Experimental Proof of Concept Applying Digital Predistortion to Radio Frequency Power Amplifiers

Sogamoso, Óscar Javier Montañez; Fernández, Eduardo Avendaño

doi:10.1109/colcom52710.2021.9486303

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The existing methods for constructing simulation data are mainly divided into two categories: single device modelling and mathematical model modelling. In the single device modelling, power amplifiers are mostly studied [15–18], and behavioural modelling such as Saleh model [16], Polynomial model [17] and Volterra series model [18] are usually used to simulate its non‐linear features and memory effect. In the mathematical modelling, there are mainly two types of models: (1) a time‐domain envelope model which can reflect the transient and steady‐state features of the radar emitter [19]; (2) the phase noise model [20] and the frequency shift mode [21] of the frequency stability that can reflect the spectrum purity of the emitted signal of the radar emitter.…”

Section: Introductionmentioning

confidence: 99%

Radar emitter structure identification based on stacked frequency sparse auto‐encoder network

Liu,

Zhang,

Song

et al. 2023

IET Radar Sonar & Navi

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In the current complex situations of electronic intelligence (ELINT), the authors present a radar emitter structure (RES) identification method based on deep learning at a new level to address the issue of incomplete cognitive information. Firstly, due to the fact that existing simulation data cannot fully reflect the structure features of the entire radar emitter, the structure feature‐level RES model is built using direct digital synthesiser (DDS) technology and radio frequency (RF) simulation platform. Afterwards, considering that the structure features are reflected in the frequency domain, a stacked frequency sparse auto encoder (sFSAE) network is constructed by adding a constraint term in frequency domain to the loss function of sparse auto encoder (SAE). Using deep learning to extract structure features with constraints in different domains is instructive for feature extraction techniques under variable operating parameters. Finally, the extracted structure features are input into the Softmax classifier to perform the identification from the radar signal to the RES. The experimental results show that the proposed method has high generalisation ability and robustness under different modulation types, different operating parameters and different signal to noise ratio (SNR). It also has a high identification rate even for untrained modulated signals.

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