Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN

The rapid development of the sensor equipment has promoted the rapid growth of the Internet of Things (IoT). The IoT has been widely employed in the multidimensional signal processing and gradually formed the IoT networks. Mobile communication promotes the wide application of the IoT networks. In th...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of antennas and propagation Vol. 2022; pp. 1 - 7
Main Authors Tang, Zhen, Fu, Xiaobin, Xiao, Pingping
Format Journal Article
LanguageEnglish
Published New York Hindawi 16.03.2022
Hindawi Limited
Subjects
Accuracy
Algorithms
Artificial neural networks
Error analysis
Internet of Things
Mathematical models
Neural networks
Parameter estimation
Performance evaluation
Signal processing
Simulation
Online AccessGet full text

Cover

More Information
Summary:The rapid development of the sensor equipment has promoted the rapid growth of the Internet of Things (IoT). The IoT has been widely employed in the multidimensional signal processing and gradually formed the IoT networks. Mobile communication promotes the wide application of the IoT networks. In this study, the transmit antenna selection (TAS) scheme is employed to investigate the average symbol error probability (ASEP) performance of mobile IoT networks over the 2-Rayleigh channels. We first employ moment-generating function (MGF) approach to derive the exact ASEP expressions. We also investigate the outage probability (OP) performance and derive OP expressions. Employing the deep neural network (DNN), an OP intelligent prediction algorithm is proposed. Then, the numerical simulations are conducted to confirm the ASEP and OP performance analysis. The effect of different channel parameters is also analyzed. Compared with Nakagami and Rayleigh channel models, the 2-Rayleigh model has 83.6% and 59.1% increase in ASEP values, respectively. Compared with ELM and RBF models, the DNN model has 31.7% and 22.5% increase in OP prediction accuracy, respectively.
ISSN:1687-5869
1687-5877
DOI:10.1155/2022/4038830