Enhancing mmWave Channel Estimation: A Practical Experimentation Approach With Modeled Physical Layer Impairments Incorporated in Deep Learning Training

This paper introduces a novel methodology for wireless channel estimation in millimeter-wave (mmWave) bands, with a primary focus on addressing diverse physical (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 key contribution centers around the innovative approach of training a convolutional neural network (CNN) using a synthetic and labeled dataset that encompasses a wide range of wireless channel conditions. The methodology involves the synthetic generation of labeled datasets, representing various types of wireless channels and PHY-layer impairments, which are subsequently employed in the CNN training stage. The resulting model-based trained CNN demonstrates exceptional adaptability to diverse operational scenarios, showcasing its capability to operate effectively under various channel conditions. To validate the efficacy of the proposed methodology, the trained CNN is deployed in a practical wireless testbed. Experimental results underscore 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 real-world mmWave communication environments. This research not only advances reliable channel estimation techniques for mmWave systems but also provides valuable practical assessment results, with potential applications in next-generation wireless communication networks.