Predictive 5G UPF Load Balancing With Network Data Analytics

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Solution Overview

Problem

5G networks face potential overloading in certain portions due to increased demand from smartphones and IoT devices, necessitating efficient UPF load balancing to maintain network performance.

Innovation Solution

Implement predictive UPF load balancing using network data analytics to select an optimal UPF for anchoring PDU sessions based on current and predicted loads, considering geographical proximity and time-sensitive predictions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If 5G networks increase bandwidth and speed to meet growing demand from smartphones and IoT devices, then content transmission performance is improved, but certain portions of the network may become overloaded

Engineering Contradiction:

Improvecontent transmission speedVSAvoidnetwork load balance

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting future UPF load conditions before actual overload occurs. The NWDAF analyzes current load metrics and generates predictions about future network states, allowing the SMF to proactively select optimal UPFs for PDU session anchoring before overload conditions develop, thus preventing reliability issues while maintaining high transmission speeds

Inventive Principle:

Principle #10Preliminary action

2Device complexity

If traditional load balancing methods are used without predictive analytics, then system complexity is reduced, but network performance degradation occurs during peak demand periods

Engineering Contradiction:

Improveload balancing system complexityVSAvoidnetwork throughput

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The NWDAF serves as an intermediary component that bridges the gap between simple load monitoring and complex predictive analytics. It collects load information from UPFs, performs predictive analysis using machine learning algorithms, and provides recommendations to the SMF. This intermediary layer enables advanced productivity optimization through predictive insights while keeping the core load balancing logic in the SMF relatively simple and manageable

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of time

If UPFs are selected based solely on current load status, then response time is reduced, but future overload conditions cannot be prevented

Engineering Contradiction:

ImproveUPF selection response timeVSAvoidpreventive load management

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250254565A1Predictive user plane function (UPF) load balancing based on network data analytics

Publication Date: 2025.08.07 DISH WIRELESS LLC

AI summary

Embodiments are directed towards systems and methods for selecting, in a Fifth Generation (5G) cellular telecommunication network, a User Plane Function (UPF) of a plurality of UPFs on which to anchor a Protocol Data Unit (PDU) session of a new user equipment (UE) newly appearing on the cellular telecommunication network. The selection is based on: a location of the new UE; a plurality of current loads for each UPF of the plurality of UPFs; a predicted UE load of the new UE based on network data analytics; and predicted UPF loads of the plurality of UPFs as a function of time considering the predicted UE load based on network data analytics from the Network Data Analytics Function. In the UPF selection, the Session Management Function (SMF) gives higher priority to shorter term predicted loads than longer term predicted loads.