Optimization by Vector Space Methods
Optimization by Vector Space Methods
Blind channel identification based on second-order statistics: a frequency-domain approach
IEEE Transactions on Information Theory
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A blind sequence detection algorithm based on the innovations approach is proposed and its performance in a slow and flat Rayleigh fading environment is evaluated. For a blind sequence detection algorithm, the main goal is to estimate the source correlation from the observed received samples. The Viterbi algorithm is then used to detect the input symbols. The proposed detector is a blind algorithm since it does not require any channel parameter information in order to detect the input symbols. A comparison between the innovations and the singular value decomposition (SVD) [14] based blind sequence detection algorithm is also presented. Our study considers only linearly modulated signals which are useful for diversity receivers. Simulation results show that the innovations blind sequence (BSD) detector has about a 3 dB gain over the SVD BSD in the fading model studied in this paper. Various performance measures will be presented to explain the behaviour of these BSDs in both the additive white Gaussian noise and fading environments.