Due to an increasing number of wireless spectrums,the network components are tangling with multiple frequencies and the result create hindrance in resource management process.During resource management process,data le...Due to an increasing number of wireless spectrums,the network components are tangling with multiple frequencies and the result create hindrance in resource management process.During resource management process,data leakage is one of the sensitive enigma that requires an astute consideration.Considering all these issues,a sustainable wireless resource management proposal(DSWR-SNN)has been developed by incorporating a shrewd Neural Network.The resources are managed by testing performance of each network component connected wirelessly through dataset testing which matches the results from the dataset corpus.The performance of the proposed DSWR-SNN method has been compared with state of the art studies Hopfield Neural Network(HNN),Radio Resource Management(RRM),and Deep Q-Network(DQN),and results are evaluated by conducting simulation using Python with TensorFlow based on Bandwidth Utilization,Duplicate Packet Handling,Data Leakage,and Energy Consumption.The result illustrates the marvelous performance of the proposed method and effective in addressing the challenges of resource allocation in wireless communication systems.展开更多
文摘Due to an increasing number of wireless spectrums,the network components are tangling with multiple frequencies and the result create hindrance in resource management process.During resource management process,data leakage is one of the sensitive enigma that requires an astute consideration.Considering all these issues,a sustainable wireless resource management proposal(DSWR-SNN)has been developed by incorporating a shrewd Neural Network.The resources are managed by testing performance of each network component connected wirelessly through dataset testing which matches the results from the dataset corpus.The performance of the proposed DSWR-SNN method has been compared with state of the art studies Hopfield Neural Network(HNN),Radio Resource Management(RRM),and Deep Q-Network(DQN),and results are evaluated by conducting simulation using Python with TensorFlow based on Bandwidth Utilization,Duplicate Packet Handling,Data Leakage,and Energy Consumption.The result illustrates the marvelous performance of the proposed method and effective in addressing the challenges of resource allocation in wireless communication systems.
