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In this paper, the conditions for blind identifiability from second-order statistics (SOS) of multiple-input multiple-output (MIMO) channels under orthogonal space--time block coded (OSTBC) transmissions are studied. The main contribution of the paper consists in the proof that, assuming more than one receive antenna, any OSTBC with a transmission rate higher than a given threshold, which is inversely proportional to the number of transmit antennas, permits the blind identification of the MIMO channel from SOS. Additionally, it has been proven that any real OSTBC with an odd number of transmit antennas is identifiable, and that any OSTBC transmitting an odd number of real symbols permits the blind identification of the MIMO channel regardless of the number of receive antennas, which extends previous identifiability results and suggests that any nonidentifiable OSTBC can be made identifiable by slightly reducing its code rate. The implications of these theoretical results include the explanation of previous simulation examples and, from a practical point of view, they show that the only nonidentifiable OSTBCs with practical interest are the Alamouti codes and the real square orthogonal design with four transmit antennas. Simulation examples and further discussion are also provided. Index Terms--Blind identifiability, multiple-input multipleoutput (MIMO) communications, orthogonal space-time block codes (OSTBC), second-order statistics (SOS).

A new deterministic technique is proposed for blind equalization of finite impulse response multiple-input multiple-output (FIR-MIMO) channels. The batch and adaptive algorithms are obtained by reformulating canonical correlation analysis (CCA) as a set of coupled least squares (LS) regression problems.

A new approach is presented for adaptive beamforming that has provided increased robustness against the mismatch problem as well as additional control over the sidelobe level. The computer simulations have shown an improved performance of the support vector machine (SVM)-based beamformer when compared with other robust beamforming methods.

A new criterion is described for effective channel order detection of single-input multiple-output (SIMO) channels. The straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate signal-to-noise ratios (SNRs), while the proposed method works with small data sets, colored signals and channels with small head and tail taps.

Spectrum sensing is a key component of the cognitive radio paradigm. Primary signals are typically detected with uncalibrated receivers at signal-to-noise ratios (SNRs) well below decodability levels. Multiantenna detectors exploit spatial independence of receiver thermal noise to boost detection performance and robustness. We study the problem of detecting a Gaussian signal with rank-P unknown spatial covariance matrix in spatially uncorrelated Gaussian noise with unknown covariance using multiple antennas. The generalized likelihood ratio test (GLRT) is derived for two scenarios. In the first one, the noises at all antennas are assumed to have the same (unknown) variance, whereas in the second, a generic diagonal noise covariance matrix is allowed in order to accommodate calibration uncertainties in the different antenna frontends. In the latter case, the GLRT statistic must be obtained numerically, for which an efficient method is presented. Furthermore, for asymptotically low SNR, it is shown that the GLRT does admit a closed form, and the resulting detector performs well in practice. Extensions are presented in order to account for unknown temporal correlation in both signal and noise, as well as frequency-selective channels. [ABSTRACT FROM PUBLISHER]