Transformer NN-based behavioral modeling and predistortion for wideband pas

Abstract. This work investigates the suitability of transformer neural networks (NNs) for behavioral modeling and the predistortion of wideband power amplifiers. We propose an augmented real-valued time delay transformer NN (ARVTDTNN) model based on a transformer encoder that utilizes the multi-head attention mechanism. The inherent parallelized computation nature of transformers enables faster training and inference in the hardware implementation phase. Additionally, transformers have the potential to learn complex nonlinearities and long-term memory effects that will appear in future high-bandwidth power amplifiers. The experimental results based on 100 MHz LDMOS Doherty PA show that the ARVTDTNN model exhibits superior or comparable performance to the state-of-the-art models in terms of normalized mean square error (NMSE) and adjacent channel power ratio (ACPR). It improves the NMSE and ACPR up to −37.6 dB and −41.8 dB, respectively. Moreover, this approach can be considered as a generic framework to solve sequence-to-one regression problems with the transformer architecture

Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks

neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce the complexity without significant performance penalty. First, propose a novel NN with shortcut connections, referred to as shortcut real-valued time-delay neural network (SVDEN), where trainable neuron-wise shortcut connections are added between the input and output layers. Second, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that SVDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity

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)

Residual Neural Networks for Digital Predistortion

Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging. To tackle this problem, we build a connection between residual learning and the PA nonlinearity, and propose a novel residual neural network structure, referred to as the residual real-valued time-delay neural network (R2TDNN). Instead of learning the whole behavior of the PA, the R2TDNN focuses on learning its nonlinear behavior by adding identity shortcut connections between the input and output layer. In particular, we apply the R2TDNN to digital predistortion and measure experimental results on a real PA. Compared with neural networks recently proposed by Liu et al. and Wang et al., the R2TDNN achieves the best linearization performance in terms of normalized mean square error and adjacent channel power ratio with less or similar computational complexity. Furthermore, the R2TDNN exhibits significantly faster training speed and lower training error.Comment: 6 pages, 6 figure

Digital predistortion of RF amplifiers using baseband injection for mobile broadband communications

Radio frequency (RF) power amplifiers (PAs) represent the most challenging design parts of wireless transmitters. In order to be more energy efficient, PAs should operate in nonlinear region where they produce distortion that significantly degrades the quality of signal at transmitter’s output. With the aim of reducing this distortion and improve signal quality, digital predistortion (DPD) techniques are widely used. This work focuses on improving the performances of DPDs in modern, next-generation wireless transmitters. A new adaptive DPD based on an iterative injection approach is developed and experimentally verified using a 4G signal. The signal performances at transmitter output are notably improved, while the proposed DPD does not require large digital signal processing memory resources and computational complexity. Moreover, the injection-based DPD theory is extended to be applicable in concurrent dual-band wireless transmitters. A cross-modulation problem specific to concurrent dual-band transmitters is investigated in detail and novel DPD based on simultaneous injection of intermodulation and cross-modulation distortion products is proposed. In order to mitigate distortion compensation limit phenomena and memory effects in highly nonlinear RF PAs, this DPD is further extended and complete generalised DPD system for concurrent dual-band transmitters is developed. It is clearly proved in experiments that the proposed predistorter remarkably improves the in-band and out-of-band performances of both signals. Furthermore, it does not depend on frequency separation between frequency bands and has significantly lower complexity in comparison with previously reported concurrent dual-band DPDs

Joint compensation of I/Q impairments and PA nonlinearity in mobile broadband wireless transmitters

The main focus of this thesis is to develop and investigate a new possible solution for compensation of in-phase/quadrature-phase (I/Q) impairments and power amplifier (PA) nonlinearity in wireless transmitters using accurate, low complexity digital predistortion (DPD) technique. After analysing the distortion created by I/Q modulators and PAs together with nonlinear crosstalk effects in multi-branch multiple input multiple output (MIMO) wireless transmitters, a novel two-box model is proposed for eliminating those effects. The model is realised by implementing two phases which provide an optimisation of the identification of any system. Another improvement is the capability of higher performance of the system without increasing the computational complexity. Compared with conventional and recently proposed models, the approach developed in this thesis shows promising results in the linearisation of wireless transmitters. Furthermore, the two-box model is extended for concurrent dual-band wireless transmitters and it takes into account cross-modulation (CM) products. Besides, it uses independent processing blocks for both frequency bands and reduces the sampling rate requirements of converters (digital-to-analogue and analogue-to-digital). By using two phases for the implementation, the model enables a scaling down of the nonlinear order and the memory depth of the applied mathematical functions. This leads to a reduced computational complexity in comparison with recently developed models. The thesis provides experimental verification of the two-box model for multi-branch MIMO and concurrent dual-band wireless transmitters. Accordingly, the results ensure both the compensation of distortion and the performance evaluation of modern broadband wireless transmitters in terms of accuracy and complexity

Compensation of nonlinear distortion in RF amplifiers for mobile communications

Compensation of nonlinear distortion of power amplifiers in mobile communications is an important requirement for improving power consumption performance while maintaining efficiency, since mobile phone became an essential accessory for everyone nowadays. This problem demands a good power amplifier model, in order to develop an effective predistortion system. Current researches are focused on modelling and predistortion of power amplifiers with memory, as well as memoryless ones. Different methods for modelling are used, as the Volterra series, polynomial models, look-up tables, the Hammerstein models, the Wiener models, and artificial intelligence systems. For predistortion feedback, feedforward and digital predistortion techniques are used. Among digital predistortion methods there are artificial intelligence systems, used in this thesis for linearization of power amplifier. This thesis presents developed robust method for modelling power amplifiers without memory effects and gives a comparison of proposed method with least squares method. Also, this research presents two novel techniques based on artificial intelligence systems for modelling and predistortion of highly nonlinear power amplifier with memory. The first approach is based on artificial neural networks, while the second one uses adaptive fuzzy logic systems. Forward and inverse models of power amplifier are created with both proposed methods. Superiority of artificial intelligence systems over partial least squares method is presented. Developed models are employed in a cascade to make a linearized system. Verification of proposed methods is carried out through the signal performance parameters and spectra of measured signal and signal from predistortion system. The feasibility and performances of the proposed digital predistortions are examined by simulations and experiments. The comparison of proposed methods is given to present advantages/disadvantages of both methods. The achieved distortion suppression from 72.2% to 93.6% and spectral regrowth improvement from 11.4 dB to 16.2 dB prove that the proposed methods have great ability to compensate the nonlinear distortion in power amplifier