Separating spatially correlated signals using first-order precoders

This paper deals with blind separation of spatially correlated signals mixed by an instantaneous system. Taking advantage of the fact that the source signals are accessible in some man-made systems such as wireless communication systems, we preprocess the source signals in transmitters by a set of properly designed first-order precoders and then the coded signals are transmitted. At the receiving side, information about the precoders are utilized to perform signal separation. Compared with the existing precoder-based methods, the new method only employs the simplest first-order precoders, which reduces the delay in data transmission and is easier to implement in practical applications.

Application of extended multivariate modeling for information flow analysis of event related responses

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

Blind channel estimation and signal retrieving for MIMO relay systems

In this paper, we propose a blind channel estimation and signal retrieving algorithm for two-hop multiple-input multiple-output (MIMO) relay systems. This new algorithm integrates two blind source separation (BSS) methods to estimate the individual channel state information (CSI) of the source-relay and relay-destination links. In particular, a first-order Z-domain precoding technique is developed for the blind estimation of the relay-destination channel matrix, where the signals received at the relay node are pre-processed by a set of precoders before being transmitted to the destination node. With the estimated signals at the relay node, we propose an algorithm based on the constant modulus and signal mutual information properties to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, the proposed algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.