Blind separation of communication signals invariably relies on some form(s) of diversity to overdetermine the problem and thereby recover the signals of interest. More often than not, linear (e.g., spreading) diversity is employed, i.e., each diversity branch provides a linear combination of the unknown signals, albeit with possibly unknown weights. If multiple forms of linear diversity are simultaneously available, then the resulting data exhibit multilinear structure, and the blind recovery problem can be shown to be tantamount to low-rank decomposition of the multi-dimensional received data array. This paper generalizes Kruskal's fundamental result on the uniqueness of low-rank decomposition of 3-way arrays to the case of multilinear decomposition of 4- and higher-way arrays. The result characterizes diversity combining for blind identifiability when N forms of linear diversity are available; that is the balance between different forms of diversity that guarantees blind recovery of all signals involved.
|Original language||English (US)|
|Title of host publication||CommunicationsSensor Array and Multichannel Signal Processing|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - 2000|
|Event||25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey|
Duration: Jun 5 2000 → Jun 9 2000
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000|
|Period||6/5/00 → 6/9/00|
Bibliographical noteFunding Information:
Supported by NSF/CAREER CCR-9733540, NSF/Wireless CCR-9979295. Supported by LMC (Center for Advanced Food Studies) and PMP (Center for Predictive Multivariate Process Analysis) through the Danish ministries of research and industry.
* Supported by NSFKAREER CCR-9733540, NSFNireless CCR-9979295. t Supported by LMC (Center for Advanced Food Studies) and PMP (Center for Predictive Multivariate Process Analysis) through the Danish ministries of research and industry.
© 2000 IEEE.