Abstract
With the explosive growth of ubiquitous mobile services and the advent of the 5G era, ultra-dense wireless network (UDN) architectures have entered daily production and life. However, the massive access capacity provided by 5G networks and the dense deployment of micro base stations also bring challenges such as high energy consumption, high maintenance costs, and inflexibility. Fiber-based visible light communication (FVLC) has the advantages of large bandwidth and high speed, which provides an efficient connection option for UDN. Thus, in order to make up for the poor flexibility of UDN, we propose a new FVLC-UDN architecture based on software-defined networks (SDNs). Specifically, SDN decouples the data plane and the control plane of the device and centralizes the control of the LED in the cell through a unified control plane, which can not only improve the resource allocation ability of the network but also transmit the data only as the data plane, reducing the manufacturing and implementation costs of the LED. In order to get a better resource allocation scheme, this paper proposes a model for predicting cell traffic based on convolutional neural networks. By predicting the traffic of each cell in the control domain, the traffic trend and cells’ status in the future period of time in the control domain can be obtained, so that a much more efficient resource allocation scheme can be formulated proactively to reduce energy consumption and balance communication loads. The experimental results show that on the real cell traffic dataset, this method is better than the existing prediction methods when the size of training dataset is limited.
Highlights
With the rapid development of communication networks and the continuous expansion of network scales, great challenges are confronting wireless communication systems due to the need for a large number of concurrent services with high connection density and heterogeneous service demands. is is especially the case in dense urban areas, where there are large-scale distributed wireless communication systems [1]
The traditional communication equipment cannot meet the high requirements of 5G or 6G communication systems, such as high capacity, high data rate, high spectral efficiency, high energy efficiency, and low delay. e latest research shows that the ultra-dense wireless network (UDN) based on fiber-based visible light communication (FVLC) is an effective solution [4]
This paper proposes a new network framework based on software-defined networks (SDNs), UDN, and FVLC, which is called the software-defined ultra-dense FVLC (SDUDFVLC) network
Summary
With the rapid development of communication networks and the continuous expansion of network scales, great challenges are confronting wireless communication systems due to the need for a large number of concurrent services with high connection density and heterogeneous service demands. is is especially the case in dense urban areas, where there are large-scale distributed wireless communication systems [1]. The traditional UDN architecture lacks flexibility in adapting to the data traffic demand with rapid time, Security and Communication Networks space, and spectrum changes, and the redundant deployment of equipment will lead to high maintenance and energy consumption costs To this end, this paper proposes a new network framework based on SDN, UDN, and FVLC, which is called the software-defined ultra-dense FVLC (SDUDFVLC) network. Erefore, to deal with the problem of delay, we believe that it is necessary to proactively evaluate the state of traffic changes and formulate a control plane based on the evaluation results To this end, we propose a traffic prediction model based on convolutional neural networks. A three-channel convolutional neural network framework is the input to predict the cell traffic Both of the above studies focus on the prediction of cellular traffic data of large base stations in the whole city.
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