Deep Learning Framework for Secure Communication With an Energy Harvesting Receiver

In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Conside...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-10, Vol.70 (10), p.10121-10132
Main Authors: Lee, Kisong, Hong, Jun-Pyo, Lee, Woongsup
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Cochannel interference
Computing time
Convexity
Deep learning
deep neural network
Energy harvesting
Interference
Iterative algorithms
Iterative methods
Machine learning
Optimization
Power control
pre-training
Secure communication
Security
Training
transmit power control
Wireless communications
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Summary:In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Considering co-channel interference among signal links, we find the optimal transmit power to maximize the sum rate of the signal links, while ensuring the requirements of information secrecy and energy harvesting. Due to the non-convexity of the optimization problem formulated here, we first derive suboptimal solutions using an iterative algorithm based on a dual method. In order to address the limitations caused by the use of the iterative algorithm, i.e., long computation time and suboptimality, we design an efficient deep neural network (DNN) framework and a novel training strategy as a means of combining supervised and unsupervised training. Specifically, the DNN is first pre-trained using labeled training data with the suboptimal solutions obtained from the iterative algorithm in a supervised manner; further training is then applied to the DNN using a well designed loss function in an unsupervised manner to enhance the training performance. Simulation results reveal that the proposed scheme achieves a near-optimal performance with a lower computation time than existing schemes. We also verify that the pre-training and the new loss function are effective in improving the speed of training of the DNN.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3103521