Deep Transfer Learning for Site-Specific Channel Estimation in Low-Resolution mmWave MIMO

被引：22

作者：

Alves, Wesin ^{[1
]}

Correa, Ilan ^{[1
]}

Gonzalez-Prelcic, Nuria ^{[2
]}

Klautau, Aldebaro ^{[1
]}

机构：

[1] Fed Univ Para, LASSE, 5G & IoT Res Grp, BR-66615170 Belem, Para, Brazil

[2] North Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27606 USA

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2021年 / 10卷 / 07期

关键词：

Deep learning; deep transfer learning; channel estimation; mmWave; quantized MIMO; MASSIVE MIMO; SYSTEMS;

D O I：

10.1109/LWC.2021.3069199

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We consider the problem of channel estimation in low-resolution multiple-input multiple-output (MIMO) systems operating at millimeter wave (mmWave) and present a deep transfer learning (DTL) approach that exploits previously trained models to speed up site adaptation. The proposed model is composed of a feature extractor and a regressor, with only the regressor requiring training for the new environment. The DTL approach is evaluated using two 3D scenarios where ray-tracing is performed to generate the mmWave MIMO channels used in the simulations. Under the defined testing setup, the proposed DTL approach can reduce the computational cost of the training stage without decreasing the estimation accuracy.

引用

页码：1424 / 1428

页数：5