Adaptive Congestion Control Algorithm Selection for Wireless Traffic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing congestion control, leading to suboptimal throughput and latency in various communication scenarios due to the lack of dynamic and adaptive congestion control algorithms.
Innovation Solution
A user equipment (UE) employs a congestion control algorithm selected from a set of candidate algorithms based on obtained communication parameters, using machine learning models to optimize throughput and latency by dynamically switching between different congestion control methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single congestion control algorithm is used, then device complexity is reduced, but adaptability to different communication scenarios deteriorates
Solution Approach 1:
The patent implements dynamic congestion control by enabling the UE to switch between multiple congestion control algorithms (e.g., TCP Cubic, TCP BBR, TCP Reno) based on real-time communication parameters such as network conditions, traffic type, and service requirements. This dynamic selection mechanism allows the system to adapt to varying network scenarios without permanently increasing device complexity, as the algorithm switching is triggered only when parameter thresholds are met.
Solution Approach 2:
The patent changes the operational parameters of the congestion control mechanism by introducing a set of configurable communication parameters (e.g., bandwidth, delay, packet loss rate) that determine which algorithm should be active. By monitoring these parameters and adjusting the selected algorithm accordingly, the system achieves versatility across different scenarios while maintaining manageable complexity through parameter-driven decision-making rather than hard-coded complexity.
2Adaptability or versatility
If multiple congestion control algorithms are supported, then adaptability to different scenarios is improved, but device complexity increases
Solution Approach 1:
The patent segments the congestion control functionality by dividing it into multiple independent algorithms (TCP Cubic, TCP BBR, TCP Reno, etc.), each optimized for specific scenarios. The UE evaluates communication parameters and selects only the appropriate algorithm for the current scenario, rather than maintaining all algorithms simultaneously active. This segmentation reduces the effective complexity at any given time while preserving adaptability across scenarios.
Solution Approach 2:
The patent introduces an intermediary selection mechanism that mediates between multiple congestion control algorithms and the actual communication traffic. This intermediary layer evaluates communication parameters and routes traffic through the most suitable algorithm, acting as a bridge that manages the complexity of supporting multiple algorithms without requiring the entire system to handle all algorithms simultaneously. The intermediary simplifies the overall system architecture by centralizing the selection logic.
3Productivity
If dynamic algorithm switching is implemented, then communication efficiency is improved, but processing overhead increases
Solution Approach 1:
The patent applies partial action by implementing dynamic algorithm switching only when communication parameters indicate a change in network conditions or traffic characteristics. Rather than continuously evaluating and switching algorithms, the system monitors parameters and triggers switching only when threshold changes are detected, reducing unnecessary processing overhead while maintaining communication efficiency when it matters most.
Solution Approach 2:
The patent implements feedback mechanisms where the UE continuously monitors communication parameters (bandwidth, delay, packet loss) and uses this feedback to determine when algorithm switching is appropriate. The feedback loop allows the system to make informed decisions about algorithm selection based on actual network performance, improving communication efficiency while avoiding unnecessary switching that would increase processing overhead. The feedback-driven approach ensures switching occurs only when beneficial.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may obtain one or more communication parameters associated with one or more communications to be transmitted by the UE. The UE may transmit the one or more communications based at least in part on a congestion control algorithm selected from a set of candidate congestion control algorithms, the congestion control algorithm selected based at least in part on the one or more communication parameters. Numerous other aspects are described.