Traffic Volume Prediction for Dynamic 5G Resource Allocation
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Solution Overview
Problem
Existing network optimization mechanisms fail to address the dynamic resource allocation needs of 5G services, leading to improper allocation of radio resources, which affects service quality and user experience due to static allocation strategies.
Innovation Solution
Implement a traffic volume prediction method to dynamically allocate resources based on predicted traffic volumes, considering prediction granularities and periods, using management devices to collect and analyze historical data to forecast traffic demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static resource allocation is used, then network operation and maintenance becomes simpler, but resource allocation becomes improper leading to excessive or insufficient radio resources at certain time points and areas
Solution Approach 1:
The patent implements dynamic resource allocation by predicting traffic volumes and adjusting radio resource allocation accordingly. The system transitions from static pre-allocation to dynamic allocation based on predicted traffic patterns, allowing resource quantities to adapt to changing network conditions while maintaining operational simplicity through automated prediction and allocation mechanisms.
2Reliability
If network optimization is performed after performance deterioration is detected, then network optimization measures can be taken, but service requirements are affected during the time-consuming optimization process
Solution Approach 1:
The patent applies preliminary action by predicting traffic volumes in advance and proactively allocating resources before performance deterioration occurs. Instead of reacting after congestion is detected, the system forecasts future traffic patterns and pre-allocates radio resources to prevent service degradation, eliminating the time-consuming optimization process and its associated service impacts.
3Device complexity
If radio resources are statically allocated, then resource allocation is simpler to manage, but service requirements are affected due to excessive or insufficient resources at certain time points
Solution Approach 1:
The patent implements self-service by enabling the network system to automatically predict its own traffic volumes and allocate resources without external intervention. The automated prediction and allocation mechanisms allow the system to self-adjust resource distribution based on forecasted demands, reducing management complexity while simultaneously improving service quality through appropriate resource provisioning.
Data Source
AI summary
A method includes receiving, by a first management device, a first message from a second management device, and determining a predicted traffic volume corresponding to a traffic type for a predicted object based on a prediction requirement information. The first message is useable to request the first management device to perform traffic volume prediction. The first message includes the predicted object, the traffic type, and the prediction requirement information. The prediction requirement information includes a prediction granularity or a prediction period. The prediction granularity includes at least one of a prediction area, a prediction service type, a prediction slice, a prediction cell, a prediction public land mobile communications network or a prediction tenant.