Network Slice Co-Processor Scheduling for Real-Time PHY Processing
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Solution Overview
Problem
Existing network slice technologies struggle to efficiently satisfy the diverse real-time performance requirements of different application scenarios without wasting resources and power.
Innovation Solution
A method and apparatus that utilize a co-processor to determine a target processing module based on a service identifier and S-NSSAI, enabling efficient physical layer processing for network slices, thereby optimizing resource and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If physical layer processing is performed by using a CPU, then general processing capability is maintained, but processing speed and real-time performance are insufficient
Solution Approach 1:
The patent segments the processing architecture by introducing a co-processor to handle physical layer processing tasks separately from the CPU. The data channel is divided into multiple processing modules that can be selectively activated based on network slice requirements, allowing specialized processing without overloading the general-purpose CPU.
Solution Approach 2:
The patent introduces a co-processor as an intermediary component between the CPU and the physical layer processing tasks. This co-processor acts as a mediator that offloads specific processing modules to dedicated hardware, improving processing speed while maintaining system manageability through controlled complexity.
2Adaptability or versatility
If co-processor resources are differentially scheduled for different network slices, then real-time performance requirements are satisfied, but resource allocation complexity increases
Solution Approach 1:
The patent implements dynamic resource allocation where the co-processor's processing modules are selectively activated based on the specific network slice requirements. Different target processing modules are determined based on S-NSSAI (Single Network Slice Selection Assistance Information), allowing the system to adapt processing resources dynamically to match real-time performance needs of different slices.
Solution Approach 2:
The patent applies local quality by assigning different processing module configurations to different network slices. Each network slice receives a customized set of processing modules tailored to its specific real-time requirements, rather than using a uniform processing approach for all slices.
3Productivity
If all processing modules are activated for all network slices, then processing capability is maximized, but resource and power waste occurs
Solution Approach 1:
The patent implements partial action by activating only the necessary target processing modules for each specific network slice rather than running all processing modules continuously. This selective activation ensures sufficient processing capability for each slice's requirements while avoiding the energy waste of running unnecessary processing modules.
Solution Approach 2:
The patent enables dynamic deactivation of processing modules when they are not needed for current network slice operations. Processing modules can be discarded (deactivated) when not required and recovered (reactivated) when needed, optimizing the balance between processing capability and power consumption.
Data Source
AI summary
This application provides a method and apparatus based on network slice communication and a terminal device. The terminal device determines, based on a first service identifier, first S-NSSAI used to indicate a first network slice, and determines, based on the first S-NSSAI, a target processing module of a data channel that is processed by using a co-processor. In this way, performing physical layer processing on data by using the co-processor and through the target processing module can significantly improve a processing speed and real-time performance. For S-NSSAI of different network slices, different target processing modules can be determined based on the different S-NSSAI, so that when resource and power are wasted due to non-differential scheduling of calculation resources of the co-processor in different network slice scenarios can be avoided, requirements of different application scenarios on real-time performance can be satisfied, and flexibility of a processing process can be improved.