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.
翻译:在这项工作中,我们调查在基地站安装低成本和低功率1位模拟对数字转换器的大型多antenna系统的动态过度抽样技术。为了补偿粗微四分法造成的性能损失,在接收器中应用了过度抽样。与使用统一多抽样法、以恒定速率对信号进行取样的现有工程不同,我们提出了一个新的动态过度抽样方案。基本想法是进行时间变化式非单式多抽样,选择具有不同时间变化的非统一模式的样本。我们考虑了两种系统设计标准:一种设计,最大限度地实现可实现的总和率,另一种设计,最大限度地减少所测符号的平均平方错误。动态过度抽样使用一个尺寸减少矩阵 $\mathb=Delta}$ 进行,该矩阵可以由通用的乙价脱钩法或由新的子矩阵级特征选择算法进行计算。此外,拟议方案还从趋同、计算复杂度和在接收器中使用非统一电荷消费模式的样本的角度进行分析。模拟了可实现性能率的系统,并用这些可实现的模标度比值的系统,以拟议的可实现的精确度计算法计算。