Core Network Traffic Pattern Classification for SPS
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Radio communication networks face inefficiencies in supporting emerging applications like smartphone apps due to their different traffic patterns, which are not effectively managed by existing network resources designed for traditional voice and data applications.
Innovation Solution
A method where a Core Network node assists a Radio Access Network node in performing Semi Persistent Scheduling (SPS) by monitoring data traffic, classifying traffic patterns, establishing traffic shaping policies, and initiating dedicated bearer establishments to optimize resource allocation based on periodicity, using techniques like Deep Packet Inspection (DPI) to identify and aggregate periodical traffic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional scheduling methods are used for radio resources, then voice and data applications are supported, but emerging smartphone applications with periodical traffic patterns cannot be efficiently supported
Solution Approach 1:
The patent implements dynamic scheduling by transitioning from static scheduling methods to Semi-Persistent Scheduling (SPS) that adapts to the periodic nature of smartphone application traffic. The system dynamically identifies periodical traffic patterns and applies SPS configurations with appropriate periodicities, allowing the scheduling mechanism to evolve from traditional continuous scheduling to pattern-based semi-persistent scheduling, thereby improving resource allocation efficiency for emerging applications
Solution Approach 2:
The patent changes scheduling parameters by introducing SPS-specific parameters such as periodicity values, resource allocation patterns, and traffic pattern classifications. The system modifies scheduling behavior by adjusting these parameters based on identified traffic characteristics, enabling efficient support for periodical traffic patterns while maintaining compatibility with traditional scheduling for other application types
2Productivity
If Semi Persistent Scheduling is implemented without traffic pattern classification, then resource allocation may be optimized, but control resource overhead increases due to lack of targeted scheduling
Solution Approach 1:
The patent segments traffic into distinct categories based on periodicity characteristics (periodical, non-periodical, and uncertain traffic patterns). This segmentation allows the system to apply SPS selectively only to periodical traffic patterns that benefit from it, while using traditional scheduling for other traffic types, thereby reducing unnecessary control resource overhead while maintaining resource allocation efficiency for appropriate application scenarios
Solution Approach 2:
The patent implements feedback mechanisms through Deep Packet Inspection (DPI) that continuously monitors and classifies traffic patterns. The system uses this feedback to dynamically determine when to apply SPS configurations and when to revert to traditional scheduling, enabling intelligent control of resource allocation and minimizing physical layer control overhead by making informed scheduling decisions based on actual traffic characteristics
3Measurement precision
If Deep Packet Inspection is used to identify traffic patterns, then accurate classification is achieved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent applies partial DPI by focusing inspection efforts specifically on identifying periodical traffic patterns rather than performing comprehensive deep inspection of all traffic characteristics. The system implements targeted pattern recognition that looks for specific periodicity signatures in the traffic flow, achieving sufficient classification accuracy for scheduling decisions without the full computational overhead of exhaustive deep packet inspection across all traffic dimensions
Data Source
Figure 1
Figure 2
Figure 3
AI summary
This disclosure relates to radio communication. In particular, this disclosure relates to methods and means (e.g. a Core Network (CN) node) for assisting a RAN node (e.g. eNB) in performing Semi Persistent Scheduling (SPS). According to an example embodiment, the CN node is operative to: monitor (101) data traffic pertaining to a UE; determining (102) a traffic pattern based on the monitored data traffic; classify (103) the determined traffic pattern into different categories based on the determined traffic pattern; establish (104) a traffic shaping policy for each category of the different categories; and initiate (105A/105B) a dedicated bearer establishment for thereby enforcing the established traffic shaping policy.