Adaptive Congestion Control for Wireless Networks
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Solution Overview
Problem
The increasing number of IoT devices in wireless access networks leads to congestion, affecting consumer traffic and IoT device operations, with existing congestion control schemes often being passive and reactive, leading to poor customer experience and potential disruptions in mission-critical applications.
Innovation Solution
A closed-loop adaptive congestion avoidance system that monitors device types, cell locations, load levels, and predicted congestion to proactively throttle traffic, adjusting based on predicted congestion duration to minimize unnecessary throttling and ensure network resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If proactive traffic throttling is implemented to prevent congestion, then network resource utilization is improved, but customer experience deteriorates due to unnecessary throttling
Solution Approach 1:
The system performs preliminary actions by predicting congestion before it occurs using machine learning models that analyze historical traffic patterns and current network conditions. This allows the network to prepare and throttle traffic proactively, preventing congestion while avoiding unnecessary throttling of legitimate traffic.
Solution Approach 2:
The system implements continuous feedback loops where network performance metrics are monitored, predictions are made, actions are taken, and results are evaluated. This closed-loop approach allows the system to learn from outcomes and refine its predictions, improving the balance between preventing congestion and maintaining customer experience.
2Stability of the object's composition
If traffic is throttled to avoid congestion, then network stability is improved, but traffic flow deteriorates due to excessive throttling
Solution Approach 1:
The system dynamically adjusts throttling decisions based on real-time network conditions and prediction confidence levels. Rather than applying fixed throttling rules, the system adapts its behavior to current traffic patterns, network load, and predicted congestion scenarios, optimizing the balance between stability and traffic flow.
Solution Approach 2:
The system changes key parameters such as throttling intensity, prediction time horizons, and traffic selection criteria based on network conditions. By dynamically adjusting these parameters, the system can maintain network stability while minimizing the impact on legitimate traffic flow.
3Measurement precision
If machine learning models are deployed for congestion prediction, then prediction accuracy is improved, but computational complexity worsens
Solution Approach 1:
The system segments the prediction task by deploying specialized machine learning models at different network locations (edge devices, base stations, core network). This distribution of computational load reduces the complexity burden on any single device while maintaining overall prediction accuracy through coordinated processing.
Data Source
AI summary
A system may receive user device information that includes a location associated with the user device and an identifier of a base station associated with the location and receives load information associated with the base station. The system may determine, based on the user device information and the load information, whether congestion is predicted and perform traffic throttling in response to determining the congestion is predicted. A schedule for adjusting the traffic throttling is determined and the traffic throttling is adjusted based on the schedule.