Power amplifier linearization technique with IQ imbalance and crosstalk compensation for broadband MIMO-OFDM transmitters

The design of predistortion techniques for broadband multiple input multiple output-OFDM (MIMO-OFDM) systems raises several implementation challenges. First, the large bandwidth of the OFDM signal requires the introduction of memory effects in the PD model. In addition, it is usual to consider an imbalanced in-phase and quadrature (IQ) modulator to translate the predistorted baseband signal to RF. Furthermore, the coupling effects, which occur when the MIMO paths are implemented in the same reduced size chipset, cannot be avoided in MIMO transceivers structures. This study proposes a MIMO-PD system that linearizes the power amplifier response and compensates nonlinear crosstalk and IQ imbalance effects for each branch of the multiantenna system. Efficient recursive algorithms are presented to estimate the complete MIMO-PD coefficients. The algorithms avoid the high computational complexity in previous solutions based on least squares estimation. The performance of the proposed MIMO-PD structure is validated by simulations using a two-transmitter antenna MIMO system. Error vector magnitude and adjacent channel power ratio are evaluated showing significant improvement compared with conventional MIMO-PD systems.Peer reviewe

Low-dimensional tracking of association structures in categorical data

In modern applications, such as text mining and signal processing, large amounts of categorical data are produced at a high rate and are characterized by association structures changing over time. Multiple correspondence analysis (MCA) is a well established dimension reduction method to explore the associations within a set of categorical variables. A critical step of the MCA algorithm is a singular value decomposition (SVD) or an eigenvalue decomposition (EVD) of a suitably transformed matrix. The high computational and memory requirements of ordinary SVD and EVD make their application impractical on massive or sequential data sets. Several enhanced SVD/EVD approaches have been recently introduced in an effort to overcome these issues. The aim of the present contribution is twofold: (1) to extend MCA to a split-apply-combine framework, that leads to an exact and parallel MCA implementation; (2) to allow for incremental updates (downdates) of existing MCA solutions, which lead to an approximate yet highly accurate solution. For this purpose, two incremental EVD and SVD approaches with desirable properties are revised and embedded in the context of MCA