Optimizing Millimeter Wave MIMO Channel Estimation Through GPU-Based Edge Artificial Intelligence

In the context of upcoming sixth-generation (6G) wireless communication systems, the use of millimeter wave (mmWave) frequencies is a key technology for achieving high-throughput communications. Accurate parametric estimation of mmWave channels is critical for effective beamforming design and configuration, requiring sophisticated models to capture the directional characteristics of these channels. This work considers an innovative artificial intelligence (AI) approach for accurate estimation of angle-of-arrival (AoA) and angle-of-departure (AoD) parameters from frequency-domain channel observations. Our approach is based on the implementation of two convolutional neural networks (CNNs): a residual CNN (ResNet) and a U-Net CNN. Specifically, this work focuses on the efficient implementation of both schemes in an embedded system suitable for edge AI. We performed the experiments in a low-power NVIDIA Jetson Orin Nano platform and evaluated the effect of modifying the frequencies of its CPU and GPU on the performance of the inference process, both in terms of execution time and energy consumption. Experimental results showed that the U-Net model is more power consuming, but as it is faster, it consumes less energy per channel.