5G QoS Flow Mapping for TSN Traffic Classes
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Solution Overview
Problem
Current 5G systems lack an accurate mechanism to populate the Maximum Data Burst Volume (MDBV) parameter for 5G Quality of Service (QoS) flows, leading to inefficient radio resource allocation and potential TSN traffic loss due to insufficient or excessive payload capacity.
Innovation Solution
The proposed solution involves determining traffic class-specific information, including Packet Delay Budget (PDB) and maximum payload volume, to accurately configure 5G QoS flows. This includes establishing 5QI table entries with appropriate PDB and MDBV attributes, allowing for efficient mapping of TSN streams into subgroups supported by common 5G QoS flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the Maximum Data Burst Volume (MDBV) parameter is not accurately configured for 5G QoS flows, then radio resource allocation becomes inefficient, but implementing accurate MDBV configuration requires complex mechanisms to determine traffic class-specific information
Solution Approach 1:
The system performs preliminary actions by establishing 5QI table entries with pre-determined PDB and MDBV attributes before actual TSN stream transmission. This allows the network to have QoS parameters ready in advance, enabling efficient mapping of TSN streams to appropriate 5G QoS flows without complex real-time calculations, thus improving radio resource allocation efficiency while managing configuration complexity.
Solution Approach 2:
The invention changes parameters by introducing traffic class-specific PDB and maximum payload volume information as new configuration parameters for 5G QoS flows. By modifying the QoS parameter set to include these specific attributes, the system enables accurate MDBV configuration that reflects actual TSN traffic requirements, resolving the contradiction between allocation efficiency and configuration complexity.
2Reliability
If payload capacity is increased to prevent TSN traffic loss, then reliability improves, but radio interface resources are wasted due to excessive allocation
Solution Approach 1:
The system applies local quality by configuring different MDBV values for different 5G QoS flows based on their specific TSN traffic class requirements. Instead of using a uniform high payload capacity for all flows, each flow receives precisely the payload volume it needs according to its traffic characteristics, ensuring reliable TSN transmission without excessive resource allocation. This localized customization resolves the contradiction between reliability and resource efficiency.
Solution Approach 2:
The invention uses partial action by allocating payload capacity that is exactly sufficient for TSN traffic requirements rather than providing excessive capacity. By determining traffic class-specific maximum payload volume and configuring MDBV accordingly, the system provides just enough resources to prevent traffic loss without the wasteful over-provisioning that would consume unnecessary radio resources.
3Productivity
If payload capacity is reduced to optimize resource allocation, then resource efficiency improves, but TSN traffic loss occurs due to insufficient capacity
Solution Approach 1:
The system performs preliminary determination of traffic class-specific maximum payload volume and configures appropriate MDBV values in 5QI table entries before TSN stream transmission. This advance configuration ensures that each 5G QoS flow has sufficient payload capacity pre-allocated based on actual traffic requirements, preventing TSN traffic loss while maintaining efficient resource allocation. The preliminary action resolves the contradiction by eliminating the need for conservative over-provisioning.
Solution Approach 2:
The invention implements feedback mechanisms where the system determines traffic class-specific information including maximum payload volume requirements, and uses this information to configure appropriate MDBV values. This feedback loop ensures that payload capacity is accurately matched to actual TSN traffic needs, maintaining both high resource allocation efficiency and reliable TSN traffic delivery without either waste or insufficiency.
Data Source
AI summary
Systems and methods for Quality of Service (QoS) mapping based on latency and throughput are provided. A method performed by a first node for mapping Time-Sensitive Networking (TSN) streams includes: receiving traffic class specific information for one or more TSN streams; and determining a set of one or more 5G QoS flows to support the traffic class. This provides solutions where 5QI table entries configured by a 5GS can result in underproviding or overproviding the payload capacity made available to support the traffic class. If less payload space is provided, then TSN stream payload corresponding to the traffic class will be lost. If more space is provided, then radio interface resources will be used inefficiently. As such, the solutions identified herein allow for more efficient use of radio interface resources associated with a 5G QoS flow used to support a set of TSN streams corresponding to a TSN traffic class.