Design of low power, high gain LNA for WCDMA range and parameters extraction using Artificial Neural Network (ANN)

With the demand of 3G wireless network access is increasing, high performance Wide Band Code Division Multiple Access (WCDMA) transceivers are needed. Low Noise Amplifier (LNA) is the key component of WCDMA transceivers. In this paper, we designed the various configurations of LNA (Single-ended LNA, Differential LNA, Current-reuse LNA) at 180nm technology and compared their performance metrics. The LNA is designed for low power, high gain applications and it provides a series of good performance in Noise Fig. (NF), Linearity, Power consumption and Fig. of merit (FOM). Our analysis shows that Current-reuse LNA (CRLNA) achieve the best gain and FOM among all the other configurations. The FOM of CRLNA is 1.5507GHz/mW which is higher than others LNA configurations. Further, we proposed a novel endeavor in the form of an Artificial Neural Network (ANN) model which estimates different amplifier parameters based upon the simulated results, thereby, providing an alternative to the popular simulation tools which are based on complex analytical and mathematical models and are time consuming.