Deep Neural Network Based Detection Algorithm for High-Order Modulation in Uplink Massive MIMO

被引：3

作者：

Hou, Huijun ^{[1
]}

Li, Lin ^{[2
,3
]}

Meng, Weixiao ^{[4
]}

机构：

[1] CETC, Res Inst 14, Nanjing, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Broadband Wireless Commun & Inten, Nanjing, Peoples R China

[3] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing, Peoples R China

[4] Harbin Inst Technol, Commun Res Ctr, Harbin, Peoples R China

来源：

IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) | 2021年

关键词：

massive MIMO; deep neural network; detection algorithm; high order modulation; computational complexity;

D O I：

10.1109/IWCMC51323.2021.9498882

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

For the 6th generation (6G) communications, the interference exploitation generated from a growing number of intelligent factors with ultra scale is great challenge for the low complexity detection algorithms in the uplink multiuser massive multiple input and multiple output (MIMO) systems, especially for detecting high order quadrature amplitude modulation (QAM) signals. The deep learning technology is one of the key technical solutions for 6G. In this paper, a deep neural network based semidefinite relaxation (DNNSR) detection algorithm is proposed on the basis of the graphical detection model for uplink multiuser massive MIMO systems. Compared with the counterpart detection algorithms, along with a low polynomial average per symbol computational complexity, the proposed DNNSR detection algorithm requires lower average received signal to noise ratios to obtain better bit error rate performance as well as achieve theoretical spectral efficiency for graphical high order QAM signals in the uplink multiuser massive MIMO systems.

引用

下载

页码：1326 / 1331

页数：6

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