Abstract
In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.
Original language | English |
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Pages (from-to) | 7279-7293 |
Number of pages | 15 |
Journal | IEEE Transactions on Communications |
Volume | 70 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2022 Nov 1 |
Bibliographical note
Publisher Copyright:© 1972-2012 IEEE.
Keywords
- Deep learning
- MU-MIMO
- limited feedback
- precoder
ASJC Scopus subject areas
- Electrical and Electronic Engineering