Dynamic Oversampling Tecniques for 1-Bit ADCs in Large-Scale MIMO Systems

TL;DR

This paper introduces a novel dynamic nonuniform oversampling method for large-scale MIMO systems with 1-bit ADCs, improving performance over uniform oversampling by optimizing sampling patterns based on system criteria.

Contribution

It proposes a time-varying nonuniform oversampling scheme and algorithms for designing the sampling pattern, enhancing sum rate and error performance in 1-bit ADC MIMO systems.

Findings

Dynamic oversampling outperforms uniform oversampling in simulations.

The scheme reduces computational cost and power consumption.

Performance improvements include higher sum rate and lower symbol error rate.

Abstract

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.