User Plane Handoff Method Based on Mobility Prediction

Abstract

The invention adopts a content-based hybrid collaborative filtering prediction algorithm to improve the prediction accuracy of the user plane access target SBS in the ultra-dense networking. Integrate content-based filtering and collaborative filtering to form a content-based hybrid collaborative filtering algorithm CCHF, and based on the target SBS predicted by CCHF, and considering the signal strength changes of the target base station and source base station, a soft handover mechanism "CCHF‑ handover" to increase the SINR and reduce the HIR of edge users. Compared with the existing handover method, the method proposed in the present invention can effectively improve prediction accuracy, increase SINR, reduce handover interruption frequency HIR, further improve TCP throughput, and guarantee user service quality.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a user plane handover mechanism in an ultra-dense networking, and in particular, the invention relates to a mobility prediction-based user plane handover method. Background technique [0002] In wireless communication, ultra-dense cellular networks (UDNs) can effectively improve network capacity. In UDNs, more and more small base stations (SBS) are deployed within the coverage of existing macro base stations (MBS). Therefore, when the user moves, frequent wireless handoffs will occur. The researchers proposed a U / C (user / control plane) separation architecture to reduce the switching frequency of the control plane, that is, the user's control plane is connected to the macro base station, and the user plane is connected to the micro base station. When the user moves within the coverage of the same macro base station, the control plane does not need to be handed over, but the...

AI Technical Summary

Problems solved by technology

[0003] (1) When the user is at the edge of the cell, the signal to interference and noise ratio (SINR) is low, and the user can only access one SBS. When the user moves to the edge of the source SBS and handover is about to occur, the user is affected by other surrounding SBS Severe interference caused by the signal-to-noise ratio decreases, the bit error rate is high, and data packet transmission errors are lost;

[0004] (2) The handover interruption rate (interruption time per unit time, handover interruption ratio, HIR) is high, and the traditional handover "disconnect first and then connect" means that the user first disconnects from the source SBS and then connects to the target SBS, resulting in wireless transmission Interruption, packet loss;

In general, DTMM-Handover can reduce HIR, but cannot improve SINR of cell edge users

Therefore, the user's TCP throughput will still drop rapidly

Moreover, the DTMM-based prediction method is only suitable for regular trajectories, i.e., the prediction is only valid for the SBS collections that the user has visited

When the user's historical trajectory is relatively sparse or the user enters a new area, the number of SBSs visited by the user is very small, and the currently resident SBS may not exist in the set of SBSs visited by the user, so that the user cannot be correctly predicted The next SBS

Therefore, such irregular trajectories are bound to lead to a sharp drop in predictive performance

[0007] To sum up, the existing handover mechanism cannot effectively guarantee the high SINR of users and reduce the handover interruption rate, so it cannot guarantee stable network performance and seriously affects user experience

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