2021/02/27 by Zhichao Shao, Z. Shao, Shao, Z. +6
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Information Theory (cs.IT) #Signal Processing (eess.SP) #cs.IT #eess.SP #electronic engineering #information engineering #math.IT
paper · pdf · doi:10.48550/arxiv.2103.00103
11 figures, 12 pages
arxiv created 2021/02/27 · openalex publication_date 2021/02/27 · arxiv updated 2021/03/02 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28
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 \mathbfΔ, 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.