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In this paper we employ a 2 × 2 Multiple‐Input Multiple‐Output (MIMO) hardware platform to evaluate, in realistic indoor scenarios, the performance of different space‐time block coded (STBC) transmissions at 2.4 GHz. In particular, we focus on the Alamouti orthogonal scheme considering two types of channel state information (CSI) estimation: a conventional pilot‐aided supervised technique and a recently proposed blind method based on second‐order statistics (SOS). For comparison purposes, we also evaluate the performance of a Differential (non‐coherent) space‐time block coding (DSTBC). DSTBC schemes have the advantage of not requiring CSI estimation but they incur in a 3 dB loss in performance. The hardware MIMO platform is based on high‐performance signal acquisition and generation boards, each one equipped with a 1 GB memory module that allows the transmission of extremely large data frames. Upconversion to RF is performed by two RF vector signal generators whereas downconversion is carried out with two custom circuits designed from commercial components. All the baseband signal processing is implemented off‐line in MATLAB®, making the MIMO testbed very flexible and easily reconfigurable. Using this platform we compare the performance of the described methods in line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) indoor scenarios. Copyright © 2007 John Wiley & Sons, Ltd.

SUMMARYThis paper focuses on blind channel estimation in Alamouti coded systems with one receiving antenna working in indoor scenarios where the flat fading assumption is reasonable. A comparative study of several channel estimation techniques in both simulated and realistic scenarios is presented. The tested methods exploit the orthogonality property of the Alamouti coded channel matrix, and are based on the eigendecomposition of a square matrix made up of second-order statistics (SOS) or higher order statistics (HOS) of the observed signals. An experimental evaluation is carried out on a testbed developed at the University of A Coruña (UDC) and operating at 2.4 GHz. The results show the superior performance of the SOS-based blind channel estimation technique in both line of sight (LOS) and non-LOS (NLOS) channels.

This paper presents a novel video coding technique where most frames are represented as their projection onto a proper basis (eigenspace) computed using Principal Component Analysis (PCA). Since a video sequence contains regions with high time variations, a learning procedure is used to obtain an adequate basis. We also introduce the idea of bidirectional predicted frames to denote those frames that can be estimated from the nearest past and future PCA coefficients. Experimental results show the high quality/compression achieved using the new scheme with different eigenspace updating algorithms.

The popular Alamouti orthogonal space time code attains full transmit diversity in multiple antenna systems. This paper addresses the problem of blind channel identification in (2 × 1) Alamouti coded systems. Under the assumption of independent symbol substreams, the channel can be estimated from the eigendecomposition of matrices composed of second-or higher-order statistics (cumulants) of the received signal. The so-called joint approximate diagonalization of eigenmatrices (JADE) method for blind source separation via independent component analysis is optimal in that it tries to simultaneously diagonalize a full set of fourth-order cumulant matrices. To reduce computational complexity, we perform the eigenvalue decomposition of a single cumulant matrix, which is judiciously chosen by maximizing its expected eigenvalue spread. Simulation results show that the resulting technique outperforms existing blind Alamouti channel estimation methods and achieves a performance close to JADE's at a fraction of the computational cost.