Non-Uniform Spatial Sampling for Visibility Region Recognition in XL-MIMO
Extra-large massive multiple-input multiple-output(XL-MIMO)is a key technology for future 6G ultra wireless broadband and ul-tra large-scale connectivity.Its spatial non-stationary characteristic may result in the local antenna array region being only"visible"to some users,known as the user's visibility region(VR).Utilizing VR can achieve low complexity transmission design for XL-MIMO,while recogniz-ing user's VR is a necessary prerequisite.Due to the natural connection between user's VR and their spatial location,a small number of us-ers can be selected to estimate and provide feedback on the VR at their location,and then combined with the user's location to extrapolate the VR information of other users.This process can be explained as spatial sampling and reconstruction of"VR maps",and the extrapolation effect is closely related to the selection of sampling point positions.In order to improve sampling efficiency,a non-uniform spatial sampling scheme under limited samples is proposed based on the design concept of combining exploration and refinement,and the design method of exploration and refinement control factor in general scenarios is analyzed.The simulation results show that the proposed scheme has higher efficiency compared to traditional random sampling and can significantly improve the accuracy of VR recognition in small samples.
XL-MIMOspatial samplingvisibility region recognitionexploration and refinement