, we present two complexity reduction strategies for the AMR. The AMR is extended and validated under four related tasks that are prone to overfitting: the few-sample learning, the piecewise DPD technique, the aliasing-sampling technique, and the band-limited technique. The experiment results show that the proposed technique can dramatically improve the forward behavioral modeling and linearization performance compared with the other state-of-the-art algorithms in all tasks.

An Inter-Modulation Oriented Learning Based Digital Pre-Distortion Technique via Joint Intermediate and Radio Frequency Optimization

Pre-distortion is a key technique to compensate for the nonlinear distortions caused by the transmitter in wireless communication systems. Generally, pre-distortion can be classified into digital pre-distortion (DPD) and analog pre-distortion (APD), which focus on optimizing and assessing the nonlinearity in their own areas. In this paper, we propose a new DPD approach to optimize the performance metric of the analog RF-domain (i.e., inter-modulation distortion (IMD) or adjacent channel power ratio (ACPR)) and that of the digital IF-domain (i.e., mean square error (MSE)) simultaneously. To make the joint design feasible, we derive a new hybrid performance metric, where the analog preferred metric is defined in the form of digital signals to bridge the gap between digital and analog signal processing. On top of that, an effective DPD scheme is developed based on a new dual time-delayed neural network (TDNN) learning architecture. The coefficients of the TDNN for power amplifier (PA) modeling can be trained offline with a PA dataset, while those of the TDNN for pre-distortion are obtained adaptively by optimizing the proposed joint design metric. Experimental results show that the proposed scheme is able to significantly improve the IMD/ACPR performance without compromising the MSE, compared to conventional DPD schemes.

Scalable Single-Input Behavioral Modeling Architecture for MIMO Systems With Crosstalk

The inherent nonlinear behavior exhibited by power amplifiers (PAs) in saturation mode is a major impediment in wireless systems to achieve higher power efficiency, and higher spectral efficiency. Although PA behavioral modeling and digital predistortion (DPD) techniques are widely utilized at transmitter to characterize and linearize such distorted nonlinear output of PAs, the nonlinear distortions with memory effects have become more severe due to simultaneous strong crosstalk between multiple PA branches in multiple-input multiple-output (MIMO) arrays. Furthermore, current multi-input behavioral models suffer from a sharp increase in number of coefficients as the number of transmitter path increases in MIMO. In overcoming these challenges, we propose a decomposed cross-correlation based single-input-single-output (CC-SISO) behavioral modeling architecture. The proposed solution utilizes a low-cost, novel cross-correlation based method to estimate and cancel the simultaneous nonlinear and reverse crosstalk from multiple PA branches. Once the crosstalk is mitigated, the MIMO DPD can be implemented with single-input DPD blocks which significantly reduces the complexity. Furthermore, CC-SISO DPD eliminates the requirement for signal feedback paths before and after the PA, and thus reduces overall hardware implementation complexity. Through simulations, we demonstrate that the proposed CC-SISO architecture can reduce the overall complexity of state-of-art multi-input DPD models for MIMO systems.

Stable-Sample-Level-Based Digital Predistortion for 2-D Millimeter-Wave Phased Array Steering

This letter proposes a stable-sample-level (SL)-based digital predistortion (DPD) technique for 2-D phased array with fixed-position over-the-air (OTA) feedback (FB) to overcome significant SL fluctuation when the beam is steered. It is achieved by optimizing the phase of the beamformer based on the DPD requirement, which can control the SL at a given value without affecting the main lobe of radiation pattern, relaxing the requirement of FB dynamic range. The radiation pattern and DPD performances are measured on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> phased array at 26 GHz. The measured results show that the proposed method can realize the stable SL with good DPD performance.

New Load Modulation Combiner Having a Capability of Back-Off Control for Doherty Power Amplifiers

A new load modulation combiner (LMC) having the capability of back-off control is proposed for a Doherty power amplifier (DPA). The proposed LMC consists of two transmission lines tied at the combining node, and a shunt element across the other ends of the transmission lines. The shunt element of the LMC becomes virtually open-circuited at the peak power level and allows the carrier amplifier to have an arbitrary load impedance at the low power level. The operating principle and circuit structure of the DPA based on the proposed LMC are presented in detail. The proposed DPA was implemented using GaN-HEMT for the 3.5 - 4.0 GHz band. Using a 5G NR signal with a peak-to-average power ratio (PAPR) of 7.9 dB and signal bandwidth of 100 MHz, the implemented DPA showed drain efficiency (DE) of 49.6 - 54.4% and power gain of 11.1 - 13.0 dB at an average output power of 34.5 dBm. An adjacent channel leakage power ratio (ACLR) was obtained as −50.4 - −45.0 dBc at the average power level with a digital pre-distortion (DPD) technique.

Behavior Modeling and Digital Predistortion of Mismatched Wireless Transmitters Using Convolutional Neural Networks

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\boldsymbol{\Gamma }$ </tex-math></inline-formula> 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.

Load-Mismatch Tracking Digital Predistortion Technique for Mobile-Terminal Power Amplifiers

The power amplifiers (PAs) often suffer from load mismatch in mobile terminals due to the complicated working environments, making linearizing those PAs a challenging task. This article proposes a novel load-mismatch tracking digital predistortion (LMT-DPD) technique for linearizing mobile-terminal PAs under varying load-mismatch conditions. The time-domain polyharmonic distortion (TD-PHD) model is discussed first for the behavioral modeling and linearization of PA under mismatch, followed by a practical model construction method. Then, the proposed LMT-DPD technique is derived based on the superposition principle. According to the real-time load reflection coefficients, the LMT-DPD enables automatic and fast adjustments of the predistorter to adapt to the PA’s nonlinear distortion under different load-mismatch conditions without coefficient recalibration. Experimental validations are carried out on a mobile-terminal PA module at 4.6 GHz with a 100-MHz 5G new radio (NR) signal. The test results show that the proposed LMT-DPD technique can significantly reduce the update frequency of predistorter while realizing excellent linearization performance.

DTC Linearization via Mismatch-Noise Cancellation for Digital Fractional-N PLLs

Digital-to-time converter (DTC) based quantization noise cancellation (QNC) has recently been shown to enable excellent fractional- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> PLL performance, but it requires a highly-linear DTC. Known DTC linearization strategies include analog-domain techniques which involve performance tradeoffs and digital predistortion techniques which converge slowly relative to typical required PLL settling times. Alternatively, a DTC implemented as a cascade of 1-bit DTC stages can be made highly linear without special techniques, but such DTCs typically introduce excessive error from component mismatches which has so far hindered their use in low-jitter PLLs. This paper presents a background calibration technique that addresses this issue by adaptively canceling error from DTC component mismatches. The technique is entirely digital, is compatible with a large class of digital fractional- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> PLLs, and has at least an order of magnitude lower convergence time than the above-mentioned predistortion techniques. The paper presents a rigorous theoretical analysis closely supported by simulation results which quantifies the calibration technique’s convergence time and noise performance.

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

This work proposes a novel machine learning (ML)-based linearization scheme for radio-over-fiber (RoF) systems with external modulation. The proposed approach has the advantage of not requiring new training campaigns in case the Mach-Zehnder modulator (MZM) parameters are changed over time. Our innovative digital pre-distortion (DPD) was designed to favor enhanced remote areas (eRAC) scenarios, in which the non-linearities introduced by the MZM become more severe. It employs a multi-layer perceptron (MLP) artificial neural network (ANN) to model the RoF system and estimate its post-inverse response, which is then applied to the DPD block. We investigate the ML-based DPD performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error (NMSE), resultant signal root mean square error vector magnitude error (EVM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\mathrm{RMS}$</tex-math></inline-formula> ), and complexity. Numerical results demonstrate that the intended DPD method is less complex and outperforms the orthogonal scalar feedback linearization (OSFL) scheme, which has been considered a state-of-the-art DPD technique. The proposal has the potential to effectively and efficiently compensate for the RoF nonlinear distortions, especially in a time-variant system, without needing new training campaigns.

Joint Satellite-Transmitter and Ground-Receiver Digital Pre-Distortion for Active Phased Arrays in LEO Satellite Communications

A novel joint satellite-transmitter and ground-receiver (JSG) digital pre-distortion (DPD) (JSG-DPD) technique is proposed to improve the linearity and power efficiency of the space-borne active phased arrays (APAs) in low Earth orbit (LEO) satellite communications. Different from the conventional DPD technique that requires a complex RF feedback loop, the DPD coefficients based on a generalized memory polynomial (GMP) model are extracted at the ground-receiver and then transmitted to the digital baseband front-end of the LEO satellite-transmitter via a satellite–ground bi-directional transmission link. The issue of the additive white Gaussian noise (AWGN) of the satellite–ground channel affecting the extraction of DPD coefficients is tackled using a superimposing training sequences (STS) method. The proposed technique has been experimentally verified using a 28 GHz phased array. The performance improvements in terms of error vector amplitude (EVM) and adjacent channel power ratio (ACPR) are 7.5% and 3.6 dB, respectively. Requiring limited space-borne resources, this technique offers a promising solution to achieve APA DPD for LEO satellite communications.

Research on digital predistortion technique of harmonic cancellation based on volterra series

When using shortwave PAs for long-distance communication, the output of the PA will have a series of in-band and out-of-band spectral interference problems introduced by intermodulation distortion (IMD) and harmonic distortion because of the nonlinearity and memory effect of the PA. The existing methods of harmonic cancellation include the use of filter banks for harmonic cancellation and digital harmonic cancellation. Based on the Volterra series, the harmonic cancellation generalized memory polynomial (HC-GMP) model is proposed. Compared with the generalized memory polynomial (GMP) model and the memory polynomial (MP) model, the HC-GMP model has better harmonic cancellation capability. The experimental data show that the HC-GMP model mitigates the second and third harmonics of the PA's output signal by about 5 dB and 10 dB, respectively, compared to the GMP model.

Low-Complexity Multi-Symbol Multi-Modulus Weighted Lookup Table Predistortion in UWOC System

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.

Concurrent Multiband Linearization for Power Amplifiers With Intermodulation Distortions

This letter investigates digital predistortion (DPD) models for concurrent multiband (MB) transmitters, considering intermodulations (IMDs) fully and partially overlapping with in-band carriers. In order to mitigate the issue of increasing complexity of IMD terms construction, a novel prefrequency shift MB DPD technique is proposed to translate the baseband input signals in frequency by carrier frequencies. Theoretical analysis is provided to justify the prefrequency shift technique. Experimental measurements have been carried out with a quad-band signal to validate the effectiveness of the proposed technique.

Self-Sensing Digital Predistortion of RF Power Amplifiers for 6G Intelligent Radio

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.

A Novel Digital Predistortion Based on Flexible Characteristic Detection for 5G Massive MIMO Transmitters

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.

Digital Predistortion for Concurrent Dual-Band Millimeter Wave Analog Multibeam Transmitters

This brief proposes a novel digital predistortion (DPD) technique to linearize concurrent dual-band millimeter wave (mmWave) analog multibeam transmitters. Beams at the same frequency band give full play to the spatial multiplexing, so as to improve spectrum utilization. By means of detailed analysis of the system characteristics, a DPD model is proposed to effectively eliminate the distortions caused by power amplifiers (PAs) including nonlinearity, memory effect, the intermodulation distortion (IMD) between two different frequency bands, and the multibeam interference incurred in undesirable sidelobes in the main beam direction simultaneously. To validate the proposed idea, a test bench of mmWave analog multibeam transmitter utilizing Butler matrix was designed, and then it was stimulated by a concurrent dual-band signal at 26.91 GHz and 27.09 GHz, respectively. Compared with existing DPD techniques, the proposed method is suitable for compensating the distortions in dual-band analog multibeam transmitters, which has shown its great potential for the applications in future mmWave concurrent dual-band multibeam wireless communication systems.