Enhanced Signal Quality and Spectrum Efficiency in MIMO Cognitive Radio Networks using Adaptive Non-linear Pre-Distortion Power Amplifier Linearization

Cognitive Radio Networks (CRNs) aim to optimize the limited frequency spectrum by enabling sharing among networks and utilizing unoccupied frequency bands. The combination of massive multiple-input multiple-output (mMIMO) and CRNs has the potential to improve the efficiency of upcoming wireless communication networks greatly. Modern communication networks require improved signal quality and spectrum efficiency. Measurement noise and crosstalk significantly impact data rates and Power Amplifiers (PAs) performance in MIMO systems. This paper introduces Adaptive Non-linear Pre-Distortion Power Amplifier Linearization (ANP-DPAL), a technique that uses a parameterized non-linear model to leverage PAs' inherent non-linearities. The adaptive pre-distortion module compensates for non-linear effects by making realtime adjustments to the input signals. ANP-DPAL employs adaptive filtering algorithms (AFA) to measure and correct for interference between channels to address crosstalk. The technique integrates accurate measurement noise estimation to enhance the linearization process further. ANP-DPAL's use of fuzzy controllers to adaptively alter predistortion parameters ensures system flexibility, improving signal quality in diverse communication contexts, including 5G, wireless local area networks, and satellite communications. Simulation studies demonstrate ANP-DPAL's effectiveness across various parameters, including signal linearity, Bit Error Rate (BER), and Error Vector Magnitude (EVM). Results show that ANP-DPAL significantly improves linearization, crosstalk reduction, and noise robustness, confirming its suitability for real-world MIMO communication networks.