Machine learning with A MIMO Antenna for Wireless Communication Systems

Abstract

Multiple-input multiple-output (MIMO) antenna is a technology that uses multiple antennas at both the transmitter and receiver to improve the performance of wireless communication systems. One of the challenges in designing MIMO antennas is to reduce the complexity of the antenna array, while still maintaining good performance. One way to reduce complexity is to use shared antennas. Shared antennas are antennas that are used by multiple elements of the MIMO array. This can reduce the number of required antennas and simplify the design of the antenna array. The B-shape of the MIMO antenna by using CST program can also be used to reduce complexity. For example, hexagonal MIMO antennas have been shown to have good performance and can be implemented with a simple design. Additionally, the use of defected ground planes (DGPs) can be used to improve the isolation between the elements of a MIMO antenna array, which can also reduce complexity. The design of MIMO antennas is a complex and challenging task. However, by using shared antennas and other design techniques and AI, it is possible to reduce the complexity of MIMO antennas while still maintaining good performance.