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下行链路无蜂窝大规模 MIMO 系统中混合 DAC 的分析与优化。

Analysis and Optimization for Downlink Cell-Free Massive MIMO System with Mixed DACs.

机构信息

The Department of Wireless Communication Key Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.

出版信息

Sensors (Basel). 2021 Apr 8;21(8):2624. doi: 10.3390/s21082624.

DOI:10.3390/s21082624
PMID:33918009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8068372/
Abstract

This paper concentrates on the rate analysis and optimization for a downlink cell-free massive multi-input multi-output (MIMO) system with mixed digital-to-analog converters (DACs), where some of the access points (APs) use perfect-resolution DACs, while the others exploit low-resolution DACs to reduce hardware cost and power consumption. By using the additive quantization noise model (AQNM) and conjugate beamforming receiver, a tight closed-form rate expression is derived based on the standard minimum mean square error (MMSE) channel estimate technique. With the derived result, the effects of the number of APs, the downlink transmitted power, the number of DAC bits, and the proportion of the perfect DACs in the mixed-DAC architecture are conducted. We find that the achievable sum rate can be improved by increasing the proportion of the perfect DACs and deploying more APs. Besides, when the DAC resolution arrives at 5-bit, the system performance will invariably approach the case of perfect DACs, which indicates that we can use 5-bit DACs to substitute the perfect DACs. Thus, it can greatly reduce system hardware cost and power consumption. Finally, the weighted max-min power allocation scheme is proposed to guarantee that the users with high priority have a higher rate, while the others are served with the same rate. The simulation results prove the proposed scheme can be effectively solved by the bisection algorithm.

摘要

本文专注于分析和优化具有混合数模转换器(DAC)的下行链路无蜂窝大规模多输入多输出(MIMO)系统的速率,其中一些接入点(AP)使用完美分辨率 DAC,而其他则利用低分辨率 DAC 来降低硬件成本和功耗。通过使用加性量化噪声模型(AQNM)和共轭波束形成接收机,基于标准最小均方误差(MMSE)信道估计技术,推导出了一个紧密的闭式速率表达式。利用推导的结果,研究了 AP 的数量、下行传输功率、DAC 位数以及混合-DAC 架构中完美 DAC 的比例对系统的影响。结果表明,通过增加完美 DAC 的比例和部署更多的 AP,可以提高系统的总速率。此外,当 DAC 分辨率达到 5 位时,系统性能将始终接近完美 DAC 的情况,这表明我们可以使用 5 位 DAC 来替代完美 DAC。因此,可以大大降低系统的硬件成本和功耗。最后,提出了加权最大化最小功率分配方案,以保证优先级较高的用户具有更高的速率,而其他用户则具有相同的速率。仿真结果证明了二分算法可以有效地解决所提出的方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/d419e7559b54/sensors-21-02624-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/5a48e72e6ed6/sensors-21-02624-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/e38c5fd1da48/sensors-21-02624-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/a5ab2cddf605/sensors-21-02624-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/9312dafd7930/sensors-21-02624-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/26991f565c7d/sensors-21-02624-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/d419e7559b54/sensors-21-02624-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/5a48e72e6ed6/sensors-21-02624-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/e38c5fd1da48/sensors-21-02624-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/a5ab2cddf605/sensors-21-02624-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/9312dafd7930/sensors-21-02624-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/26991f565c7d/sensors-21-02624-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff60/8068372/d419e7559b54/sensors-21-02624-g006.jpg

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Massive MIMO Systems for 5G and Beyond Networks-Overview, Recent Trends, Challenges, and Future Research Direction.面向5G及未来网络的大规模MIMO系统——概述、最新趋势、挑战及未来研究方向
Sensors (Basel). 2020 May 12;20(10):2753. doi: 10.3390/s20102753.
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A Joint Symbol-Detection, Channel-Estimation and Decoding Scheme under Few-Bit ADCs in mmWave Communications.毫米波通信中基于少比特模数转换器的联合符号检测、信道估计与解码方案
Sensors (Basel). 2020 Mar 27;20(7):1857. doi: 10.3390/s20071857.