  The use of one-bit analog-to-digital converter (ADC) has been considered as a
viable alternative to high resolution counterparts in realizing and
commercializing massive multiple-input multiple-output (MIMO) systems. However,
the issue of discarding the amplitude information by one-bit quantizers has to
be compensated. Thus, carefully tailored methods need to be developed for
one-bit channel estimation and data detection as the conventional ones cannot
be used. To address these issues, the problems of one-bit channel estimation
and data detection for MIMO orthogonal frequency division multiplexing (OFDM)
system that operates over uncorrelated frequency selective channels are
investigated here. We first develop channel estimators that exploit Gaussian
discriminant analysis (GDA) classifier and approximated versions of it as the
so-called weak classifiers in an adaptive boosting (AdaBoost) approach.
Particularly, the combination of the approximated GDA classifiers with AdaBoost
offers the benefit of scalability with the linear order of computations, which
is critical in massive MIMO-OFDM systems. We then take advantage of the same
idea for proposing the data detectors. Numerical results validate the
efficiency of the proposed channel estimators and data detectors compared to
other methods. They show comparable/better performance to that of the
state-of-the-art methods, but require dramatically lower computational
complexities and run times.
