Prediction-Based User Plane Handover for TCP Throughput Enhancement in Ultra-Dense Cellular Networks

Abstract

In ultra-dense cellular networks (UDNs) with user/control plane (U/C) splitting, frequent handovers in user planes are unavoidable. This seriously degrades MS's transmission control protocol (TCP) throughput. This paper proposes a prediction-based user plane handover scheme to improve the TCP throughput in UDNs. Firstly, based on algorithms used in recommender systems, a mobility prediction algorithm called content-based collaborative hybrid filters (CCHF) is proposed to predict the target small base station (SBS). When the mobile station (MS) moves into the cell-edge of the source SBS, it can set up connections to the predicted target SBS and the source SBS simultaneously. An accurate prediction and a simultaneous connection can enhance the signal to interference and noise ratio (SINR) at cell-edge and reduce the handover interruption ratio (HIR). Thus packet loss can be reduced and the MS's TCP throughput will be improved. Simulations are carried out to verify the effectiveness of the proposed CCHF-handover. It is shown that using CCHF, the prediction accuracy of random trajectory can be improved by more than 100% compared with existing prediction algorithm. Moreover, the CCHF-handover improves the average TCP throughput significantly by more than 3 times compared with that of existing handover schemes.