Abstract-Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy. Localizing a user and tracking its trajectory change today's cellular networks. At application layer, many services make heavy use of the user's current position. At the physical layer, accurate tracking is crucial for many beamforming approaches. In this paper, we focus on a coarse localization of the wireless user. Instead of obtaining accurate geographical coordinates, we are satisfied in expressing the user's location in terms of a cell index. However, rather than obtaining the user's current location or a short-term trajectory, we propose a framework to predict the next cell the user will hand-over to. Although this prediction is performed at low geographical accuracy and at large time scale, it has to be provided early enough to allow the system to adapt. This requires an accurate prediction before the user enters the next cell, thus, rendering simple, linear predictors inapplicable with many applications. Such applications include various adaptation functions at the medium access and network layer. New, context-aware schedulers can provide seamless quality-of-service by knowing in advance that a user will join a congested cell [1] . Interference management schemes can blank subframes of interfering users even before they join an interfered cell and context-aware handover schemes can use our long-term prediction to make faster and better decisions. Unlike previous work, based on the Global Positioning System (GPS) or Time Difference Of Arrival (TDOA) [2], our solution combines Channel State Information (CSI) with limited handover history. This combination of long-term handover information with short-term CSI, has several benefits:
In REF , they used a supervised learning classifier SVM (Support vector machine) to predict the next cell.
Predicting a user's next cell with supervised learning based on channel states
2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Computer Science