8,330 research outputs found
Joint data detection and channel estimation for OFDM systems
We develop new blind and semi-blind data detectors and channel estimators for orthogonal frequency-division multiplexing (OFDM) systems. Our data detectors require minimizing a complex, integer quadratic form in the data vector. The semi-blind detector uses both channel correlation and noise variance. The quadratic for the blind detector suffers from rank deficiency; for this, we give a low-complexity solution. Avoiding a computationally prohibitive exhaustive search, we solve our data detectors using sphere decoding (SD) and V-BLAST and provide simple adaptations of the SD algorithm. We consider how the blind detector performs under mismatch, generalize the basic data detectors to nonunitary constellations, and extend them to systems with pilots and virtual carriers. Simulations show that our data detectors perform well
Technical Report: Compressive Temporal Higher Order Cyclostationary Statistics
The application of nonlinear transformations to a cyclostationary signal for
the purpose of revealing hidden periodicities has proven to be useful for
applications requiring signal selectivity and noise tolerance. The fact that
the hidden periodicities, referred to as cyclic moments, are often compressible
in the Fourier domain motivates the use of compressive sensing (CS) as an
efficient acquisition protocol for capturing such signals. In this work, we
consider the class of Temporal Higher Order Cyclostationary Statistics (THOCS)
estimators when CS is used to acquire the cyclostationary signal assuming
compressible cyclic moments in the Fourier domain. We develop a theoretical
framework for estimating THOCS using the low-rate nonuniform sampling protocol
from CS and illustrate the performance of this framework using simulated data
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