Machine Learning Mobility Management for 5G Uplink Congestion
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Solution Overview
Problem
5G wireless networks experience uplink resource congestion leading to latency, accessibility degradation, and Quality of Experience (QoE) issues due to unavailability of uplink grants, resulting in repeated attempts by user equipment (UE) to reallocate Physical Uplink Control Channel (PUCCH) resources and potential service disruptions.
Innovation Solution
Implementing a neural network-based mobility management system that predicts uplink congestion using machine learning models to proactively move user equipment from congested cells to less loaded cells, utilizing centralized Radio Access Networks (RANs) to optimize handover decisions based on various parameters, including UE-specific inputs and base station information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the network provides uplink resources to all user equipment requests, then user accessibility is improved, but network congestion increases and service reliability deteriorates
Solution Approach 1:
The system performs preliminary detection of uplink congestion conditions using machine learning models before service degradation occurs. By predicting congestion based on historical data and current network state, the system can proactively implement load balancing measures, redirect users to less congested cells, or adjust resource allocation in advance, preventing service reliability deterioration rather than reacting after congestion occurs.
2Productivity
If the user equipment repeatedly attempts to reallocate PUCCH resources during congestion, then resource allocation flexibility is improved, but access latency increases and service continuity deteriorates
Solution Approach 1:
The system implements a feedback mechanism where the network monitors PUCCH resource allocation requests and congestion conditions in real-time. When congestion is detected, the network provides feedback to user equipment to pause or modify reallocation attempts, preventing unnecessary repeated attempts that increase latency. The machine learning model continuously learns from allocation outcomes and congestion patterns to optimize future resource allocation decisions, balancing flexibility with time efficiency.
3Reliability
If the network implements traditional congestion handling procedures, then service recovery is achieved, but service disruption time increases and user experience deteriorates
Solution Approach 1:
The machine learning model detects early signs of congestion and predicts service disruptions before they fully manifest. This preliminary detection enables the network to initiate recovery actions such as load balancing, cell reconfiguration, or user redirection in advance, significantly reducing service disruption time compared to traditional reactive approaches that only respond after service degradation is evident.
Solution Approach 2:
The system dynamically adjusts network parameters such as PUCCH resource allocation thresholds, handover conditions, and load balancing factors based on real-time congestion predictions from the machine learning model. These parameter changes enable adaptive service recovery that responds to actual network conditions rather than following fixed procedural timelines, reducing service disruption time while maintaining reliable recovery.
Data Source
AI summary
Embodiments of the disclosure provide a method for provisioning mobility management during congestion in a wireless network by a network apparatus. The method includes: detecting a plurality of parameters of a current cell associated with at least one User Equipment (UE) in the wireless network; predicting an uplink (UL) congestion condition with an uplink radio resource of the current cell by applying at least one machine learning model to the plurality of parameters of the current cell; performing a mobility of the at least one UE from the current cell to at least one target cell in the wireless network based on the predicted UL congestion condition with the uplink radio resource of the current cell associated with the at least one UE.