DocumentCode
1656602
Title
Semi-blind signal separation and channel estimation in MIMO communication systems by tensor factorization
Author
Abadi, Bahador Makki ; Sarrafzadeh, Amir ; Jarchi, Delaram ; Abolghasemi, Vahid ; Sanei, Saeid
Author_Institution
Centre of Digital Signal Process., Cardiff Univ., Cardiff, UK
fYear
2009
Firstpage
305
Lastpage
308
Abstract
In this paper, we introduce a tensor-factorization method for signal detection in MIMO applications. We address the detection problem through a 3-way tensor analysis. We represent the 4times4 MIMO received signals as a third-order tensor with modes: receiver antennas, user data symbols at each packet, and finally number of packets. Then, we demonstrate that by multi-way analysis using PARAFAC2 we can successfully solve the blind MIMO signal detection problem. In order to solve the permutation and scaling ambiguities of the detected signals we used different M-Sequence training symbols are used. For evaluating the method we compared our BER results with those of MMSE-VBLAST signal detection method.
Keywords
MIMO communication; antenna arrays; blind source separation; channel estimation; m-sequences; signal detection; tensors; 3-way tensor analysis; M-sequence training symbols; MIMO communication systems; MMSE-VBLAST signal detection; PARAFAC2; channel estimation; multi-way analysis; receiver antennas; semi-blind signal separation; tensor factorization; user data symbols; Channel estimation; Fading; MIMO; Multiaccess communication; Receiving antennas; Signal detection; Source separation; Tensile stress; Transmitters; Transmitting antennas; Fading Channel Estimation; M-Sequence; MIMO Detection; MMSE-VBLAST; PARAFAC2; Semi-Blind Source Separation; Tensor Factorization;
fLanguage
English
Publisher
ieee
Conference_Titel
Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Conference_Location
Cardiff
Print_ISBN
978-1-4244-2709-3
Electronic_ISBN
978-1-4244-2711-6
Type
conf
DOI
10.1109/SSP.2009.5278577
Filename
5278577
Link To Document