Evolutionary Multiobjective Optimization for Digital Predistortion Architectures

Li, Lin; Ghazi, Amanullah; Boutellier, Jani; Anttila, Lauri; Valkama, Mikko; Bhattacharyya, Shuvra S.

doi:10.1007/978-3-319-40352-6_41

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…A preliminary version of this paper has been presented in [12]. This paper goes beyond the previous optimization framework presented in [12] by employing fidelity-based validation of our employed power estimation approach, and applying an improved system accuracy measurement for DPD design space exploration.…”

Section: Related Workmentioning

confidence: 99%

“…This paper goes beyond the previous optimization framework presented in [12] by employing fidelity-based validation of our employed power estimation approach, and applying an improved system accuracy measurement for DPD design space exploration. More specifically, in Section 4, computation of estimation fidelity is integrated to verify the accuracy of the proposed power estimator, and the EVM measurement is modified to better represent the accuracy of the system.…”

Section: Related Workmentioning

confidence: 99%

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Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Li¹,

Ghazi²,

Boutellier³

et al. 2017

EAI Endorsed Transactions on Cognitive Communications

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In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across timevarying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for runtime adaptation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Li¹,

Ghazi²,

Boutellier³

et al. 2017

EAI Endorsed Transactions on Cognitive Communications

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, the identification of such an optimum might involve inefficient trial-and-error approaches or the non-trivial formulation of a high-dimensionality optimization problem. As a consequence, an active area of research concerns approaches based on numerical optimization or machine learning for optimal signal-input synthesis and operating point selection of digitally controlled PAs [9][10][11][12][13], and DIDPAs in particular [14,15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Optimization of Input Split and Bias Voltage in Digitally Controlled Dual-Input Doherty RF PAs

et al. 2022

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Digitally controlled Dual-Input Doherty Power Amplifiers (DIDPAs) are becoming increasingly popular due to the flexible input signal splitting between the main and auxiliary stages. Nevertheless, the presence of many degrees of freedom, e.g., input amplitude split and phase displacement as well as biasing for multiple stages, often involves inefficient trial-and-error procedures to reach a suitable PA performance. This article presents automated parameter setting based on coordinate descent or Bayesian optimizations, demonstrating an improvement in the performance in terms of RF output power and power-added efficiency (PAE) in the presence of broadband-modulated signals, yet maintaining suitable linear behavior for, e.g., communications applications.

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“…Typically, the search for the best features (or regressors) to be added to a model of a given complexity is a combinatorial task, performed by exhaustive search [24], becoming a nondeterministic polynomial time (NP-hard) problem [25]. Indeed, although heuristic methods, such as hill-climbing [26], particle swarm optimization (PSO) [27], [28] and evolutionary algorithms [29] have been proposed, they still suffer from scalability [24], [30].…”

Section: Introductionmentioning

confidence: 99%

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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Evolutionary Multiobjective Optimization for Digital Predistortion Architectures

Cited by 5 publications

References 8 publications

Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Automatic Optimization of Input Split and Bias Voltage in Digitally Controlled Dual-Input Doherty RF PAs

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

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