دسته بندی:
اینترنت اشیاء - Internet of Things
سال انتشار:
2022
عنوان انگلیسی مقاله:
Deep learning-based transceiver design for multi-user MIMO systems
ترجمه فارسی عنوان مقاله:
طراحی فرستنده گیرنده مبتنی بر یادگیری عمیق برای سیستم های MIMO چند کاربره
منبع:
ScienceDirect- Elsevier- Internet of Things, 19 (2022) 100512: doi:10:1016/j:iot:2022:100512
نویسنده:
Tong Zhang
چکیده انگلیسی:
Multi-user multiple-input multiple-output (MIMO) is a key technique to increase both the
channel capacity and the number of users that can be served simultaneously. One of the main
challenges related to the deployment of such systems is the complexity of the transceiver
processing. Although the conventional optimization algorithms are able to provide excellent
performance, they generally require considerable computational complexity, which gets in the
way of their practical application in real-time systems. In contrast to existing work, we study
a DL-based transceiver design scheme for a downlink MIMO broadcasting channel (MIMO BC)
system, which consists of a base station (BS) serving multi-users. The objective of this work
is to maximize the sum-rate of all users by jointly optimizing the transmitter and receivers
under the total power constraint, while suppressing interference as much as possible. Due to
the inter-user interference in such system, the considered problem is nonconvex and NP-hard.
Different from traditional optimization algorithms, we rely on the convolutional neural networks
(CNNs) to optimize the transceivers in an adaptive way. In the proposed scheme, we develop an
unsupervised learning strategy, where a loss function is constructed innovatively for reducing
the inter-user interference. Simulation results show that the inter-user interference is reduced
effectively by our proposed CNN-based transceiver optimization method.
Keywords: Transceiver design | MIMO BC | Deep learning | Convolutional neural networks
قیمت: رایگان
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