Derin Sinir Ağı Tabanlı İnce ve Kaba Modelleme Yoluyla Güç Kuvvetlendirici Performansının Tahmini

Yıl 2025, Cilt: 2 Sayı: 1, 45 - 50, 28.03.2025

Lida Kouhalvandi , Mohammad Alibakhshikenari , İsmail Serdar Özoğuz

Öz

Güç kuvvetlendiricileri (GK), tasarımında yüksek doğruluklu bir karakterizasyonun kritik öneme sahip olduğu lineer olmayan bir devre bloğudur ve zorlayıcı isterlere göre tasarlanması için etkin bir şekilde modellenip optimize edilmesi gerekmektedir. Bu amaçla, öncelikle yapıda kullanılan transistörün X parametreleri ve DNN kullanılarak "ince modeli" elde edilir. Ardından, önceki adımda yapılandırılmış gizli katman yapısıyla transistörün bu sefer S parametreleri elde edilir, çünkü bu "kaba model" genel PA boyutlandırmasını kolaylaştırmaktadır. Son olarak, GK, optimize edilmiş DNN aracılığıyla modellenir ve bu da GK’nın genişletilmiş frekanstaki performanslarının S parametreleri, çıkış gücü, güç kazancı ve verimlilik açısından tahmin edilmesine olanak tanır. Önerilen ince ve kaba modelleme yöntemi, DNN’lerin gizli katman yapılandırmasını belirlemek için yeterli olup, gizli katman sayısı veya her katmandaki nöron sayısı gibi hiperparametreleri belirlemek için ek bir optimizasyon yöntemine ihtiyaç duymamaktadır. Sunulan yöntem, 600 MHz bant frekansında çalışan, 11 dB’den fazla güç kazancı ve yaklaşık %60’lık güç eklenen verimliliğe sahip bir GK’nin tasarlanması ile doğrulanmıştır.

Anahtar Kelimeler

ince ve kaba modelleme, derin sinir ağı, optimizasyon, güç kuvvetlendirici

Prediction of Power Amplifier Performance via Fine and Coarse Modeling Along with Deep Neural Network

Öz

The power amplifier (PA) is a nonlinear design for which an accurate characterization is required for modeling and optimizing effectively. To tackle this difficulty, we present a method based on the fine and coarse modeling approach along with the implementation of deep neural networks (DNNs). For this case, firstly the executed transistor is modeled with the X-parameters and the DNN, as the ’fine modeling’. Then, the S-parameters are modeled with the help of configured hidden-layer structure at the previous step as the ’coarse modeling’ leads to facilitate the overall PA sizing. Finally, the PA is modeled through the optimized DNN, which leads to estimating the performances of PA at the extended frequency in terms of S-parameters, output power, power gain, and efficiency. The presented fine and coarse modeling is powerful enough to configure the hidden-layer configuration of DNNs without any need for other optimization methods for determining the number of hidden layers with neurons in each one. The presented methodology is validated by designing and optimizing a PA with a power gain of more than 11 dB and a power-added efficiency of around 60% operating with 600 MHz band frequency.

Anahtar Kelimeler

fine and coarse modeling, deep neural network, optimization, power amplifier

Kaynakça

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