Identifying LTE Connectivity Hot Spots in Vehicular Environments: A Learning Approach

Abstract—Due to the increasing demand of mobile Machine-Type Communications (MTC), the interaction between MTC and human services is a recent problem for cellular communication systems like Long Term Evolution (LTE). In order to reduce the negative impact of MTC on human communication, a Learning-based Channel-Aware Transmission (L-CAT) scheme will be introduced in this paper. The algorithm bases on a learning process of cellular connectivity hot spots and is designed for non-time-critical vehicular data applications like extended Floating Car Data (xFCD) transmissions for traffic forecast systems. The results based on real-world measurements show that L-CAT leads to a much faster data transmission that correlates with a more resource efficient MTC. I. INTRODUCTION AND RELATED WORK Many mobile devices follow the routes of driving vehicles