Classification of STBC Systems Over Frequency-Selective Channels
House–time block code (STBC) classification algorithms have recently received growing attention in academia and business. Over and above their use in the context of military operations, these algorithms found civilian applications in reconfigurable systems, like software-defined and cognitive radios. The previously reported single-carrier-based mostly STBC classification algorithms are limited to frequency-flat fading channels; but, the wireless channels are sometimes frequency selective. This paper exploits the dispersive nature of the frequency-selective fading channels to classify Alamouti (AL) and spatial multiplexing (SM) STBCs over such channels. We show that the cross-correlation perform of two completely different received signals for AL exhibits peaks at a specific set of time lags, whereas that for SM does not. Furthermore, we develop a maximum-probability classification algorithm. This requires channel data, which may be unavailable in some eventualities like radio surroundings awareness in cognitive radios. To avoid this demand, we additionally propose a replacement classification algorithm based mostly on the false alarm rate. Monte Carlo simulations are conducted to demonstrate the performance of the proposed algorithms.
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