CoMP Radio Clustering With Dynamic Scheduler Reallocation
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Solution Overview
Problem
The increasing demand on telecommunications networks, particularly in Cooperative Multi-Point (COMP) deployments, leads to increased energy usage, computing resource consumption, and infrastructure complexity, with scheduler compute capacity often becoming a bottleneck, necessitating static overprovisioning that results in wasted resources during low-demand periods.
Innovation Solution
Dynamic clustering of radio devices and corresponding scheduler instances based on network utilization, allowing for re-allocation and modification of processing resources, including powering down or adding scheduler instances as needed to match demand fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static overprovisioning of scheduler compute capacity is used to ensure network can cope with peak demand, then network reliability during peak periods is improved, but resource waste increases during low-demand periods
Solution Approach 1:
The patent implements dynamic clustering by continuously monitoring network conditions and adjusting cluster configurations in real-time. Scheduler instances are dynamically allocated to clusters based on current demand, allowing the system to adapt from static overprovisioning to dynamic resource allocation that matches actual network conditions.
Solution Approach 2:
The system changes operational parameters by adjusting the number of active scheduler instances and cluster configurations based on demand thresholds. When demand falls below thresholds, scheduler instances are deactivated or reassigned, transforming the fixed capacity parameter into a variable parameter that responds to network conditions.
2Productivity
If increased network infrastructure and computing capacity are deployed to support more connected devices, then network capacity is improved, but infrastructure complexity increases
Solution Approach 1:
The patent segments the network into dynamic clusters that can be independently managed and scaled. Instead of deploying monolithic infrastructure, the system divides functionality into smaller, flexible cluster units that can be activated or deactivated based on demand, reducing overall infrastructure complexity.
Solution Approach 2:
Scheduler instances are designed as multi-functional components that can serve multiple clusters dynamically. A single scheduler instance can be reassigned to different clusters as needs change, making the infrastructure more versatile and reducing the total number of dedicated components required.
3Productivity
If scheduler compute capacity is increased to handle more radio devices, then network throughput is improved, but processing resource consumption increases
Solution Approach 1:
The system dynamically adjusts processing resource allocation by activating or deactivating scheduler instances based on real-time cluster utilization. When clusters have low device counts, corresponding scheduler instances are reduced or shut down, eliminating unnecessary processing resource consumption while maintaining throughput capability when needed.
Solution Approach 2:
The clustering system automatically monitors and adjusts its own resource consumption based on network conditions without external intervention. Utilization metrics trigger automatic scheduler instance allocation changes, allowing the system to self-optimize the balance between throughput capability and processing resource usage.
Data Source
AI summary
A computer-implemented method is described, for performing dynamic clustering of radio devices in a Cooperative Multi-Point network, the network comprising a plurality of radio devices, one or more clusters of radio devices of the plurality of radio devices, one or more scheduler instances each associated with a cluster of the one or more clusters, and a controller. The method includes: determining that a re-clustering condition associated with utilisation of one or more radio devices in a given cluster is satisfied; in response to determining that the re-clustering condition is satisfied: signalling a scheduler instance associated with the given cluster to remove the one or more radio devices from the given cluster; changing a processing resource allocation allocated for executing the one or more scheduler instances; and signalling a second scheduler instance to adopt the one or more radio devices.