JOINT MITIGATION OF ANALOG IMPERFECTIONS IN MIMO (MULTIPLE INPUT MULTIPLE OUTPUT) SYSTEM

Abstract

Multiple-Input Multiple-Output (MIMO) technology has become integral to advancing wireless communication systems, facilitating improved connectivity, accelerated data rates, and enriched user experiences. As the evolution of 5G New Radio (NR) networks continues, MIMO emerges as a critical component essential for meeting the requirements of next-generation communication technologies. Leveraging the vast bandwidths available in mm-wave bands and optimizing spatial degrees of freedom through sophisticated multiple antenna arrays are pivotal aspects of the new 5G NR network design. However, the operational feasibility of Massive MIMO in practical scenarios necessitates addressing challenges such as hardware imperfections and nonlinear distortions. In tandem with contemporary wireless technologies, Digital Predistortion (DPD) has emerged as a widely adopted technique to mitigate the nonlinear distortions introduced by transmitters and RF power amplifiers (PAs). DPD enhances linearization performance and offers lower complexity in diverse application scenarios. Despite the promise of DPD, existing models encounter limitations in MIMO systems, including escalating computational complexity. This thesis systematically examines the limitations of existing MIMO-DPD models in the context of MIMO systems and proposes novel solutions to overcome the computational complexity and enhance the numerical stability of the MIMO-DPD algorithms in such environments. One key focus of this research is the development of efficient MIMO-DPD algorithms tailored to enhance computational efficiency while offering effective linearization solutions. These algorithms are designed to mitigate the adverse effects of nonlinear distortions introduced by transmitters and RF power amplifiers, improving overall system performance. The research also proposes the linearization techniques to mitigate Self-interference in Full duplex system in SISO and MIMO. Moreover, the research endeavors to devise novel linearization techniques tailored to combat self-interference in full-duplex systems, spanning both Single-Input, Single- Output (SISO) and MIMO configurations. Additionally, innovative methods are proposed to address pervasive issues such as DC offset, IQ imbalance, and crosstalk, thereby priming the system for optimal DPD performance. Innovative approaches such as the Cross-Interference Reduction Digital Predistortion (CIR-DPD) method are introduced to specifically address challenges related to crosstalk in MIMO systems. By leveraging techniques such as Independent Component Analysis (ICA) and wavelet multi-scale PCA (WMSPCA), these methods aim to mitigate noise and interference, thereby enhancing system reliability.Furthermore, advancements in linearization techniques are explored to optimize the performance of communication systems. Emphasizing lower feedback bandwidth and effective crosstalk mitigation, these techniques offer practical solutions to improve numerical stability and reduce computational complexity. By effectively addressing hardware challenges and overcoming limitations posed by existing DPD models, these innovative techniques contribute to the evolution of wireless communication technologies. The research presented in this thesis offers theoretical insights and practical implementations aimed at enhancing the efficiency and effectiveness of MIMO systems. By meticulously examining the challenges and introducing novel solutions, this work seeks to pave the way for developing advanced linearization methods capable of meeting the demands of future wireless communication systems. As MIMO technology continues to evolve, the contributions of this research are poised to significantly impact the future landscape of wireless communication.