Blind identification of out-of-cell users in DS-CDMA
EURASIP Journal on Applied Signal Processing
Multidimensional rank reduction estimator for parametric MIMO channel models
EURASIP Journal on Applied Signal Processing
Spectrum sharing in wireless networks via QoS-aware secondary multicast beamforming
IEEE Transactions on Signal Processing
Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar
IEEE Transactions on Signal Processing
| Hi-index | 35.69 |
Two-dimensional (2-D) and, more generally, multidimensional harmonic retrieval is of interest in a variety of applications, including transmitter localization and joint time and frequency offset estimation in wireless communications. The associated identifiability problem is key in understanding the fundamental limitations of parametric methods in terms of the number of harmonics that can be resolved for a given sample size. Consider a mixture of 2-D exponentials, each parameterized by amplitude, phase, and decay rate plus frequency in each dimension. Suppose that I equispaced samples are taken along one dimension and, likewise, J along the other dimension. We prove that if the number of exponentials is less than or equal to roughly IJ/4, then, assuming sampling at the Nyquist rate or above, the parameterization is almost surely identifiable. This is significant because the best previously known achievable bound was roughly (I+J)/2. For example, consider I=J=32; our result yields 256 versus 32 identifiable exponentials. We also generalize the result to N dimensions, proving that the number of exponentials that can be resolved is proportional to total sample size