Optimization on Multiple-Input and Multiple-Output (MIMO) Network Affect Performance of an Radio Frequency (RF) in 6G

Abstract

In this article, we introduce the reconfigurable intelligent surfaces (RISs) restrict their general adoption, which employs digital pre-distortion, deep learning-based correction, and adaptive filtering to counteract real-time RF impairments. The technique is highly applicable to future 6G networks because it enhances MIMO performance by reducing BER, improving phase noise resilience, and achieving the highest spectral efficiency. Thermal noise, phase noise, and nonlinearity loss are RF impairments that significantly reduce the effectiveness of MIMO communication in 6G networks. Signal distortion, phase instability, and spectrum inefficiencies are the consequences of these impairments, which further increase BER and reduce capacity. A dynamic distortion mitigation framework is required because conventional compensating strategies cannot respond to new scenarios in real time. These approaches come with extra latency and power usage, making them less suitable for real-time use in 6G. However, there remains a challenge to using ML-based adaptive filtering on high-speed and low-power hardware, even though it has been at the forefront regarding dynamically compensating RF impairments. The cost and complexity of deployment of hybrid beamforming and reconfigurable intelligent surfaces (RISs) restrict their general adoption, yet they enhance MIMO performance in RF impairment. The basic challenge for smooth operation in 6G-enabled MIMO systems is to develop adaptive, low-power, and computationally efficient solutions.