Abstract
In order to meet the needs of blowout growth of data flow and 10-100 times increase of user experience rate, the next generation mobile communication(5G) heterogeneous network deployment will use ultra-dense network. Accurate fault location is the basis of slice management. In virtualized network model, the function of network elements is software, the underlying layer is highly abstract, the structure changes dynamically, and common cause faults occur frequently, which brings difficulties to fault location. Deep Learning is a machine Learning method that USES Deep Neural Network (DNN) with multiple hidden layers to complete Learning tasks. The essence is that by building a neural network model with multiple hidden layers and using a large amount of training data to learn more useful features, the accuracy of model prediction or classification can be improved. Based on the theory of deep mapping learning algorithm, a scheduling algorithm based on event-driven access point, improved time-driven access point and idle network is designed for centralized wireless network control system. Aiming at the centralized wireless network control system, this paper designs a scheduling algorithm based on event-driven access point, improved time-driven access point and network idleness. The designed sensor network node platform uses the programmable logic controller as the main control chip, contains the data interface module that realizes the communication function between the node and the upper computer, and the acquisition and conditioning module responsible for the collection and conditioning and conversion of the analog signal. At the same time, the TCP and UDP are compared and studied. The accuracy of the model is verified, and the interference distribution of the model and the influence of the D2D inter-pair distance on the system performance are simulated. The distance between the D2D pairs under the given simulation parameters has a certain influence on the system performance. As the distance increases, the system performance will deteriorate, but the impact is not a big conclusion.
Highlights
With the deployment and implementation of ‘‘broadband China strategy’’, ‘‘smart city’’ and ‘‘Internet+’’, communication infrastructure, especially the construction of wireless communication infrastructure, has risen to the national strategic level and has become the national strategic public infrastructure [1]
This paper proposes the combination of wireless network planning and urban control planning, wireless network construction and urban infrastructure construction to realize the rational and effective allocation and utilization of resources to meet the needs of urban public mobile communication development, and is an important performance of Internet of Things applications
This paper mainly studies the 5G wireless network node planning and control logic optimization of deep mapping learning algorithm
Summary
With the deployment and implementation of ‘‘broadband China strategy’’, ‘‘smart city’’ and ‘‘Internet+’’, communication infrastructure, especially the construction of wireless communication infrastructure, has risen to the national strategic level and has become the national strategic public infrastructure [1]. This paper proposes the combination of wireless network planning and urban control planning, wireless network construction and urban infrastructure construction to realize the rational and effective allocation and utilization of resources to meet the needs of urban public mobile communication development, and is an important performance of Internet of Things applications. A depth mapping learning algorithm for 5G wireless network node planning and control logic optimization is proposed. This algorithm can achieve significant performance improvement in systems with more transmit and receive antennas. This paper mainly studies the 5G wireless network node planning and control logic optimization of deep mapping learning algorithm. In the third part, based on the above research, the 5G wireless network node planning and control logic optimization model based on depth mapping learning algorithm is analyzed and studied. The scheduling problem of wired and wireless hybrid networked control systems is studied
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
