High-dimensional Channel Estimation for Simultaneous Localization and Communications

Simultaneous localization and communication (SLAC) is a desirable feature of 5G and beyond 5G wireless networks. To be able to implement SLAC, efficient high dimensional channel estimation methods are critical. This work presents a low-complexity multidimensional channel parameter estimation via rotational invariance techniques (MD-ESPRIT). We use both the spatial smoothing and forward-backward averaging techniques to further explore data samples to extract multipath components (MPCs). We propose a one-dimensional Fast-Fourier-Transform- (FFT) and inverse-FFT-based approach to obtain the signal subspaces for angular frequency estimation. The geometry relationship between MPCs and positions is utilized for simultaneous positioning and mapping. Numerical results demonstrate the improved identifiability and low complexity performance of the proposed scheme.