Enhancing 5G massive MIMO systems with EfficientNet-B7-powered deep learning-driven beamforming

Abstract

The development of wireless communication systems is a challenging and constantly evolving field and the issue of gaining optimal performance is of utmost importance. This work intends to give a thorough and detailed description of massive MIMO technology and its properties, with a significant emphasis on digital beamforming (FDB) and hybrid beamforming (HBF) techniques and the potential of combining them with the most recent and exciting frontier of research: deep learning. On one hand, FDB provides accurate signal control but, on the other hand, it deals with substantial needs like high-power consumption. This challenge makes the focus shift to HBF—the innovative technology successfully coupled with deep learning's powerful potential. The chosen research explores extensively the major areas of application and compatibility of this operating mode in a diverse range of operational situations in the interference environment as well as in different levels of noise conditions. Moreover, the study offers a comprehensive comparison, which is highly effective in exploring further methods that focus on improving spectral efficiency. Significantly, the “Proposed Method” is suggested to be at the leading position, which demonstrates superior performance. Showing outstanding generalization capability, versatile robustness, and efficiency of usage in the proposed framework rely on EfficientNet-B7 as the major portion. This makes it adaptive to its dynamic surroundings and puts it as a powerful tool in the world of advanced connectivity and massive MIMO technology. Due to its core ability to respond to changes in conditions effectively and efficiently, the proposed framework is seen as one of the most powerful approaches that could be used to change wireless communication systems.