On the Design of Quantization Functions for Uplink Massive MIMO with Low-Resolution ADCs

Lifu Liu, Songyan Xue, Yi Ma, N. Yi, R. Tafazolli
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引用次数: 3

Abstract

Quantization is the characterization of analogue-to-digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.
基于低分辨率adc的上行海量MIMO量化功能设计
量化是大规模MIMO系统中模数转换器(ADC)的表征。量化函数或量化阈值的设计与量化步长有关,而量化步长是适应发射功率和噪声方差变化的因素。为了利用低分辨率ADC来降低大规模MIMO的成本,我们提出了一种有必要具有自适应阈值量化函数的想法。研究发现,当采用最大似然(maximum-likelihood, ML)作为检测方法时,对低分辨率adc设定量化阈值不会造成明显的性能损失。此外,这种固定阈值量化功能不需要任何信号功率信息,可以降低adc的硬件成本。本文对固定阈值量化和自适应阈值量化在不同因素下进行了仿真比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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