Finite-length MIMO equalization using canonical correlation analysis

Finite-length MIMO equalization using canonical correlation analysis

We propose finite-length multi-input multi-output (MIMO) equalization methods for “smart” antenna arrays using the statistical theory of canonical correlations. We show that the proposed methods are related to maximum likelihood (ML) reduced-rank channel and noise estimation algorithms in unknown spatially correlated noise as well as to several recently developed equalization schemes.

Finite-length MIMO adaptive equalization using canonical correlations

We propose finite-length multi-input multi-output adaptive equalization methods for “smart” antenna arrays using the statistical theory of canonical correlations. We show that the proposed methods are related to maximum likelihood reduced-rank channel and noise estimation algorithms in unknown spatially correlated noise, and to several recently proposed adaptive equalization schemes.

Canonical correlation analysis using within-class coupling

0167-8655/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.patrec.2010.09.025 q The work of O. Kursun was supported by Scienti nation Unit of Istanbul University under the grant YA ⇑ Corresponding author. Tel.: +90 212 473 7070/17 E-mail addresses: [email protected] (O. Kurs Alpaydin), [email protected] (O.V. Favorov). Fisher’s linear discriminant analysis (LDA) is one of the most ...

Predicting Atmospheric Parameters using Canonical Correlation Analysis

Unsupervised speaker normalization using canonical correlation analysis

Conventional speaker-independent HMMs ignore the speaker di erences and collect speech data in an observation space. This causes a problem that the output probability distribution of the HMMs becomes vague so that it deteriorates the recognition accuracy. To solve this problem, we construct the speaker subspace for an individual speaker and correlate them by o-space canonical correlation analys...