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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2022/4038830 |
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| author | Zhen Tang Xiaobin Fu Pingping Xiao |
| author_facet | Zhen Tang Xiaobin Fu Pingping Xiao |
| author_sort | Zhen Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-44b29e99f9234f8b8bb6799e3ffd7ee1 |
| institution | Kabale University |
| issn | 1687-5877 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-44b29e99f9234f8b8bb6799e3ffd7ee12025-02-03T06:01:51ZengWileyInternational Journal of Antennas and Propagation1687-58772022-01-01202210.1155/2022/4038830Mobile Performance Intelligent Evaluation of IoT Networks Based on DNNZhen Tang0Xiaobin Fu1Pingping Xiao2College of Physical Science and EngineeringCollege of Physical Science and EngineeringCollege of Physical Science and EngineeringThe 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.http://dx.doi.org/10.1155/2022/4038830 |
| spellingShingle | Zhen Tang Xiaobin Fu Pingping Xiao Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN International Journal of Antennas and Propagation |
| title | Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN |
| title_full | Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN |
| title_fullStr | Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN |
| title_full_unstemmed | Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN |
| title_short | Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN |
| title_sort | mobile performance intelligent evaluation of iot networks based on dnn |
| url | http://dx.doi.org/10.1155/2022/4038830 |
| work_keys_str_mv | AT zhentang mobileperformanceintelligentevaluationofiotnetworksbasedondnn AT xiaobinfu mobileperformanceintelligentevaluationofiotnetworksbasedondnn AT pingpingxiao mobileperformanceintelligentevaluationofiotnetworksbasedondnn |