LAUSR

This paper presents an artificial intelligence-assisted network slice prediction method, which utilizes a novel incremental random vector functional link (IRVFL) network to deal with the wireless network slice assignment (WNSA) problem in a data-driven way. The goal of WNSA is to assign an appropriate network slice for a user's requirement based on next-generation communication data. The IRVFL network is an incremental version of the RVFL network, where a data stream processing approach is used to gradually update output layer weights as new data arrive rather than processing the data as a single large data set. To ensure that the RVFL network can be trained for WNSA and have high adaptability and expansibility, we derive a novel flexible and appropriate rule for updating output layer weights of the IRVFL network. We have carried out extensive experiments to validate the feasibility, rationality, and effectiveness of using the IRVFL network for the WNSA problem. Results show that network slice prediction converges as the IRVFL network is incrementally trained and that the time required for training the incremental RVFL network is far less than for the non-incremental RVFL network. The incremental training of IRVFL network improves the performance of wireless network slice prediction. In addition, a comparison with six classification algorithms reveal that the IRVFL network consumes the least amount of time and has equivalent wireless network slice prediction performance.