Study and analysis for improving the efficiency in Massive MIMO systems- a deep learning based approach

This is the authors' abstract. We don't add key points for this paper.

The tremendous growth in wireless communication broadband use cases demands high data rates and the global bandwidth shortage leads to focus the study on Massive Multiple Input Multiple Output (mMIMO) for the deployment of 5G/6G networks-based applications. mMIMO extends the concept of MIMO by placing thousands of antennas at the base station, with an aim to improve both spectral and energy efficiency. While enhancing the number of antenna elements increases data rate at the same time increases the computational complexity and cost. The deep learning (DL) based approach can reduce the computational complexity leading to beyond fifth generation (B5G) networking epitome wherein machine learning fully controls network function and resource utilization optimization. The fundamental challenges like precoding, scheduling, pilot contamination, channel estimation, signal detection, spectral efficiency and energy, etc. need to be addressed. This article discusses some of the major challenges and mitigation techniques, focusing on the mMIMO, and suggesting the research methodologies for simulation and implementation aspects. From the studies, it is inferred that DL based solutions reduce the computational complexity for meeting the performance requirements in the future networks.