Signal Processing Methods in MIMO Communication Systems

Abstract

This paper delves into the latest advancements and hurdles in Multi-Input Multi-Output (MIMO) technology, particularly in the realm of 5G and future networks. Initially, it examines the fusion of Non-Orthogonal Multiple Access (NOMA) with MIMO, emphasizing its crucial role in user grouping, beam steering, and power distribution strategies. Subsequently, the research explores progress in massive MIMO systems, concentrating on signal processing methods that improve spectral efficiency and system performance in sparse scattering contexts. The paper also reviews sophisticated signal processing approaches for millimeter-wave (mmWave) MIMO systems, with a particular focus on techniques like compressed sensing, which tackle the challenges associated with high-frequency bands and extensive antenna arrays. Furthermore, it investigates MIMO signal processing within Mode-Division Multiplexing (MDM) systems, suggesting an adaptive frequency-domain equalization combined with chromatic dispersion compensation to enhance performance. The discussion extends to Extremely Large-Scale MIMO (XL-MIMO) technology for 6G networks, highlighting the significance of deep learning in signal processing tasks. In conclusion, the paper summarizes the benefits of MIMO-Orthogonal Frequency Division Multiplexing (OFDM) systems, emphasizing their pivotal role in shaping the future landscape of wireless communications.