Sparse Estimation Technique for Digital Pre-distortion of Impedance-Mismatched Power Amplifiers

Hemsi, Cyro S.; Panazio, Cristiano

doi:10.1007/s00034-021-01659-z

Cited by 2 publications

(3 citation statements)

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“…Finally, before applying group-sparse techniques, each RV 2 model block Φ (k,l) is further sub-divided into M k + 1 non-overlapping sub-blocks, through the sample delay index m = m 1 in Equation ( 13), i.e., the elements of ũ(n) in Equation (23). The length of each sub-block of Φ (k,l) is given by:…”

Section: Proposed Sparse-group Lasso Solutionmentioning

confidence: 99%

“…The so-called FV curse of dimensionality has motivated several Volterra pruning techniques in the literature, the most prominent being the memory polynomial (MP) [7], the generalized memory polynomial (GMP) [8], the dynamic deviation reduction (DDR) [9], [10] and block-oriented [11], [12] behavioural models. Moreover, several specialized models have also been proposed, each of which taking into account additional nonlinearities, e.g., [13]- [23]. Nevertheless, choosing among several candidates with a given complexity the most accurate pruned structure to describe a PA or DPD with unknown characteristics often requires trial and error.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the proposed model is validated through simulations and experimental comparison with well-known DPD behavioural models. Finally, we should mention that in [23] we also use LASSO and SGL, but with the Wiener Hammerstein with feedback (WHFB) model proposed, based on physical knowledge, specifically for DPD of PAs subject to impedance mismatch, therefore with a more limited application than the model proposed here.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Flexible Reduced-Volterra Model for Power Amplifier Digital Pre-Distortion

Hemsi

Panazio

2022

IEEE Access

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The Volterra framework plays an important role in digital pre-distortion (DPD) for power amplifier (PA) linearization. In practice, the full-Volterra (FV) model is avoided due to the so-called curse of dimensionality, which has motivated several Volterra pruning techniques in the literature. However, choosing the pruned-Volterra structure with a given complexity that most accurately describes a PA or DPD with unknown characteristics often requires trial and error. The motivation of this article is to propose a reduced-Volterra model that extends traditional memory polynomials, e.g., the generalized memory polynomial (GMP), by including higher-dimensional terms in a flexible and controlled way, avoiding the curse of dimensionality of the Volterra model and resulting in a model that can apply to a wide range of PA and DPD. Furthermore, the article investigates the parsimonious sizing of the proposed model using least absolute shrinkage and selection operator (LASSO) and sparse-group LASSO (SGL) convex optimization, significantly reducing the model's running cost, while preserving its performance. In our experimental validation, the sparse versions of the proposed model achieved 3 dB better adjacent channel power ratio (ACPR) than the conventional GMP model for the same or lower DPD running cost, at the expense of a more costly estimation, which is performed off-line.

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Section: Proposed Sparse-group Lasso Solutionmentioning

confidence: 99%