UE Throughput Distribution for VoIP and Non-VoIP Applications
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Solution Overview
Problem
In 5G communication systems, there is a lack of clear methods for a User Equipment (UE) to determine when and how to trigger a recommended bit rate (RBR) query for specific logical channels and to select a suitable bit rate for applications, leading to inadequate rate adaptation for applications like VoIP and non-VoIP services.
Innovation Solution
The UE employs a method to dynamically distribute throughput among applications using a throughput handler and application manager, which determine throughput requirements based on application response descriptions and feedback reports, allocating codec rates to VoIP applications and bit rates to non-VoIP applications based on priority and requirements, utilizing adaptive rate control and reinforcement learning-based codec rate adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UE dynamically distributes throughput among applications using throughput handler and application manager, then the quality of service for VoIP applications is improved, but the device complexity increases
Solution Approach 1:
The patent segments the throughput distribution function by introducing a dedicated throughput handler component that separates the rate distribution logic from the application manager. This segmentation allows the complex throughput distribution task to be managed through modular components (throughput handler, application manager, and per-application rate controllers) rather than a monolithic system, thereby improving service quality while managing complexity through structural organization.
Solution Approach 2:
The throughput handler acts as an intermediary component between the network layer and the application layer. It receives throughput information from the network and distributes it to various applications based on their requirements and priorities. This intermediary structure enables sophisticated rate adaptation without requiring direct complex interactions between all components, thus improving QoS while maintaining manageable system complexity.
2Productivity
If the UE implements adaptive rate control and reinforcement learning-based codec rate adaptation, then the throughput allocation is optimized, but the use of energy increases
Solution Approach 1:
The patent implements feedback mechanisms where the application manager receives feedback reports from applications about their throughput requirements and performance. This feedback loop enables the system to dynamically adjust rate allocations based on actual application needs and network conditions. The feedback-driven adaptation allows the system to optimize throughput allocation by distributing resources more efficiently, reducing energy consumption compared to static or less-responsive allocation methods.
Solution Approach 2:
The system dynamically adjusts codec rates and bit rates based on real-time conditions rather than using fixed allocation. The reinforcement learning-based adaptation continuously learns from network conditions and application behavior to optimize rate distribution. This dynamic approach ensures that energy is allocated proportionally to actual throughput needs, improving productivity while avoiding the energy waste associated with over-provisioning or static allocation schemes.
3Measurement precision
If the UE determines throughput requirements based on application response descriptions and feedback reports, then the bit rate allocation is improved, but the loss of information increases
Solution Approach 1:
The patent extracts essential throughput requirement information from the potentially overwhelming amount of application data and feedback reports. The application manager selectively processes and extracts only the critical parameters needed for rate allocation decisions, filtering out redundant or less relevant information. This extraction process enables precise bit rate allocation based on meaningful application requirements while minimizing the processing burden and potential information loss that would result from attempting to process all available data.
Data Source
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AI summary
In an embodiment, a method of distributing throughput intelligently amongst a plurality of applications residing at a User Equipment (UE) is disclosed. The method comprises receiving, at the UE, recommended bit rate (RBR) information from a network node, wherein the RBR information is indicative of a throughput value allocated to the UE. The method further comprises allocating a codec rate from the allocated throughput value to at least one voice over internet protocol (VoIP) application from the plurality of applications. Further, the method comprises allocating, from remaining throughput value of the allocated throughput value, a bit rate to each of a plurality of non-VoIP applications from the plurality of applications, based on corresponding throughput requirement associated with the plurality of non-VoIP applications.