Massive MIMO with Artificial Intelligence for Interference Management

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 3, pp. 317–326, March 2026

https://doi.org/10.58482/ijersem.v2i3.41

This work is licensed under a Creative Commons Attribution 4.0 International License .

Massive MIMO with Artificial Intelligence for Interference Management

Kothapalle Bhaskar, Cherukula Jahnavi, Gobbilla Alekhya, Kuruba Dhanush, Akula chaithanya, M Akash

Department of ECE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract

Massive Multiple-Input Multiple-Output (Massive MIMO) is a key technology for fifth-generation (5G) and beyond wireless communication systems due to its ability to provide high spectral efficiency and improved network capacity. However, the performance of Massive MIMO systems is severely affected by interference, including inter-user interference, inter-cell interference, and pilot contamination, especially in dense network scenarios. Conventional interference mitigation techniques rely on fixed mathematical models and often fail to adapt to dynamic wireless environments. This paper presents an Artificial Intelligence (AI)-based approach for interference management in Massive MIMO systems. Machine learning techniques are employed to learn complex interference characteristics and to optimize channel estimation, beamforming, and resource allocation. Simulation results demonstrate that the proposed AI-enabled Massive MIMO framework achieves significant improvements in signal-to-interference-plus-noise ratio (SINR), system throughput, and error performance when compared to traditional methods. The results confirm that integrating artificial intelligence with Massive MIMO provides an effective and scalable solution for interference management in next-generation wireless networks.

Keywords: Massive MIMO, Artificial Intelligence, Interference Management, Deep Neural Networks, Beamforming Design.

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DOI: https://doi.org/10.58482/ijersem.v2i3.41

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Issue 3 (March 2026)

2026-03-31