Mobile Network Congestion Management via Cell Metric Correlation
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Solution Overview
Problem
Mobile/radio access networks face increasing congestion due to growing online traffic, particularly from streaming video and gaming applications, leading to poor Quality of Experience (QoE) for subscribers and potential customer churn, with operators lacking effective methods to assess congestion.
Innovation Solution
A system and method for managing mobile network congestion by determining cell metrics, including subscriber and traffic metrics, over a predetermined time interval, analyzing correlations between these metrics, identifying congested cells, determining the type of congestion, and taking appropriate traffic actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operators upgrade network to 5G to meet growing traffic demands, then network capacity is improved, but capital expenditure increases significantly
Solution Approach 1:
The system performs preliminary analysis of congestion patterns and predicts future congestion points by analyzing historical data and correlations between cell metrics. This allows operators to proactively manage congestion through traffic shaping and load balancing before infrastructure upgrades are needed, avoiding the need for immediate capital expenditure on 5G upgrades.
Solution Approach 2:
The system changes operational parameters such as traffic priorities, bandwidth allocation, and load distribution based on real-time congestion detection. By dynamically adjusting these parameters rather than relying solely on infrastructure upgrades, the network can maintain capacity without significant capital expenditure.
2Quantity of substance
If operators manage congestion within existing network, then capital expenditure is reduced, but subscriber Quality of Experience deteriorates
Solution Approach 1:
The system continuously monitors cell metrics including throughput, round trip time, and packet loss, and uses this feedback to identify congestion conditions. By implementing feedback loops that trigger traffic management actions only when congestion is detected, the system maintains Quality of Experience while avoiding unnecessary infrastructure expenditure during normal operating conditions.
Solution Approach 2:
The system dynamically adjusts network parameters based on real-time congestion conditions rather than using static infrastructure upgrades. This dynamic approach allows the network to optimize performance in response to actual demand patterns, maintaining Quality of Experience without the need for continuous capital expenditure.
3Productivity
If operators invest in additional RAN equipment and infrastructure, then network capacity is improved, but device complexity increases
Solution Approach 1:
The system enables the network to self-manage congestion by automatically detecting congestion conditions through correlation analysis of cell metrics and implementing traffic management actions without requiring complex external control systems or additional RAN equipment. The network uses its existing monitoring capabilities to perform self-diagnosis and self-adjustment.
Solution Approach 2:
The system uses existing network infrastructure and monitoring tools for multiple purposes: congestion detection, traffic analysis, and capacity planning. By making existing components multi-functional rather than adding dedicated equipment for each function, the system avoids increasing device complexity while still achieving effective congestion management.
4Quantity of substance
If operators do not manage congestion properly, then infrastructure cost is reduced, but subscriber churn increases due to poor QoE
Solution Approach 1:
The system performs preliminary identification of at-risk subscribers and congestion-prone cells by analyzing historical data and predicting future congestion patterns. This allows the network to take preventive actions such as proactive traffic shaping or load redistribution before congestion actually occurs, maintaining subscriber QoE and preventing churn without requiring additional infrastructure investment.
Solution Approach 2:
The system continuously monitors subscriber experience metrics and uses this feedback to identify and address congestion issues that could lead to churn. By implementing feedback-driven traffic management actions, the network maintains subscriber satisfaction and retention while avoiding the need for expensive infrastructure upgrades.
Data Source
AI summary
A method for managing mobile network congestion including: determining cell metrics over a predetermined time interval for each cell of a plurality of cells; determining correlations between the cell metrics for each cell; determining whether any cell of the plurality of cell are congestion based on the correlations; determining a type of congestion for any cell determined to be congested; and determining traffic actions based on the type of congestion. A system for managing mobile network congestion having: a collection module configured to determine cell metrics over a predetermined time interval for each cell of a plurality of cells; a correlation module configured to determine correlations between the cell metrics; an analysis module configured to determine whether any cell is congestion based on the correlations and a type of congestion for any cell determined to be congested; and a traffic action module configured to determine traffic actions.