Dynamic Bearer Splitting for Multi-RAT Data Flow Optimization
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Solution Overview
Problem
Current wireless communication systems, particularly LTE, face challenges in efficiently managing data flows across multiple radio access technologies (RATs) due to variations in channel conditions, resource availability, and latency differences, leading to suboptimal performance in mobile broadband access.
Innovation Solution
The system dynamically splits data flows between different RATs based on optimization functions that consider channel conditions, traffic conditions, resource availability, and latency differences, using mechanisms like token bucket mechanisms and proportional fairness to maximize system utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data flows are managed using a single RAT, then device complexity is reduced, but system utility and performance are suboptimal due to inability to leverage multiple RATs
Solution Approach 1:
The data flow is segmented into multiple bearers that can be independently routed over different RATs. The system divides the data flow into first and second fractional amounts, each served through separate communication links using different RATs, allowing optimized routing while maintaining manageable complexity through structured segmentation
Solution Approach 2:
The system dynamically adjusts the fractional amounts of data flow assigned to each RAT based on real-time channel conditions, traffic conditions, and resource availability. This dynamic allocation allows the system to adapt to changing conditions and maximize system utility without requiring static complex configurations
2Productivity
If data flows are dynamically split between multiple RATs, then system utility and performance are optimized, but device complexity increases due to multiple communication links
Solution Approach 1:
The system implements partial action by serving only the necessary fractional amounts of data flow through each RAT based on current conditions. Rather than maintaining full capability across all RATs simultaneously, the system dynamically determines the optimal fractional allocation, reducing the effective complexity while maintaining high system utility
Solution Approach 2:
The system changes key parameters including the fractional amounts of data flow assigned to each RAT, channel conditions thresholds, and resource availability metrics. By dynamically adjusting these parameters based on real-time measurements, the system optimizes productivity without requiring permanent complex structures
3Speed
If fractional amounts of data flow are dynamically determined, then data rates and latencies are optimized, but measurement and control difficulty increases
Solution Approach 1:
The system employs feedback mechanisms where the network node continuously monitors channel conditions, traffic conditions, and resource availability for each RAT. Based on this feedback, the system dynamically determines the fractional amounts of data flow to be served through each RAT, enabling optimized data rates and latencies through closed-loop control
Solution Approach 2:
The system performs preliminary measurements and evaluations of channel conditions, traffic patterns, and resource availability before making routing decisions. By proactively assessing conditions and determining optimal fractional allocations in advance, the system reduces the difficulty of real-time measurement and control while maintaining optimized performance
Data Source
AI summary
A method, an apparatus, and a computer program product for wireless communication are provided. The apparatus determines a first fractional amount of a first data flow to be served to a UE via a first communication link using a first radio access technology (RAT), determines a second fractional amount of the first data flow to be served to the UE via a second communication link using a second RAT, and serves the first fractional amount of the first data flow to the UE using the first communication link.