Beyond 5G: Leveraging Cell Free TDD Massive MIMO Using Cascaded Deep Learning

被引：22

作者：

Athreya, Navaneet ^{[1
]}

Raj, Vishnu ^{[2
]}

Kalyani, Sheetal ^{[2
]}

机构：

[1] Virginia Tech, Dept Elect & Comp Engn, Blacksburg, VA 24061 USA

[2] IIT Madras, Dept Elect Engn, Chennai 600036, Tamil Nadu, India

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2020年 / 9卷 / 09期

关键词：

Uplink; Downlink; Calibration; MIMO communication; Channel estimation; OFDM; Radio frequency; Cell free massive MIMO; deep learning; channel reciprocity; CHANNEL ESTIMATION; RECIPROCITY CALIBRATION;

D O I：

10.1109/LWC.2020.2996745

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter deals with the calibration of Time Division Duplexing (TDD) reciprocity in an Orthogonal Frequency Division Multiplexing (OFDM) based Cell Free Massive MIMO system where the responses of the (Radio Frequency) RF chains render the end to end channel non-reciprocal, even though the physical wireless channel is reciprocal. We further address the non-availability of the uplink channel estimates at locations other than pilot subcarriers and propose a single-shot solution to estimate the downlink channel at all subcarriers from the uplink channel at selected pilot subcarriers. We propose a cascade of two Deep Neural Networks (DNN) to achieve the objective. The proposed method is easily scalable and removes the need for relative reciprocity calibration based on the cooperation of antennas, which usually introduces dependency in Cell Free Massive MIMO systems.

引用

页码：1533 / 1537

页数：5

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