粗量化Massive MIMO上行链路误码率性能分析

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

摘要翻译：
作为massive MIMO和毫米波MIMO系统中降低功耗的一种解决方案，它具有较低的量化分辨率。本文分析了采用几位分辨率ADC的量化massive MIMO上行链路的误码率性能。在考虑迫零(ZF)检测的情况下，利用线性量化模型，推导了一个封闭形式的量化信干噪比(SINR)来实现粗量化M-QAM massive MIMO系统的解析误码率近似。所提出的表达式是以比特为单位的量化分辨率的函数。我们进一步数值研究了从1比特到4比特的不同量化水平对QPSK、16-QAM和64-QAM三种调制类型的误码率的影响。模拟中采用了均匀量化器和非均匀量化器。Monte Carlo仿真结果表明，我们的近似公式给出了使用非均匀量化器的$B$bit分辨率量化系统的误码率性能的一个严格的上限，而使用均匀量化器会导致相同系统的误码率性能下降。我们还发现在粗量化系统中，例如2-3比特QPSK和3-4比特16-QAM，与全精度（未量化）情况相比，误码率性能有较小的下降。然而，这种性能下降可以通过增加BS处的天线数量来补偿。
---
英文标题：
《BER Performance Analysis of Coarse Quantized Uplink Massive MIMO》
---
作者：
Azad Azizzadeh, Reza Mohammadkhani, Seyed Vahab Al-Din Makki, Emil
  Bj\"ornson
---
最新提交年份：
2018
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--

---
英文摘要：
  Having lower quantization resolution, has been introduced in the literature, as a solution to reduce the power consumption of massive MIMO and millimeter wave MIMO systems. In this paper, we analyze bit error rate (BER) performance of quantized uplink massive MIMO employing a few-bit resolution ADCs. Considering Zero-Forcing (ZF) detection, we derive a closed-form quantized signal-to-interference-plus-noise ratio (SINR) to achieve an analytical BER approximation for coarse quantized M-QAM massive MIMO systems, by using a linear quantization model. The proposed expression is a function of quantization resolution in bits. We further numerically investigate the effects of different quantization levels, from 1-bit to 4-bits, on the BER of three modulation types of QPSK, 16-QAM, and 64-QAM. Uniform and non-uniform quantizers are employed in our simulation.   Monte Carlo simulation results reveal that our approximate formula gives a tight upper bound for the BER performance of $b$-bit resolution quantized systems using non-uniform quantizers, whereas the use of uniform quantizers cause a lower performance for the same systems. We also found a small BER performance degradation in coarse quantized systems, for example 2-3 bits QPSK and 3-4 bits 16-QAM, compared to the full-precision (unquantized) case. However, this performance degradation can be compensated by increasing the number of antennas at the BS.
---
PDF链接：
https://arxiv.org/pdf/1711.09309

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：massive mass 性能分析 MIM mas uniform bits massive 信干 误码率