MEC Resource Coordination via RF Signal Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless network technologies face challenges in dynamically managing Multi-Access/Mobile Edge Computing (MEC) resources based on varying signal strength metrics, leading to inefficient resource allocation and potential service disruptions due to user equipment (UE) movement.
Innovation Solution
The dynamic configuration of MEC resources is achieved by using radio frequency (RF) metrics such as signal strength, SINR, RSRP, RSRQ, and CQI to adjust resource allocation and deallocate or 'age' resources as UE moves, ensuring seamless service continuity while conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MEC resources are statically allocated without considering signal strength metrics, then resource allocation is simple and stable, but resource utilization efficiency deteriorates and service continuity is compromised when UE moves between service regions
Solution Approach 1:
The patent implements dynamic MEC resource allocation by continuously monitoring signal strength metrics (RSRP, RSRQ, SINR) and adjusting resource allocation in real-time based on UE movement and signal conditions. This transforms the static resource allocation system into a dynamic one that adapts to changing network conditions, thereby improving resource utilization efficiency without requiring complete redesign of the resource management framework
Solution Approach 2:
The system establishes a feedback mechanism where signal strength metrics from the radio access network are continuously collected and used to trigger resource allocation decisions. When signal strength thresholds are breached or metrics degrade, the system automatically initiates resource allocation adjustments or UE handover procedures, creating a closed-loop control system that improves productivity through responsive resource management
2Reliability
If MEC resources are dynamically adjusted based on signal strength metrics, then resource utilization efficiency improves and service continuity is maintained, but system complexity and coordination overhead increase
Solution Approach 1:
The patent introduces a MEC resource coordination function that acts as an intermediary between the radio access network and edge computing resources. This intermediary monitors signal strength metrics, determines when resource adjustments are needed, and coordinates allocations across multiple MECs, thereby maintaining service continuity while managing coordination complexity through a centralized decision-making layer
Solution Approach 2:
The system performs preliminary resource allocation adjustments before UE handover is triggered by signal degradation. By proactively allocating resources at target MECs based on predicted UE movement patterns and signal trends, the system ensures seamless service continuity while reducing the complexity of last-minute coordination during actual handover events
3Reliability
If resource allocation is increased for all UEs regardless of signal strength, then user experience is maintained, but resource waste increases and network efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating resource allocation based on individual UE signal strength conditions and service requirements. Instead of uniform resource allocation, the system adjusts resource allocation locally for each UE based on its specific signal metrics, service type, and mobility pattern, thereby maintaining quality user experience for those who need it while avoiding resource waste for UEs with strong signals or low service requirements
Solution Approach 2:
The system dynamically changes resource allocation parameters (CPU resources, memory, bandwidth) based on signal strength metrics and service requirements. By adjusting these parameters in real-time according to actual network conditions rather than using fixed allocations, the system maintains user experience quality when needed while reducing resource consumption during periods of strong signal or low service demand
Data Source
AI summary
A system described herein may monitor, via a Central Unit (“CU”) of a radio access network (“RAN”), radio frequency (“RF”) metrics associated with a User Equipment (“UE”). The monitored RF metrics may be based on communications between the UE and a Distributed Unit (“DU”) that is communicatively coupled to the CU. The system may identify a Multi-Access/Mobile Edge Computing (“MEC”) device that is communicatively coupled to the DU and that provides one or more services to the UE via the DU. The system may resource allocation parameters for the MEC based on the RF metrics between the UE and the DU. The system may instruct the MEC to implement the set of resource allocation parameters. The MEC may modify an allocation of MEC resources, allocated for providing the one or more services to the UE, based on the determined set of resource allocation parameters.