In this article, we present a novel recurrent neural network (RNN)-based behavioral model to linearize radio frequency (RF) power amplifiers (PAs) under wideband excitations. Based on the lightweight Just Another NETwork (JANET) unit, propose new structure that is especially suitable for modeling complex behavior of RF PAs. A signal preprocessing technique developed interaction between amplitud...

Digital predistortion (DPD) technique is widely used to linearize a radio frequency power amplifier (PA). It is the fastest and cost effective digital processing technique to moderate the distortions in PA caused by nonlinearity and memory effects. Digital predistorter is inverse of the PA, so the design of a digital predistorter requires the accurate identification of PA’s behavior. This paper...

In this paper, we give some observation of applying modern optimization methods for functionals describing digital predistortion (DPD) signals with orthogonal frequency division multiplexing (OFDM) modulation. The considered family model is determined by the class cascade Wiener--Hammerstein models, which can be represented as a computational graph consisting various nonlinear blocks. To assess...

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Recently, new emerging techniques of neuromorphic hardware render spiking neuron networks (SNN) promising as an energy-efficient solution for artificial intelligence (AI). With the idea physics informed neural network, structure can be simple while training data light. However its application in RF telecommunication system is still challenging. This paper, first time literatures, proposes a SNN...

Digital predistortion (DPD) of a phased array requires that multiple transmit paths must be measured by feedback (FB) receiver (Rx). In this article, we propose an FB concept for DPD in time-division-duplex (TDD) arrays. We use single line to collect the waveform samples from parallel Rx. The TDD switches are used enable and disable individual paths. is calibrated comparing outputs power amplif...