Mixed Near-Field and Far-Field Source Localization Based on Exact Spatial Propagation Geometry

The large-scale multiple-input-multiple-output (MIMO), also known as massive MIMO, is one of the key techniques for the fifth-generation (5 G) mobile communications. Due to large-scale antenna systems equipped at the basestations, the user-basestation distance in massive MIMO systems may be within the so-called Rayleigh distance. This would cause challenges in developing algorithms for user localization, because both near-field (NF) and far-field (FF) sources (users) may coexist. To solve this problem, most of existing algorithms are based on a simplified source-sensor spatial model, where the sensor-magnitude is assumed to be equal and the spatial phase is approximated by the Taylor polynomial. In contrast, a new algorithm based on the exact spatial geometry is developed, where no model simplification is made. The new algorithm is termed as MIxed Localization using the Exact model (MILE) in that it sets up a unified (non-approximation) model framework to the problem under consideration, and solves this problem in a mathematically quite simple manner. In fact, the MILE has the following three important advantages: (1) it is not restricted to exploit equally spaced arrays, (2) it can accommodate any arbitrary propagation loss, and (3) it does not suffer the model mismatch caused performance loss. All these advantages are not offered by current state-of-the-art techniques. The matlab codes for replication of the results in this study are available at: https://github.com/jinhesjtu/MILE.git.