On 3-D harmonic retrieval for wireless channel sounding

Multidimensional harmonic retrieval (HR) problems often appear in the context of MIMO wireless channel sounding. In particular, for a double-directional parametric MIMO channel model with uniform linear transmit and receive arrays, and a fixed wireless scenario (static - no Doppler), fitting the channel model parameters amounts to a 3-D harmonic retrieval problem. For this latter problem, we develop two new algorithms. One is based on conjugate-folding of the 3-D data and reduction to an eigenvalue decomposition problem; the other on a 3-D version of the Rank Reduction Estimator (RARE) applied to a subspace extracted from a single data snapshot, using 3-D conjugate-folding. Both algorithms remain operative close to the best known model identifiability boundary. The two algorithms are compared via pertinent simulations.