UE Traffic Shaping for Cell Handover BDP Changes
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Solution Overview
Problem
Existing congestion control mechanisms in TCP & QUIC are unable to adjust to sudden changes in bandwidth delay product (BDP) during mobile handovers between cells with varying wireless capabilities, leading to packet queueing delays and degraded user experience in heterogeneous networks.
Innovation Solution
A method and system for traffic shaping in wireless communication networks that estimate congestion using network key performance indicators (KPIs), predict handovers based on UE movement and BDP, and transmit congestion notifications to the core network for proactive traffic shaping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing congestion control mechanisms (TCP & QUIC) are used, then basic data transmission is maintained, but packet queueing delays and packet drops occur during handovers between cells with different BDP, leading to degraded user experience
Solution Approach 1:
The system performs preliminary actions by estimating congestion levels and predicting handovers before they actually occur. The network entity monitors KPIs, identifies potential handover scenarios, and prepares traffic shaping policies in advance, allowing the system to proactively adjust traffic flow before packet queueing delays occur during handover transitions.
Solution Approach 2:
The system dynamically adapts traffic shaping policies based on real-time congestion estimates and predicted handover scenarios. The traffic shaping parameters are continuously adjusted according to the UE's movement patterns and the congestion state of target cells, enabling the system to respond flexibly to changing network conditions during handovers.
2Reliability
If existing congestion control mechanisms (TCP & QUIC) are used, then basic data transmission is maintained, but packet drops occur during handovers between cells with different BDP, leading to degraded user experience
Solution Approach 1:
The system applies preliminary traffic shaping policies before handovers occur by predicting potential handover scenarios and pre-configuring appropriate traffic shaping parameters. This proactive approach prevents packet drops by adjusting traffic flow in advance, rather than reacting after packets are already lost during the handover transition.
Solution Approach 2:
The system continuously monitors network KPIs and handover performance, using this feedback to refine congestion estimates and improve handover predictions. The feedback loop enables the system to learn from past handover scenarios and optimize traffic shaping policies to minimize packet losses in future handovers between cells with different BDP.
3Productivity
If proactive traffic shaping is implemented by monitoring KPIs and predicting handovers, then user experience is improved with reduced packet losses and enhanced throughput, but device complexity increases due to additional monitoring and prediction functions
Solution Approach 1:
The network entity performs multiple functions including congestion estimation, handover prediction, and traffic shaping policy generation using a unified system architecture. By consolidating these functions in a single network entity rather than distributing them across multiple components, the system achieves improved throughput while managing complexity through functional integration.
Solution Approach 2:
The system uses existing network KPIs and monitoring infrastructure to perform congestion estimation and handover prediction, rather than requiring entirely new measurement and control mechanisms. This self-service approach leverages available network resources to implement proactive traffic shaping without proportionally increasing device complexity.
Data Source
AI summary
The present disclosure discloses a method and system for traffic shaping for a user equipment (UE) in a wireless communication network. The method comprises: estimating a congestion at each of a plurality of cells in the wireless communication network based on one or more network key performance indicators (KPIs) associated with the wireless communication network, collecting movement data of the UE by monitoring a movement of the UE connected to a first cell, predicting, based on the movement data and at least one of the estimated congestion and a bandwidth delay product (BDP) of the each of the plurality of cells, whether the UE is likely to be handed over from the first cell to a second cell, and transmitting a congestion notification to a core network of the wireless communication network based on the prediction, to perform traffic shaping for the UE upon receiving the congestion notification.