Artificial Intelligent Systems and Machine Learning

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Antenna Switching for 5G using Neural Network by Vary the Number of Layers


Affiliations
1 Electronics and Communication Engineering at Rayat Institute of Engineering & Information Technology, SBS Nagar, India
2 Department of Electronics and Communication Engineering at Rayat Institute of Engineering & Information Technology, SBS Nagar, India
3 Department of Physics, Panjab University, Chandigarh, India
     

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In this paper we optimize the wide narrow band antenna switching for 5G using feedforward neural network to measure the suitability for accuracy. As we know 5G terminals will have software defined radios and modulation scheme as well as new error-control schemes can be downloaded from the Internet on the run. Here we done the switching with the help of neural network taking some parameters. Each network will be responsible for handling user-mobility, while the terminal will make the final choice among different wireless/mobile access network providers for a given service. Here we have investigated and analyzed this system to optimize the neural networks as to what numbers of layers are most suitable for optimization. Here accuracy of above 81% is reported.

Keywords

Optimization, 5G, Neural Network, Software Defined Radios, Noise Threshold, Transmission Antenna Power, Channel Noise.
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  • Antenna Switching for 5G using Neural Network by Vary the Number of Layers

Abstract Views: 209  |  PDF Views: 3

Authors

Rishi Sharma
Electronics and Communication Engineering at Rayat Institute of Engineering & Information Technology, SBS Nagar, India
Neeru Singla
Department of Electronics and Communication Engineering at Rayat Institute of Engineering & Information Technology, SBS Nagar, India
Manish Dev Sharma
Department of Physics, Panjab University, Chandigarh, India

Abstract


In this paper we optimize the wide narrow band antenna switching for 5G using feedforward neural network to measure the suitability for accuracy. As we know 5G terminals will have software defined radios and modulation scheme as well as new error-control schemes can be downloaded from the Internet on the run. Here we done the switching with the help of neural network taking some parameters. Each network will be responsible for handling user-mobility, while the terminal will make the final choice among different wireless/mobile access network providers for a given service. Here we have investigated and analyzed this system to optimize the neural networks as to what numbers of layers are most suitable for optimization. Here accuracy of above 81% is reported.

Keywords


Optimization, 5G, Neural Network, Software Defined Radios, Noise Threshold, Transmission Antenna Power, Channel Noise.