Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN. (16th March 2022)

• Record Type: Journal Article
• Authors: Tang, Zhen; Fu, Xiaobin; Xiao, Pingping
• Other Names: Liu Qi Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2022(2022)
• Publication Date: 2022-03-16
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2022/4038830
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : 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.