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

Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-based outputs (Evoke), an instance of the recent Evolino class of methods. Evoke evolves recurrent neural networks to detect and represent temporal dependencies while using quadratic programming/support vector regression to produce precise outputs. Evoke is the first SVM-based mechanism learning to classify a context-sensitive language. It also outperforms recent state-of-the-art gradient-based recurrent neural networks (RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure

Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

This paper proposes a robust digital signal recovery (DSR) technique to tackle the high signal-to-noise ratio (SNR) variation and transmitter memory effects for broadband power efficient down-link in next-generation low Earth orbit (LEO) satellite constellations. The robustness against low SNR is achieved by concurrently integrating magnitude normalization and noise feature filtering using a filtering block built with one batch normalization (BN) layer and two bidirectional long short-term memory (BiLSTM) layers. Moreover, unlike existing deep neural network-based DSR techniques (DNN-DSR), which failed to effectively take into account the memory effects of radio-frequency power amplifiers (RF-PAs) in the model design, the proposed BiLSTM-DSR technique can extracts the sequential characteristics of the adjacent in-phase (I) and quadrature (Q) samples, and hence can obtain superior memory effects compensation compared with the DNN-DSR technique. Experimental validation results of the proposed BiLSTM-DSR with a 100 MHz bandwidth OFDM signal demonstrate an excellent performance of 11.83 dB and 9.4% improvement for adjacent channel power ratio (ACPR) and error vector magnitude (EVM), respectively. BiLSTM-DSR also outperforms the existing DNN-DSR technique in terms of the ACPR and EVM by 2.4 dB and 0.9%, which provides a promising solution for developing deep learning-assisted receivers for high-throughput LEO satellite networks

Simplified topology for integrated circuit buffer behavioural models

This paper addresses the behavioural modelling of digital integrated circuit buffers for performance assessment of high-speed data links. A new modelling technique, with several important advantages is described. All the requirements of black-box identification are met: the approach relies exclusively on the observation of the external port voltages and currents, thus allowing the extraction of models that mimic the operation of real devices without insight on their internal structure. Furthermore, unlike the standard algorithms currently used in EDA tools, the method described in this study provides a straightforward solution to modelling the input-output behaviour. Good model performance in overclocking conditions is an important advantage. This study also investigates the possibility of accounting for power-supply voltage variations and provides a simple solution

Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance

Despite advances in experimental and theoretical neuroscience, we are still trying to identify key biophysical details that are important for characterizing the operation of brain circuits. Biological mechanisms at the level of single neurons and synapses can be combined as ‘building blocks’ to generate circuit function. We focus on the importance of capturing multiple timescales when describing these intrinsic and synaptic components. Whether inherent in the ionic currents, the neuron’s complex morphology, or the neurotransmitter composition of synapses, these multiple timescales prove crucial for capturing the variability and richness of circuit output and enhancing the information-carrying capacity observed across nervous systems

Variable-fidelity electromagnetic simulations and co-kriging for accurate modeling of antennas

Accurate and fast models are indispensable in contemporary antenna design. In this paper, we describe the low-cost antenna modeling methodology involving variable-fidelity electromagnetic (EM) simulations and co-Kriging. Our approach exploits sparsely sampled accurate (high-fidelity) EM data as well as densely sampled coarse-discretization (low-fidelity) EM simulations that are accommodated into one model using the co-Kriging technique. By using coarse-discretization simulations, the computational cost of creating the antenna model is greatly reduced compared to conventional approaches, where high-fidelity simulations are directly used to set up the model. At the same time, the modeling accuracy is not compromised. The proposed technique is demonstrated using three examples of antenna structures. Comparisons with conventional modeling based on high-fidelity data approximation, as well as applications for antenna design, are also discussed