Wireless Congestion Detection via KPI Trend Prediction
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Solution Overview
Problem
Wireless network congestion caused by high concentrations of smartphone users leads to slow service and dropped calls, as background applications continue to consume network resources without user intervention, necessitating near real-time identification and management of congestion to prevent network overload.
Innovation Solution
Implementing a system that uses key performance indicators (KPIs) to predict wireless communication congestion, enabling mobile devices to restrict background application data communications during congestion periods and re-enable them when congestion subsides, thereby reducing network load and maintaining service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the wireless network rejects successive requests to establish data communications or calls to relieve congestion, then the wireless network load is reduced, but additional load is generated on the wireless network and congestion is exacerbated
Solution Approach 1:
The system performs preliminary action by detecting congestion conditions and notifying mobile devices before the network becomes overloaded. Mobile devices then proactively restrict background data communications in advance, preventing the need for network rejection of requests and avoiding the harmful feedback loop of rejection-induced congestion.
Solution Approach 2:
The system implements feedback by continuously monitoring network performance metrics (such as signal strength, data transmission rates, and error rates) and using this information to determine congestion conditions. This feedback loop enables dynamic adjustment of background data communications to maintain network stability.
2Adaptability or versatility
If background applications continue to consume network resources during high user concentration, then application functionality is maintained, but network congestion increases leading to slow service and dropped calls
Solution Approach 1:
The system applies dynamics by making background data communication restrictions conditional and reversible. When congestion is detected, background applications are restricted; when congestion subsides, restrictions are lifted. This dynamic adjustment maintains application functionality when possible while preventing network overload.
Solution Approach 2:
The system changes the parameter of data communication activity by adjusting background application behavior based on network conditions. Mobile devices monitor network parameters (signal strength, data rates, error rates) and modify their data transmission patterns accordingly, switching between unrestricted and restricted modes to optimize both functionality and network performance.
3Ease of operation
If the wireless network determines congestion and manages traffic centrally, then network control is maintained, but response time increases and user experience deteriorates
Solution Approach 1:
The system extracts the congestion management function from the network side and relocates it to the mobile device side. Instead of the network centrally controlling and managing traffic, mobile devices independently detect congestion conditions and autonomously restrict their own background data communications, enabling faster local response without network intervention delays.
Data Source
AI summary
Near real time identification of predicted wireless communication congestion is based on a relationship between a number of key performance indicator (KPI) trend prediction values and corresponding KPI trend thresholds. Each KPI trend prediction value is calculated based on a number of past intervals and a number of KPIs corresponding to each past interval of the number of past intervals utilizing linear regression. A mobile communication network node, such as an enhanced node B, may identify predicted wireless communication congestion and notify mobile devices served by the node. Mobile devices served by the node, based on the notification, may restrict background application data communication and/or take appropriate action to contribute to a reduction in and/or elimination of the congestion.