5G Wireless Channel Estimation: Addressing PHY-Layer Impairments through Model-Based Deep Learning

This paper presents a novel methodology for wireless channel estimation in millimeter wave (mmWave) bands, focusing on addressing multiple PHY-layer impairments, including phase noise (PN), in-phase and quadrature-phase imbalance (IQI), carrier frequency offset (CFO), and power amplifier non-linearity (PAN). The principal contribution lies in the proposed approach to training a convolutional neural network (CNN) using a synthetic and labeled dataset that encompasses various wireless channel conditions. The methodology involves the synthetic generation of labeled datasets representing different wireless channel types, which are then utilized in the training stage of a CNN. The resulting model-based trained CNN demonstrates the capability to operate in diverse operational scenarios, showcasing its adaptability to various channel conditions. The experimental results highlight the superiority of the proposed channel estimation methodology across different signal-to-noise ratio (SNR) regions and delay spread channel types. The trained CNN exhibits robust performance, confirming its effectiveness in mitigating the impact of PHY-layer impairments in mmWave communication environments. This research contributes to advancing reliable channel estimation techniques for mmWave systems, with potential applications in next-generation wireless communication networks.