Multi-Objective DNN-Based Precoder for MIMO Communications

This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. Firs...

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Veröffentlicht in:IEEE transactions on communications Jg. 69; H. 7; S. 4476 - 4488
Hauptverfasser: Zhang, Xinliang, Vaezi, Mojtaba
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Antennas
Artificial neural networks
beamforming
Data transmission
Deep learning
Energy harvesting
MIMO
MIMO communication
Multicast communication
Multicasting
Multiple objective analysis
Optimization
physical layer
Power transfer
Precoding
Robustness (mathematics)
Rotation
Security
SWIPT
Wireless communication
wiretap channel
ISSN:0090-6778, 1558-0857
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Zusammenfassung:This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoder is developed to solve the above problems independently. Rotation-based precoding is a new precoding and power allocation scheme that beats existing solutions for PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2021.3071536