Radio Access Network Resource Scheduling for Time-Sensitive Communication
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Solution Overview
Problem
Current wireless communication systems for Time-Sensitive Communication (TSC) rely solely on Maximum Data Burst Volume (MDBV) for Quality of Service (QOS) flows, leading to over-dimensioning of resources, which is inefficient for high data rate applications, negatively impacting system capacity and limiting the number of supported users.
Innovation Solution
Introducing additional parameters such as minimum data burst volume, data burst volume range, statistical properties, and distribution to provide a more accurate representation of data volume characteristics, allowing for dynamic and optimal scheduling of radio resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only Maximum Data Burst Volume (MDBV) is used for QoS flows, then resource reservation ensures worst-case coverage, but resource over-dimensioning occurs leading to reduced system capacity
Solution Approach 1:
The patent introduces additional parameters (minimum data burst volume, data burst volume range, statistical properties, distribution characteristics) to complement the existing MDBV parameter. This transforms the single-parameter resource allocation approach into a multi-parameter system, enabling more precise resource dimensioning that matches actual traffic patterns while maintaining QoS guarantees.
Solution Approach 2:
The patent enables dynamic resource allocation by providing the RAN with statistical properties and distribution information of data bursts. This allows the scheduling algorithm to adapt resource allocation in real-time based on actual traffic characteristics rather than static worst-case assumptions, improving system capacity while maintaining reliability.
2Ease of operation
If MDBV is used for all QoS flows, then resource allocation is simplified, but resource allocation accuracy decreases for high data rate applications
Solution Approach 1:
The patent enhances the parameter set from a single MDBV value to include minimum data burst volume, data burst volume range, statistical properties (mean, standard deviation), and distribution characteristics. This provides the RAN with a comprehensive view of actual traffic patterns, enabling accurate resource allocation for high data rate applications while maintaining operational simplicity through standardized signaling.
3Reliability
If resource over-dimensioning is applied to ensure MDBV coverage, then QoS reliability is maintained, but the number of supported users is limited
Solution Approach 1:
The patent enables dynamic resource allocation where the RAN can adjust resource grants based on actual traffic patterns revealed through statistical parameters and distribution information. This allows the system to support more users by allocating resources dynamically rather than reserving excessive resources statically, thereby increasing the number of supported users while maintaining QoS compliance.
Solution Approach 2:
By introducing statistical properties and distribution characteristics as additional parameters, the patent enables the RAN to calculate more accurate resource requirements. This reduces the safety margin needed for QoS guarantee, allowing resources to be efficiently shared among more users while maintaining reliability through statistically-informed allocation.
Data Source
AI summary
Systems and methods are disclosed for data burst volume indication for Time Sensitive Communication (TSC). In one embodiment, a method performed by Radio Access Network (RAN) node comprises receiving, from a core network (CN) node, one or more parameters, in additional to a maximum data burst volume (MDBV) parameter, that describe one or more data volume characteristics of a Quality of Service (QOS) flow. In this manner, compared to always assuming the worst-case scenario with the MDBV, the RAN node has a more accurate understanding of the traffic characteristics, which allows the RAN node to optimally schedule radio resources to cater to the data volume.