Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization

Barry, Abdoul Aziz; Li, Wantao; Becerra, Juan A.; Gilabert, Pere L.

doi:10.3390/s21175772

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…In addition, an integrated prognostic indicator consisting of multiple dimensions may help to reflect the real and complicated inflammatory and nutritional status ( 29 ). In the face of so many features, it is very important to eliminate over-fitting in feature selection ( 30 , 31 ). Published studies revealed that over-fitting might be solved by applying bootstrapping technique and LASSO Cox PH regression analysis ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Feng

Wang²,

Yang³

et al. 2022

Front. Nutr.

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BackgroundThis study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).Materials and methodsWe retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n = 346) or validation group (n = 148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors with hazard ratio (HRs) and 95% confidence intervals (CIs) grouped by IINS were analyzed. Nomogram was also established to verify the prognostic value of IINS.ResultsAccording to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-years, and 5-years prediction were 0.814 (95% CI: 0.769–0.854), 0.748 (95% CI: 0.698–0.793), and 0.792 (95% CI: 0.745–0.833) in the training cohort and 0.802 (95% CI: 0.733–0.866), 0.702 (95% CI: 0.621–0.774), and 0.748 (95% CI: 0.670–0.816) in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-years cancer-specific survival (CSS) was found in patients with IINS ≤ 2.35 compared with those with IINS > 2.35 in both training cohort (54.3% vs. 11.1%, P < 0.001) and validation cohort (53.7% vs. 18.2%, P < 0.001). The IINS was then confirmed as a useful independent factor (training cohort: HR: 3.000, 95% CI: 2.254–3.992, P < 0.001; validation cohort: HR: 2.609, 95% CI: 1.693–4.020, P < 0.001). Finally, an IINS-based predictive nomogram model was established and validated the CSS prediction (training set: C-index = 0.71 and validation set: C-index = 0.69, respectively).ConclusionPreoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.

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Section: Discussionmentioning

confidence: 99%

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Feng

Wang²,

Yang³

et al. 2022

Front. Nutr.

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“…As a first experiment, the pursuit algorithm of the Bayesian treatment proposed in this article will be contrasted with the OMP and DOMP techniques for the linearization of the commercial class AB PA under test with an average output power of 30.1 dBm and a gain compression of 2.34 dB. Comparison with these techniques is justified because the OMP is a reference in [15] and the DOMP has demonstrated superior performance compared to other algorithms [20].…”

Section: B Comparative Assessment Of the Sbp Versus Greedy Algorithms...mentioning

confidence: 99%

A Sparse-Bayesian Approach for the Design of Robust Digital Predistorters Under Power-Varying Operation

Crespo‐Cadenas

Madero‐Ayora

Becerra

et al. 2022

IEEE Trans. Microwave Theory Techn.

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In this article, a sparse-Bayesian treatment is proposed to solve the crucial questions posed by power amplifier (PA) and digital predistorter (DPD) modeling. To learn a model, the advanced Bayesian framework includes a group of specific processes that maximize the likelihood of the measured data: regressor pursuit and identification, coefficient estimation, stopping criterion, and regressor deselection. The relevance vector machine (RVM) method is reformulated theoretically to be implemented in complex-valued linear regression. In essence, given an initial set of candidate regressors, the result of this sparse-Bayesian learning approach is the most likely model. Experimental results are provided for the linearization of class AB and class J PAs driven by a 30-MHz fifth-generation new radio signal for a fixed average power, where the evolution of the figures of merit versus the number of active coefficients is examined for the proposed sparse-Bayesian pursuit (SBP) algorithm in comparison to other greedy algorithms. The SBP presents a good performance in terms of linearization capabilities and computational cost. Furthermore, the proposed Bayesian framework enabled the design of a DPD model structure, deselect regressors, and readjust coefficients in a direct learning architecture, demonstrating the robustness to changes in the power level over a 10-dB range.

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“…The vector containing the basis functions

following the EGMP behavioral model described in ( 5 ) is

, while

is the

vector of coefficients. As detailed in [ 23 ], feature selection techniques can be applied to select the most relevant basis functions and thus reduce the order of the original EGMP behavioral model.…”

Section: Envelope Tracking Pa Linearizationmentioning

confidence: 99%

Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals

Montoro

Gilabert

2022

Sensors

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This paper presents an envelope linearization technique to compensate for the nonlinear distortion of envelope tracking (ET) power amplifiers (PAs) for 5G new radio (NR) mobile terminals. The proposed envelope optimization (EOPT) method is agnostic of the nonlinear distortion generated in the envelope supply path and can compensate for the nonlinear distortion at the ET PA output without the need to monitor the output at the envelope tracking modulator (ETM). The linearization system in the envelope path is based on the envelope generalized memory polynomial (EGMP) behavioral model. Since the ETM output is not available, an iterative nonlinear least squares solution inspired in the deep deterministic policy gradient (DDPG) algorithm is proposed to extract the coefficients of the EGMP model. The EOPT method is validated on a system-on-chip (SoC) ET PA board designed for mobile terminal applications. Experimental results show the suitability of the proposed method to guarantee the linearity requirements (i.e., adjacent channel power ratio below −36 dBc) with 16.8% of power efficiency when operating the ET PA with 5G new radio test signals of 60 MHz bandwidth operating at 2.55 GHz (band 7). The linearization performance of the proposed EOPT method is comparable to the envelope leakage cancellation (ELC) approach (but saving the need for an analog to digital converter to monitor the ETM output), and can outperform a conventional I-Q digital predistorter based on the generalized memory polynomial (GMP) behavioral model.

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Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization

Cited by 13 publications

References 32 publications

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

A Sparse-Bayesian Approach for the Design of Robust Digital Predistorters Under Power-Varying Operation

Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals

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