Optimized Trade-off Design of Gain and Noise Figure in LNAs for SDR-Based Compressed Spectrum Sensing

Abstract

This paper presents a comprehensive study on the design and validation of Low-Noise Amplifiers (LNAs) optimized for Software-Defined Radio (SDR)-based Cognitive Radio Networks (CRNs). Aimed at enhancing the Signal-to-Noise Ratio (SNR) and improving compressed sensing efficiency, we developed a MATLAB-based graphical user interface to facilitate LNA design. The GUI integrates analytical methods to calculate critical parameters, including available gain, reflection coefficients, and matching networks, ensuring accuracy through comparison with simulations in Advanced Design System software. The proposed method delivers a peak gain of 17.16 dB, representing an improvement of 3.71 dB, while maintaining a noise figure of 0.35 dB, which is only 0.13 dB higher than the minimum achievable value, demonstrating an optimized trade-off between gain and noise performance. Real-case LNA parameters were used to validate the design, with ADS simulations confirming a negligible deviation of 0.02 dB in gain. These results highlight the effectiveness of the proposed approach in improving the SNR (by 7 dB) and detection efficiency for SDR-based systems.