RF-Chains Reduction by Beamspace MIMO to Achieve Near-Optimal Performance: An Overview

Due to the increase in demand for data traffic, high data rate, and low latency required for modern internet applications in 5G and beyond. Several techniques appeared to achieve the above requirements. The millimeter waves (mm-waves) and Multi-Input Multi-Output antennas (MIMO) are instances of these techniques. The problem is that these techniques are considered costly, power-hungry, and more complex than those used in previous generations of wireless communication systems. This article investigates the progress of solving these challenges and highlights one of the most successful techniques to achieve near-optimal performance with more economical manufacture, much less power consumption, and low complex hardware. This technique is the Beamspace MIMO with Lens-antenna. In addition, we discuss the use of Artificial Intelligence AI (Deep Learning and Neural Networks) to minimize time consumption and optimization complexity of the beamspace implementation. Furthermore, we discussed the work of researchers to improve Energy Efficiency (EE), Spectral Efficiency (SE), complexity, Channel Estimation (CE), Power Leakage, and sum rate. Finally, we introduce the most promising areas for future research in the beamspace concept.