Abstract
Mobile Edge Computing (MEC) has emerged as a promising technology to meet with the high data rate, real-time transmission, and huge computation requirements for the ever growing future wireless terminals, such as virtual reality devices, augmented reality, and the Internet of Vehicles. Due to the limitation of licensed bandwidth resources, mobile data offloading should be considered. On the other hand, WiFi AP that works on the abundant unlicensed spectrum can provide good wireless services under light-loaded areas. Therefore, in this paper we leverage WiFi AP to offload some devices from SBS. To effectively perform the offloading process, we build a multi-LSTM based deep-learning algorithm to predict the traffic of SBS. According to the prediction results, an offline mobile data offloading strategy has been proposed, which has been obtained through cross entropy method. Simulation results demonstrate the efficiency of our prediction model and offloading strategy.
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grants 2042018kf1009 and 204208kf10002, and in part by the National Natural Science Foundation Program of China under Grants 61371126.
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Jiang, H., Peng, D., Yang, K., Zeng, Y., Chen, Q. (2018). Predicted Mobile Data Offloading for Mobile Edge Computing Systems. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_16
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DOI: https://doi.org/10.1007/978-3-030-05755-8_16
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