Efficient Second-Order Statistics-based Channel Estimation Algorithms for MC-CDMA Systems Using Transmit Diversity

Blind channel estimation algorithms for the downlink of Space-Frequency Block Coded Multi-Carrier Code Division Multiple Access (SFBC MC-CDMA) schemes are presented. We first formulate two channel estimation methods based on the Second-Order Statistics (SOS) of the frequency-domain received signal, namely: (i) a Minimum Variance (MV) criterion, and (ii) a subspace-based approach. Then, we highlight the unique structure of the input covariance matrix. The matrix structure allows us to propose modifications of the presented algorithms that are computationally efficient and offer enhanced performance in practical cases where only an estimate of the received signal covariance matrix is available. Moreover, we address the issue of channel identifiability by investigating the necessary and sufficient conditions under which the channel estimates are unique (within a complex scalar).