Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks ar...Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.展开更多
The stability and reliability of links in wireless networks is dependent on a number of factors such as the topology of the area, inter-base station or inter-mobile station distances, weather conditions and so on. Lin...The stability and reliability of links in wireless networks is dependent on a number of factors such as the topology of the area, inter-base station or inter-mobile station distances, weather conditions and so on. Link instability in wireless networks has a negative impact on the data throughput and thus, the overall quality of user experience, even in the presence of sufficient bandwidth. An estimation of link quality and link availability duration can drastically increase the performance of these networks, allowing the network or applications to take proactive measures to handle impending disconnections. In this paper we look at a mathematical model for predicting disconnection in wireless networks. This model is originally intended to be implemented in base stations of cellular networks, but is independent of the wireless technology and can thus be applied to different types of networks with minimum changes.展开更多
文摘Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.
文摘The stability and reliability of links in wireless networks is dependent on a number of factors such as the topology of the area, inter-base station or inter-mobile station distances, weather conditions and so on. Link instability in wireless networks has a negative impact on the data throughput and thus, the overall quality of user experience, even in the presence of sufficient bandwidth. An estimation of link quality and link availability duration can drastically increase the performance of these networks, allowing the network or applications to take proactive measures to handle impending disconnections. In this paper we look at a mathematical model for predicting disconnection in wireless networks. This model is originally intended to be implemented in base stations of cellular networks, but is independent of the wireless technology and can thus be applied to different types of networks with minimum changes.
