JCNN-Based DD Hybrid Precoding Algorithm With TPSAS Structure for FD-MIMO Systems

This letter presents a joint convolutional neural network (JCNN)-based double dimensional (DD) hybrid precoding algorithm for adaptive fully-connected subarray structure with two-layer shared phase shifters (TPSAS) full-dimensional multiple-input multiple-output (FD-MIMO) systems. Firstly, a TPSAS s...

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Bibliographic Details
Published in:IEEE wireless communications letters Vol. 13; no. 2; pp. 357 - 361
Main Authors: Shi, Baozhu, Liu, Fulai, Du, Ruiyan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
adaptive fully-connected subarrays
Algorithms
Antennas
Artificial neural networks
Complexity
Double dimensional hybrid precoding
energy efficiency
Hardware
joint convolutional neural network
Labels
Optimized production technology
Phase shifters
Power demand
Precoding
Radio frequency
two-layer shared phase shifter
ISSN:2162-2337, 2162-2345
Online Access:Get full text
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Summary:This letter presents a joint convolutional neural network (JCNN)-based double dimensional (DD) hybrid precoding algorithm for adaptive fully-connected subarray structure with two-layer shared phase shifters (TPSAS) full-dimensional multiple-input multiple-output (FD-MIMO) systems. Firstly, a TPSAS structure is developed to reduce hardware complexity. Then, a novel JCNN is designed to learn predicting the vectorized hybrid precoder with the vertical phase subnetwork (VpsCNN) and horizontal phase subnetwork (HpsCNN). Furthermore, the JCNN is trained by a label and a two-stage training strategy. In the first stage, minimizing residual between hybrid precoder and the label is adopted to train the JCNN. Specially, two distinct network layers are applied to meet constraints of VpsCNN and HpsCNN, respectively, including unit modulus and custom phase layers. In the second stage, estimated channel matrices are sent into the well-trained JCNN to simultaneously obtain the vertical precoder, horizontal precoder, and digital precoder. The theoretical analyses and simulation results verify that the proposed algorithm better trades off between spectral efficiency and hardware complexity than other algorithms while enhancing energy efficiency obviously.
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3329143