An Effective Interference Cancellation System for Mimo – Noma Using Deep Learning Mechanism

The growing demand for faster data speeds, better spectral efficiency, and the ability to connect many devices simultaneously has pushed wireless communication technologies to new limits. Massive MIMO and Non-Orthogonal Multiple Access (NOMA) are two key innovations that address these challenges. However, combining these technologies also creates significant interference, which makes conventional detection methods like MRC SIC and MMSE SIC less effective, especially when there’s uncertainty in channel information. This paper introduces a new approach that uses Deep Learning to tackle these issues. By integrating a Deep Neural Network (DNN) into the interference cancellation process, the system enhances signal detection and effectively reduces interference between users. The DNN is trained to handle real world challenges, such as imperfect channel estimates, and is designed to work even in situations where the number of users exceeds the number of antennas. Simulation results show that this deep learning-based solution significantly improves performance, reducing outage probabilities by up to 35% compared to conventional methods. This makes it a reliable and scalable option for the next generation of wireless communication systems.