Virtualized DU Resource Allocation Using RU Timing Margin Feedback
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Solution Overview
Problem
Existing wireless communication networks face challenges in efficiently managing virtualized processing resources for radio units due to strict radio timing requirements, leading to inefficiencies and the need for over-dimensioning to meet worst-case scenarios, which is not suitable for cloud computing environments.
Innovation Solution
A resource management entity dynamically adjusts the allocation of virtualized processing resources for digital units based on the processing margin at radio units, using feedback loops to ensure compliance with radio timing constraints, thereby optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static allocation of virtualized processing resources is used to meet worst-case scenarios, then reliability is improved, but resource efficiency deteriorates due to over-dimensioning
Solution Approach 1:
The patent implements dynamic resource allocation where the resource management entity continuously monitors processing margins and adjusts the allocation of virtualized processing resources to the digital unit in real-time. This replaces static over-provisioning with adaptive provisioning that matches actual demand, ensuring timing compliance while optimizing resource utilization.
Solution Approach 2:
The system establishes a feedback loop where the resource management entity receives information about processing margins from the digital unit and radio unit, then uses this feedback to adjust resource allocation. This closed-loop control ensures that resource allocation responds to actual processing needs while maintaining timing requirements.
2Adaptability or versatility
If cloud computing virtualization is implemented, then resource flexibility is improved, but timing precision deteriorates due to non-deterministic processing
Solution Approach 1:
The patent uses feedback from processing margin measurements to actively control and compensate for the non-deterministic nature of virtualized processing. By continuously monitoring actual processing performance and adjusting resource allocation accordingly, the system maintains timing precision despite the inherent variability of cloud computing environments.
Solution Approach 2:
The system dynamically changes resource allocation parameters based on observed processing margins. When processing margins indicate sufficient headroom, resources can be reduced; when margins tighten, resources are increased. This parameter adaptation allows the system to maintain timing precision while optimizing for flexibility and efficiency.
Data Source
AI summary
Example methods and apparatus disclosed herein use the “processing margin” at a radio unit (RU) (24) as a control variable for modifying the allocation of virtualized processing resources used by a digital unit (DU) (16) that supports the RU (24). The DU (16) and RU (24) form a radio processing chain with a functional split, in which the DU (16) performs certain processing operations and provides corresponding processed data to the RU (24), which performs further processing for the generation and transmission of the corresponding radio signals. The “slack” between the end of processing in the RU (24) and the corresponding required transmission time represents the processing margin. The processing-margin feedback loop allows for the virtualization of all or part the DU (16), while avoiding the need for the network operator to implement or pay for over-dimensioning of the DU (16), as would be needed to ensure that a static resource allocation meets worst-case processing needs.