Pre-emptive Maintenance Node for Cellular Network Reliability
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Solution Overview
Problem
Cellular radio access networks face challenges in maintaining quality of service (QoS) due to network failures and outages, which can lead to revenue loss and negative customer experiences, and existing methods are inefficient in predicting and preventing these issues.
Innovation Solution
The implementation of a Pre-emptive Maintenance Node (PEM) that monitors network elements, predicts potential failures, and proactively creates alternative network configurations to minimize the impact of maintenance operations, allowing for automated reconfiguration and scheduling of maintenance to avoid peak traffic times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proactive maintenance operations are performed to prevent network failures, then network reliability is improved, but network service interruption occurs during maintenance
Solution Approach 1:
The system performs preliminary actions by predicting potential network failures before they occur and scheduling maintenance operations in advance. The failure prediction module analyzes historical data and current network state to identify components at risk, allowing maintenance to be performed proactively rather than reactively, thus improving reliability while planning for minimal disruption.
Solution Approach 2:
The maintenance scheduling is dynamic and adapts based on predicted network traffic patterns and failure risks. The system continuously updates maintenance schedules by evaluating changing network conditions, traffic loads, and predicted failure probabilities, allowing optimization of maintenance timing to minimize service interruption while ensuring critical maintenance is performed.
2Ease of operation
If maintenance operations are scheduled during peak traffic times to minimize service interruption, then customer experience is improved, but maintenance effectiveness decreases
Solution Approach 1:
The system utilizes periodic network traffic patterns to schedule maintenance operations during off-peak periods when traffic load is naturally lower. By analyzing historical traffic data and predicting future traffic patterns, the system identifies optimal time windows for maintenance that align with periodic low-traffic periods, thus maintaining customer experience while ensuring maintenance effectiveness.
3Productivity
If automated reconfiguration is implemented to minimize maintenance impact, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements self-service capabilities through automated failure prediction and maintenance scheduling. The failure prediction module automatically analyzes network data, identifies potential failures, and generates maintenance schedules without human intervention. The maintenance scheduling module automatically adjusts schedules based on traffic patterns and constraints, enabling the system to manage its own maintenance needs efficiently while reducing the complexity burden on operators.
Data Source
AI summary
Enhanced quality of service of a cellular radio access network is provided by monitoring the operation of the network for predicting failures. For each of the predicted failures, a proactive maintenance plan is created and an alternative network configuration determined, in which alternative network configuration the impact of the planned maintenance operations is less than in the current (non-alternative) network configuration. Additionally, timing of the maintenance operations is decided based on a network traffic estimate and the network is automatically reconfigured into the alternative network configuration prior to the selected maintenance operation time. According to an embodiment, the object is achieved by means of a Pre-emptive Maintenance Node (PEM) connected to the telecommunications network, such as to an LTE or LTE-A network.