Mining Mobile Intelligence for Wireless Systems: A Deep Neural Network Approach

Abstract

Wireless big data contain valuable information on users' behaviors and preferences, which can drive the design and optimization for wireless systems. The fundamental issue is how to mine mobile intelligence and further incorporate them into wireless systems. To this end, this article discusses two challenges on big data based wireless system design and optimization, and proposes a unified framework to tackle them with the help of Deep Neural Networks (DNNs) and online learning techniques. In particular, we propose a DNN architecture by incorporating an embedding layer to project different types of raw data to a latent space and utilize a regression or classification function to predict the mobile access pattern. It outperforms the best traditional machine learning algorithm (76% vs. 63%) significantly. Moreover, combining the proposed DNN architecture with online learning techniques, we show two cases on how to apply the mobile intelligence for wireless video applications, including video adaption and video pre-fetching. In the former case, we utilize the proposed DNN method to predict the dynamics of user count within the coverage of base stations, and adaptively adjust the bitrate for video streaming to improve the video watching experience. In the latter one, we utilize the proposed method to predict the user trajectory, i.e., the associated base stations, and conduct content prefetching to reduce the access latency. Evaluating the performance with a real wireless dataset, we show that the perceived video QoE and cache hit ratio are greatly improved (0.7db and 25% respectively).