부산대학교 도서관

Massive Multiple Input Multiple Output (MIMO) technology has a lot of potential for meeting wireless communication systems' growing need for large data rates. This work presents a novel decoder design that takes advantage of deep learning techniques and is tailored for Massive-MIMO systems. The conventional decoding methods encounter challenges in handling the massive scale of antennas and complex interference scenarios. To address these limitations, we propose an innovative decoder architecture that integrates deep learning models for enhanced signal processing and decoding accuracy. By employing neural network-based decoding strategies, our proposed system demonstrates significant improvements in decoding performance, achieving remarkable gains in spectral efficiency and error correction capabilities. Extensive simulations and comparative analyses validate the efficacy of the proposed deep learning-driven decoder, showcasing its adaptability and superior performance in real-world Massive-MIMO environments.