In wireless communication systems with antenna arrays the spatiotemporal channel can often be described by a low-rank model. By exploiting this information, corresponding low rank equalizers with reduced complexity can be designed. By applying such low rank equalizers to a set of uplink measurements, we demonstrate that the performance loss associated with the lower complexity is small.

We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume anm-dimensional latent state and n discrete observations, with a transition matrix of rank k ≤ m. This implies the dynamics evolve in ...

2 SUMMARY Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal components analysis and can therefore be carried out with standard statistical packages....

If two wide sense stationary time series are correlated then one can be used to predict the other. The reduced rank Wiener filter is the rank constrained linear operator which maps the current value of one time series to an estimate of the current value of the other time series in an optimal way. A closed form solution exists for the reduced rank Wiener filter. This paper studies the problem of...

We introduce a new dimension-reduction technique, the Partially Linear Reduced-rank Regression (PLRR) model, for exploring possible nonlinear structure in a regression involving both multivariate response and covariate. The PLRR model specifies that the response vector loads linearly on some linear indices of the covariate, and nonlinearly on some other indices of the covariate. We give a set o...