On communication diversity for blind identifiability and the uniqueness of low-rank decomposition of N-way arrays

TY - GEN

T1 - On communication diversity for blind identifiability and the uniqueness of low-rank decomposition of N-way arrays

AU - Sidiropoulos, Nicholas D.

AU - Bro, Rasmus

PY - 2000/1/1

Y1 - 2000/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0033709216&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2000.860918

DO - 10.1109/ICASSP.2000.860918

M3 - Article in proceedings

AN - SCOPUS:0033709216

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 2449

EP - 2452

BT - CommunicationsSensor Array and Multichannel Signal Processing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000

Y2 - 5 June 2000 through 9 June 2000

ER -