Wideband Channel Estimation for mmWave MIMO Systems with Beam Squint

With the scale of antenna arrays and the bandwidth increasing, many existing narrowband channel estimation methods ignoring the effect of beam squint may face severe performance degradation in wideband millimeter-wave (mmWave) communication systems. In this letter, a wideband Newtonized orthogonal matching pursuit (wNOMP) algorithm has been proposed to perform channel estimation. The proposed method based on the minimum mean square error (MMSE) criterion is optimal for Gaussian noise. Considering real communication systems, it is common that the noise follows a non-Gaussian distribution. Accordingly we extend the wideband channel estimation method via the minimum $\ell_p$-norm criterion which enhances the robustness against the non-Gaussian noise. Simulations have been conducted to validate the superiority of the proposed method over other representative methods.

MMRotate: A Rotated Object Detection Benchmark using Pytorch

We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. To facilitate future research and industrial applications of rotated object detection-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection. MMRotate is publicly released at https://github.com/open-mmlab/mmrotate.