Application-Aware Radio Scheduling for Spectrum Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for controlling application data flows in radio networks are inefficient, leading to poor quality of experience (QoE) for users, especially in mobile access due to limited bandwidth and unpredictable radio resources, and existing QoS solutions result in scalability issues and low radio spectrum efficiency.
Innovation Solution
A method and apparatus that detect and monitor application data flows, using cost measures and key performance indicators to optimize radio scheduling, prioritizing flows to maintain a satisfactory quality of experience without requiring terminal support or dynamic QoS mechanisms, thereby optimizing radio spectrum usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard QoS architecture with guaranteed bit-rate bearers is applied to guarantee required throughput, then quality of service is improved, but radio spectrum efficiency deteriorates and scalability problems occur
Solution Approach 1:
The patent changes the scheduling parameters dynamically based on application performance metrics and radio conditions. Instead of static GBR allocations, the system adjusts scheduling weights, priorities, and resource allocation parameters in real-time to optimize both QoS and spectrum efficiency. This allows the network to adapt to varying traffic patterns and channel conditions without committing to rigid bearer configurations.
Solution Approach 2:
The system implements dynamic scheduling decisions that adapt to changing application performance and radio conditions. The network node continuously monitors application KPIs and adjusts scheduling parameters dynamically, allowing the system to respond to varying traffic demands and channel quality without requiring pre-configured GBR bearers for each application.
2Reliability
If traffic throttling is applied to control delivery of application data, then quality of service is maintained, but radio spectrum efficiency deteriorates due to unused capacity
Solution Approach 1:
The system implements feedback mechanisms where the network node monitors application performance metrics and uses this information to adjust scheduling decisions. By continuously measuring application KPIs and comparing them against targets, the system can identify when throttling is unnecessary and increase resource allocation to improve spectrum efficiency while maintaining acceptable QoS levels.
Solution Approach 2:
The system performs preliminary actions by proactively adjusting scheduling parameters based on predicted application needs and radio conditions. Instead of reactive throttling, the network anticipates traffic patterns and pre-allocates resources appropriately, reducing the need for aggressive throttling while maintaining QoS and improving overall spectrum utilization.
3Productivity
If best effort traffic delivery is used for OTT services, then radio spectrum efficiency is maintained, but quality of experience deteriorates due to buffer underrun and frozen images
Solution Approach 1:
The system enables applications to self-monitor their performance metrics and report them to the network node. This self-service mechanism allows the network to identify applications experiencing QoE degradation without requiring complex network-side monitoring of application-level performance, maintaining spectrum efficiency while improving QoE through targeted scheduling adjustments.
Solution Approach 2:
The system takes preliminary action by proactively identifying applications at risk of QoE degradation through monitored KPIs and adjusting scheduling before buffer underrun occurs. This preventive approach maintains QoE by ensuring applications receive sufficient resources before performance degradation happens, rather than reacting after problems occur.
Data Source
AI summary
The disclosure relates to a method and network node for controlling a plurality of flows of application data in a radio network. The plurality of flows and a cost measure associated with each flow is detected. The cost measure is an indication of an amount of radio resources required for supplying an amount of application data to an end-user of the flow. Further, key performance indicators associated with the plurality of flows are monitored. A key performance indicator is indicative of a supply of application data needed to maintain a satisfactory quality of experience for an application session associated with the flow. Further, scheduling decisions, to control scheduling of the plurality of flows, are made based on the cost measures and key performance indicators associated with the plurality of flows to optimize the quality of experience of the application sessions associated with the plurality of flows.