TY - GEN
T1 - Single sensor blind time-frequency activity estimation of a mixture of radio signals via CP tensor decomposition
AU - Mueller-Smith, Christopher
AU - Spasojevic, Predrag
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/13
Y1 - 2014/11/13
N2 - We consider reception of non-persistently excitated radio signals that overlap in time and frequency from a group of transmitters to a single receiver. The signals can be categorized as using a linear modulation or a non-linear modulation that can be approximated as a finite sum of linearly modulated signals. An analysis of a particular slice of the fourth-order cumulant spectra (trispectra) of this signal mixture reveals that the structure of their combined trispectrum can be modeled as a 3-dimensional tensor formed by a sum of rank 1 tensors corresponding to the trispectra of the component signals which fits the Canonical Decomposition/Parallel Factors (CP) tensor model. We develop an algorithm to decompose the trispectrum tensor which allows us to blindly estimate the power spectra, activity (in time) sequences, and number of signals contributing to an approximation of nonlinear signals. We then simulate the algorithm to verify results and quantify performance.
AB - We consider reception of non-persistently excitated radio signals that overlap in time and frequency from a group of transmitters to a single receiver. The signals can be categorized as using a linear modulation or a non-linear modulation that can be approximated as a finite sum of linearly modulated signals. An analysis of a particular slice of the fourth-order cumulant spectra (trispectra) of this signal mixture reveals that the structure of their combined trispectrum can be modeled as a 3-dimensional tensor formed by a sum of rank 1 tensors corresponding to the trispectra of the component signals which fits the Canonical Decomposition/Parallel Factors (CP) tensor model. We develop an algorithm to decompose the trispectrum tensor which allows us to blindly estimate the power spectra, activity (in time) sequences, and number of signals contributing to an approximation of nonlinear signals. We then simulate the algorithm to verify results and quantify performance.
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U2 - 10.1109/MILCOM.2014.109
DO - 10.1109/MILCOM.2014.109
M3 - Conference contribution
AN - SCOPUS:84912530453
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 617
EP - 622
BT - Proceedings - 2014 IEEE Military Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Annual IEEE Military Communications Conference, MILCOM 2014
Y2 - 6 October 2014 through 8 October 2014
ER -