Deep Neural Network Assisted Approach for Antenna Selection in Untrusted Relay Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 1644-1647 ◽  
Author(s):  
Rugui Yao ◽  
Yuxin Zhang ◽  
Shengyao Wang ◽  
Nan Qi ◽  
Nikolaos I. Miridakis ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Antenna Selection ◽  
Relay Networks ◽  
Untrusted Relay

scholarly journals Secure Transmission for Simultaneous Wireless Information and Power Transfer in AF Untrusted Relay Networks

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 76 ◽  
Author(s):  
Hui Shi ◽  
Weiwei Yang ◽  
Dechuan Chen ◽  
Yunpeng Luo ◽  
Yueming Cai
Keyword(s):  
Outage Probability ◽  
Antenna Selection ◽  
Signal To Noise Ratio ◽  
Relay Networks ◽  
Optimal Number ◽  
Maximal Ratio Combining ◽  
High Signal ◽  
Secrecy Outage Probability ◽  
Transmit Antenna Selection ◽  
Untrusted Relay

This paper investigates secure communications of energy harvesting untrusted relay networks, where the destination assists jamming signal to prevent the untrusted relay from eavesdropping and to improve the forwarding ability of the energy constrained relay. Firstly, the source and the destination transmit the signals to the relay with maximal ratio transmission (MRT) technique or transmit antenna selection (TAS) technique. Then, the destination utilizes maximal ratio combining (MRC) technique or receive antenna selection (RAS) technique to receive the forwarded information. Therefore, four transmission and reception schemes are considered. For each scheme, the closed-form expressions of the secrecy outage probability (SOP) and the connection outage probability (COP) are derived. Besides, the effective secrecy throughput (EST) metric is analyzed to achieve a good tradeoff between security and reliability. In addition, the asymptotic performance of EST is also considered at the high signal-to-noise ratio (SNR). Finally, simulation results illustrate that: (1) the EST of the system with MRT and MRC scheme are superior to other schemes, however, in the high SNR regime, the EST of the system with MRT scheme is inferior to TAS; and (2) for the source node, there exists an optimal number of antennas to maximize the EST of the proposed schemes.

scholarly journals Machine Learning-Based Antenna Selection in Untrusted Relay Networks

2019 ◽  
Author(s):  
Rugui Yao ◽  
Yuxin Zhang ◽  
Nan Qi ◽  
Theodoros A. Tsiftsis
Keyword(s):  
Machine Learning ◽  
Antenna Selection ◽  
Relay Networks ◽  
Support Vector ◽  
Secrecy Outage Probability ◽  
Secrecy Rate ◽  
Transmit Antenna Selection ◽  
K Nearest Neighbors ◽  
Selection Scheme ◽  
Untrusted Relay

This paper studies the transmit antenna selection based on machine learning (ML) schemes in untrusted relay networks. First, we state the conventional antenna selection scheme. Then, we implement three ML schemes, namely, the support vector machine-based scheme, the naive-Bayes-based scheme, and the k-nearest neighbors-based scheme, which are applied to select the best antenna with the highest secrecy rate. The simulation results are presented in terms of system secrecy rate and secrecy outage probability. From the simulation, we can conclude that the proposed ML-based antenna selection schemes can achieve the same performance without amplification at the relay, or small performance degradation with transmitted power constraint at the relay, comparing with conventional schemes. However, when the training is completed, the proposed schemes can perform the antenna selection with a small computational complexity.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Sign in / Sign up

Export Citation Format

Share Document