Dynamic Network Selection using Kernels

—We present a new algorithm for vertical handover and dynamic network selection, based on a combination of multiattribute utility theory, kernel learning and stochastic gradient descent. We show that this new method is able to improve network selection in a non-stationary mobile environment. Furthermore, since the kernel employed is based on the utility functions for attributes such as Availability, Quality and Cost, the kernel regression in fact gives interpretable results. We present simulation results that demonstrate our algorithm being able to dynamically learn utilities and efficiently select networks. Keywords-Mobility management, network selection, handover, kernels, statistical learning

• Type: Conference
• Year: 2007
• Where: ICC
• Authors: Eric van den Berg, Praveen Gopalakrishnan, Byungsuk Kim, Bryan Lyles, Won-Ik Kim, Yeon-Seung Shin, Yeong-Jin Kim
• Keywords: Communications, Dynamic Network Selection, ICC 2007, Network Selection, Stochastic Gradient Descent