Power Amplifier Distortion Research Articles

Channel estimation in integrated radar and communication systems with power amplifier distortion

Channel estimation in integrated radar and communication systems with power amplifier distortion

Capacity Analysis and Throughput Maximization of NOMA with Non-linear Power Amplifier Distortion

Capacity Analysis and Throughput Maximization of NOMA with Non-linear Power Amplifier Distortion

Effect of Power Amplifier Distortion on the Speech Transmission Index for Public Address Systems

Effect of Power Amplifier Distortion on the Speech Transmission Index for Public Address Systems

A Novel Specific Emitter Identification Algorithm Based on Amplitude Probability Distribution

We propose a novel specific emitter identification (SEI) algorithm based on amplitude probability distribution (APD). By studying the subtle differences in nonlinear distortion of power amplifiers, the proposed algorithm leverages the real signal amplitude interval probability distribution (AIPD) and analytical signal’s constellation circle interval probability distribution (CIPD) to extract the local distortion features. The simulation results on both the simulated signal and actual signal show that compared to the benchmark methods, the proposed algorithm can achieve significant performance gains with relatively low complexity and has high robustness to training set size and prior information of signal to noise ratio (SNR).

Efficient Neural Network DPD Architecture for Hybrid Beamforming mMIMO

This paper presents several different Neural Network based DPD architectures for hybrid beamforming (HBF) mMIMO applications. They are formulated, tested and compared based on their ability to compensate nonlinear distortion of power amplifiers in a single user (SU) and multiuser (MU) Fully-Connected (FC) HBF mMIMO transmitters. The proof-of-concept is provided with a 64 × 64 FC HBF mMIMO system, with 2 RF chains. The complexity of DPD solution is reduced by using a single Real-Valued Time-Delay Neural Network with two hidden layers (RVTDNN2L) instead of using as many different DPD blocks as there are RF chains in the HBF mMIMO transmitter and it is shown that the proposed architecture better compensates nonlinear distortion compared to the traditional memory polynomial DPD. Two RVTDNN2L DPD architectures are developed and tested for linearization of MU FC HBF mMIMO systems, and it is also shown that the proposed RVTDNN2L DPD architecture efficiently linearizes MU FC HBF mMIMO transmitters in terms of Normalized Mean-Squared Error (NMSE) and Error Vector Magnitude (EVM).

Behavioral Model With Multiple States Based on Deep Neural Network for Power Amplifiers

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.

An Integrated Cancelling Method of Harmonic and Intermodulation Distortion of Short-Wave Power Amplifier Based on Digital Predistortion

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.

Novel Algorithm for Nonlinear Distortion Reduction Based on Clipping and Compressive Sensing in OFDM/OQAM System

Orthogonal Frequency Division Multiplexing with Offset Quadrature Amplitude Modulation (OFDM/OQAM) signal has high peak-to-average power ratio (PAPR) problem. It not only affects the distortion in High Power Amplifier (HPA) but also results in bit error ratio (BER) degradation. In this paper an improved algorithm based on Clipping and Compressed Sensing (CS) is proposed. The transmitter uses clipping to reduce the PAPR and, the receiver uses an improved inverse model, to reduce the nonlinear distortion introduced by HPA and CS cancels the signal distortion introduced by clipping. Simulation results show that the proposed method not only significantly reduces the PAPR of OFDM/OQAM signals, but also effectively improves the BER performance of the system.

A Low Complexity Moving Average Nested GMP Model for Digital Predistortion of Broadband Power Amplifiers

In this paper, a low complexity moving average nested generalized memory polynomial model (MAN-GMP) is proposed for digital predistortion (DPD) of broadband power amplifiers (PAs). As the signal bandwidth increases drastically, the strong nonlinear distortions, especially those induced by the memory effect, are generated from the highly efficient PAs. To compensate for the strong memory effect, a moving average nested envelope memory polynomial (MAN-EMP) model is derived from an accuracy-enhanced GMP model, which offers reduced complexity while suffering from degraded modeling accuracy. The MAN-GMP model is further proposed to improve the modeling accuracy by connecting several memory branches of the MAN-EMP model in parallel. An iterative algorithm is designed to extract the model coefficients efficiently through only one or two iterations. Experimental measurements are carried out on two sub-7 GHz broadband GaN Doherty PAs with up to 200 MHz bandwidth OFDM signals to benchmark the proposed MAN-GMP model against the GMP, the parallel-LUT-MP-EMP (PLUME), the augmented complexity-reduced GMP (ACR-GMP), the generalized twin-nonlinear two-box (GTNTB), and the enhanced Wiener models. The experimental results show that the MAN-GMP model can effectively compensate for the nonlinear distortion of broadband PAs with significant complexity reduction.

A Sparse Neural Network-Based Power Adaptive DPD Design and Its Hardware Implementation

In this paper, an efficient neural-network-based adaptive DPD design which performs well under power varying conditions is presented. The DPD design is derived on the basis of the envelop time-delay neural network (ETDNN). The redefined ETDNN-DPD requires the part of parameter updates, which enables to adapt it to the rapid change of power amplifier (PA) distortion. Additionally, the redefined ETDNN-DPD also maintains the stability of the compensation performances under varying power condition while its structure is pruned by the structured pruning. Furthermore, to verify its practical use, we also propose the weight scaling technique, which reduces multiplications of the redefined ETDNN-DPD, and applied it to the implementation of the redefined ETDNN-DPD on FPGA. Compared FPGA-implemented ETDNN-DPD with FPGA-implemented conventional memory polynomial DPD, we verified that our proposed DPD achieved 3.2 dB better error vector magnitude (EVM) while lower hardware resource utilization at the fixed power level. Moreover, our proposed DPD kept better performance under the varying power condition only by the partial update of its parameters than memory polynomial DPD.

Infinite Impulse Response Structure for Amplifier Modeling and Linearization

Digital predistortion (DPD) is used to compensate for the distortion of power amplifiers (PAs). Inspired by the physical structure of the PA circuit, a feedback structure in the DPD model is favored. In this letter, we proposed an infinite impulse response (IIR) model for PA modeling and linearization, by modifying a generalized memory polynomial (GMP) model. We also propose an iterative identification algorithm to estimate the IIR-DPD coefficients. The experimental results shows that the proposed IIR-GMP model can improve linearization performance compared with the conventional methods.

Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier.

Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR < 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.

Constant Envelope Multiplexing of Multi-Carrier DSSS Signals Considering Sub-Carrier Frequency Constraint

With the development of global navigation satellite systems (GNSS), multiple signals modulated on different sub-carriers are needed to provide various services and to ensure compatibility with previous signals. As an effective method to provide diversified signals without introducing the nonlinear distortion of High Power Amplifier (HPA), the multi-carrier constant envelope multiplexing is widely used in satellite navigation systems. However, the previous method does not consider the influence of sub-carrier frequency constraint on the multiplexing signal, which may lead to signal power leakage. By determining the signal states probability according to the sub-carrier frequency constraint and solving the orthogonal bases according to the homogeneous equations, this article proposed multi-carrier constant envelope multiplexing methods based on probability and homogeneous equations. The analysis results show that the methods can multiplex multi-carrier signals without power leakage, thereby reducing the impact on signal ranging performance. Meanwhile, the methods could reduce the computation complexity. In the case of three different carriers multiplexing, the number of optimization equations is reduced by nearly 66%.

An Upgraded Dual-Band Digital Predistorter Model for Power Amplifiers Linearization

Digital predistortion (DPD) based on Volterra models is commonly employed to counteract the nonlinear distortion of power amplifiers. However, when concurrent dual-band signals are transmitted, 2-D DPD models are required. In this work, upgrading of a standard dual-band model is proposed and justified using multinomial theorem. The linearization performance of the current proposal has been compared to the unextended model. Fifth generation (5G) New Radio signals have been generated to compose a dual-band signal, which later was employed as input signal at Chalmers University of Technology’s RF WebLab. Using coefficient selection techniques, the most relevant regressors are shown, and the importance of the new extension is proven. Linearization results highlight the benefits of this proposal.

Device identification based on distortion of power amplifiers excited by swept sine

Device identification based on distortion of power amplifiers excited by swept sine

Turbo receiver for next-generation wireless communication

The 5G communications operates at extremely high frequency band and have enormous bandwidth. The use of RF power amplifier will cause nonlinear distortion and may cause great difficulties for signal restorations. Several traditional methods have been proposed to compensate for the nonlinear distortions of RF power amplifier in transmitter side. This paper proposes a turbo nonlinear receiver to deal with multipath channel and nonlinear distortions. The proposed nonlinear turbo receiver is based on particle filtering. We consider some time-varying channels and modulation level in the simulation results to compare the proposed turbo nonlinear receiver with some published schemes. The proposed turbo nonlinear receiver outperforms the existing methods.

Multibeam Digital Predistortion for Millimeter-Wave Analog Beamforming Transmitters

In this letter, a novel multibeam digital predistortion (MB-DPD) technique is proposed to linearize multiple beams simultaneously targeted at the multiuser scenario of millimeter-wave (mmWave) analog beamforming transmitters. It is achieved by effectively constructing the predistorted signal for each beam to remove both the intrinsic nonlinear distortion of power amplifier (PA) and the multibeam interference incurred by all beams except the targeted main beam. To validate the proposed technique, a two-beam test on an analog beamforming transmitter was carried out and the measurement results have shown that the proposed method can effectively realize the multibeam linearization, providing a promising solution for mmWave multiuser applications.

Performance Analysis of Fiedler-SLM Mechanism in OFDM for PAPR Reduction

Orthogonal Frequency Division Multiplexing (OFDM) is extensively used in wireless transmissions as it has many advantages, such as reduced Inter Carrier Interference (ICI), Inter Symbol Interference (ISI) and high capacity. But owing to its random envelope, peak to average power ratio (PAPR) is very high, which leads to distortion and non linearity in power amplifiers. PAPR is defined as the ratio of power of peak amplitude to average power of the transmitted signal. Many techniques have been suggested in literature to reduce PAPR such as Clipping and Filtering, Selective Mapping (SLM), Partial Transmit Sequence (PTS). Clipping is an effortless technique but it introduces distortion. Therefore, to improve PAPR without distortion SLM and PTS are widely used. In this paper, a new method has been proposed for SLM PAPR reduction, Fiedler-SLM. A comparative analysis of various SLM techniques has been carried out in the form of simulation to assess the performance of Fiedler-SLM. Simulation results verify that the proposed technique gives improved results on PAPR reduction, without increasing the complexity and also establishes Fiedler-SLM as a strong PAPR reduction technique.

Digital Predistortion for MIMO Transmitters Using Multi-Channel Error Feedback Adaptation

Digital predistortion (DPD) is known as an efficient solution to suppress nonlinear distortion of power amplifiers (PAs) in wireless transmitters to achieve both high linearity and power efficiency. In multiple input and multiple output (MIMO) applications, the power gains of multiple transmitting channels are usually different. Meanwhile, the channel crosstalk cannot be ignored, given the isolations between the physical channels are limited. Accordingly, the linearization performances of the conventional DPD methods are usually degraded. In this article, we propose a novel DPD method for MIMO transmitters, where both the crosstalk and different power gains of multiple channels are considered. In this method, the expected predistorted signals, which would be used to train the DPD model, are firstly obtained by an iterative algorithm derived from the contraction mapping theorem. Then, the coefficients of a MIMO-DPD model are extracted by fitting the DPD model to the obtained predistorted signal. Experimental results verify that the extracted coefficients can achieve an improved linearization performance for the MIMO transmitter with crosstalk between the channels, especially in the case where the channel gains are different. Specifically, for a $2 \times 2$ MIMO transmitter with -20 dB crosstalk and 1.2 dB difference between the two channel gains, the proposed DPD improves the adjacent channel power ratio (ACPR) from -31.4 dBc to -47.1 dBc, which outperforms the conventional DPD by 6.8 dB.

MIMO and Massive MIMO Transmitter Crosstalk

The effects of hardware-induced crosstalk in MIMO transmitters, subject to nonlinear power amplifier distortion, are considered in this paper. A methodology that provides tractable results and a clear understanding of the effects of crosstalk on transmitter performance is introduced and applied to different transmitter models. In particular, a physically motivated $2 \times 2$ MIMO transmitter model, which is subjected to input and output crosstalk, is studied in detail, as well as a behavior motivated transmitter model, which is subjected to linear crosstalk. For the latter structure, asymptotic results, when the number of transmitters tends to infinity, are derived. These results provide insight into different 1D and 2D transmitter structures in the massive MIMO scenario. The methodology provides tractable analytical results of the performance of the transmitter. It is shown that the transmitter crosstalk degrades the performance in terms of normalized mean squared error with 3 dB going from a $2\times 2$ set-up to a 1D array of a massive amount of transmitters, and an additional 3 dB loss going from a 1D to 2D transmitter structure. Transmitter input power back-off optimization is further studied, with back-off determination that takes the effects of MIMO crosstalk into account in order to increase the energy efficiency of the transmitter.