Augmented-LSTM and 1D-CNN-LSTM based DPD models for linearization of wideband power amplifiers

Abstract. Artificial Neural Networks (ANNs) have gained popularity in modeling the nonlinear behavior of wideband power amplifiers. Recently, modern researchers have used two types of neural network architectures, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), to model power amplifier behavior and compensate for power amplifier distortion. Each architecture has its own advantages and limitations. In light of these, this study proposes two digital pre-distortion (DPD) models based on LSTM and CNN. The first proposed model is an augmented LSTM model, which effectively reduces distortion in wideband power amplifiers. The measurement results demonstrate that the proposed augmented LSTM model provides better linearization performance than existing state-of-the-art DPDs designed using ANNs. The second proposed model is a 1D-CNN-LSTM model that simplifies the augmented LSTM model by integrating a CNN layer before the LSTM layer. This integration reduces the number of input features to the LSTM layer, resulting in a low-complexity linearization for wideband PAs. The measurement results show that the 1D-CNN-LSTM model provides comparable results to the augmented LSTM model. In summary, this study proposes two novel DPD models based on LSTM and CNN, which effectively reduce distortion and provide low-complexity linearization for wideband PAs. The measurement results demonstrate that both models offer comparable performance to existing state-of-the-art DPDs designed using ANNs

Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator

A non-parametric technique for modeling the behavior of power amplifiers is presented. The proposed technique relies on the principles of density estimation using the kernel method and is suited for use in power amplifier modeling. The proposed methodology transforms the input domain into an orthogonal memory domain. In this domain, non-parametric static functions are discovered using the kernel estimator. These orthogonal, non-parametric functions can be fitted with any desired mathematical structure, thus facilitating its implementation. Furthermore, due to the orthogonality, the non-parametric functions can be analyzed and discarded individually, which simplifies pruning basis functions and provides a tradeoff between complexity and performance. The results show that the methodology can be employed to model power amplifiers, therein yielding error performance similar to state-of-the-art parametric models. Furthermore, a parameter-efficient model structure with 6 coefficients was derived for a Doherty power amplifier, therein significantly reducing the deployment's computational complexity. Finally, the methodology can also be well exploited in digital linearization techniques.Comment: Matlab sample code (15 MB): https://dl.dropboxusercontent.com/u/106958743/SampleMatlabKernel.zi

Memory polynomial with binomial reduction in digital pre-distortion for wireless communication systems

One of the biggest power consuming devices in wireless communications system is the Power Amplifier (PA) which amplifies signals non-linearly when operating in real-world systems. The negative effects of PA non-linearity are energy inefficiency, amplitude and phase distortion. The increases in transmission speed in present day communication technology introduces Memory Effects, where signal spreading happens at the PA output, thus causing overhead in signal processing at the receiver side. PA Linearization is therefore required to counter the non-linearity and Memory Effects. Digital Pre-distortion (DPD) is one of the outstanding PA Linearization methods in terms of its strengths in implementation simplicity, bandwidth, efficiency, flexibility and cost. DPD pre-distorts the input signal, using an inversed model function of the PA. Modelling of the PA is therefore vital in DPD, where the Memory Polynomial Method (MP) is used to model the PA with memory effects. In this paper, the MP method is improved in Memory Polynomial using Binomial Reduction method (MPB-imag-2k). The method is simulated using a modelled ZVE-8G Power Amplifier and sampled 4G (LTE) signals. It was found MPB-imag-2k is capable of achieving comparable anti-scattering/anti-distortion in MP for non-linearity order of 3, memory depth of 3 and pre-amplifier gain of 2

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

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