  In this work, we investigate dynamic oversampling techniques for large-scale
multiple-antenna systems equipped with low-cost and low-power 1-bit
analog-to-digital converters at the base stations. To compensate for the
performance loss caused by the coarse quantization, oversampling is applied at
the receiver. Unlike existing works that use uniform oversampling, which
samples the signal at a constant rate, a novel dynamic oversampling scheme is
proposed. The basic idea is to perform time-varying nonuniform oversampling,
which selects samples with nonuniform patterns that vary over time. We consider
two system design criteria: a design that maximizes the achievable sum rate and
another design that minimizes the mean square error of detected symbols.
Dynamic oversampling is carried out using a dimension reduction matrix
$\mathbf{\Delta}$, which can be computed by the generalized eigenvalue
decomposition or by novel submatrix-level feature selection algorithms.
Moreover, the proposed scheme is analyzed in terms of convergence,
computational complexity and power consumption at the receiver. Simulations
show that systems with the proposed dynamic oversampling outperform those with
uniform oversampling in terms of computational cost, achievable sum rate and
symbol error rate performance.
