Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is e...

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Vydané v:IEEE transactions on wireless communications Ročník 20; číslo 6; s. 3710 - 3722
Hlavní autori: Vu, Thang X., Chatzinotas, Symeon, Nguyen, Van-Dinh, Hoang, Dinh Thai, Nguyen, Diep N., Renzo, Marco Di, Ottersten, Bjorn
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Algorithms
antenna selection
Antennas
Artificial neural networks
Combinatorial analysis
Complexity
Convexity
Electronics
Engineering Sciences
Iterative algorithms
Iterative methods
Machine learning
Machine learning algorithms
Multiuser
neural networks
Optimization
Precoding
Radio frequency
Real time operation
Receiving antennas
Spatial diversity
successive convex optimization
System effectiveness
Training
Transmitting antennas
ISSN:1536-1276, 1558-2248, 1558-2248
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Popis
Shrnutí:We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> RF chains and <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> antennas, where <inline-formula> <tex-math notation="LaTeX">M < N </tex-math></inline-formula>. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.
Bibliografia:ObjectType-Article-1
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ISSN:1536-1276
1558-2248
1558-2248
DOI:10.1109/TWC.2021.3052973