Service Assurance in Telecommunications with AI
The telecommunications industry is currently witnessing remarkable growth, characterized by an increasing number of connected devices and a surging demand for top-notch connectivity. In such a landscape, ensuring service assurance is crucial for maintaining a competitive advantage.
Service assurance encompasses a range of processes, systems, and tools aimed at guaranteeing that telecom services align with the established service level agreements (SLAs) and customer expectations. This includes overseeing and managing network performance, service quality, and fault detection and resolution.
Communication Service Providers’ (CSPs) network operations center (NOC) have evolved significantly, transitioning from a reliance on subject matter experts analysing alarms and faults, to a rule-based approach where expert logic is documented to aid engineers in various situations. However, as networks become larger and more complex, this approach becomes increasingly difficult to scale.
Historically, CSPs have focused on resolving network issues as they occur, a practice known as Reactive Maintenance. This method, although effective in addressing individual incidents, often results in longer downtimes and increased maintenance costs. As a result, there has been a growing emphasis on implementing proactive maintenance strategies to better anticipate and prevent network issues.
To address this challenge, CSPs are now shifting from these labour-intensive processes towards machine learning (ML) and automation. Before taking this step, it is crucial for CSPs to understand how AI software solutions can undertake such mission-critical tasks and assist them in maintaining network service and availability.
Artificial Intelligence (AI) has the potential to dramatically transform service assurance in the telecommunications sector through its ability to detect anomalies and offer predictive maintenance capabilities. As a result, telecom companies can achieve their goals of operational efficiency, cost reduction, and network automation. AI-driven solutions can analyze large volumes of data from multiple sources, identifying patterns and predicting potential issues before they develop into larger problems.
Avanseus’ Augmented Operations and Health & Performance Management solutions are designed to enhance human decision-making processes and decrease the amount of manual labour required over time as CSPs gain confidence in ML-based predictions and decisions. Avanseus has collaborated with subject matter experts and CSPs to develop and train ML models using historical data and real-time network information. These models can analyse complex network scenarios, identifying anomalies, and predicting potential issues with high accuracy.
The key features of Avanseus’ solutions include:
Anomaly Detection: Detecting deviations from normal network behavior to identify potential problems early on, enabling preemptive maintenance actions.
Predictive Maintenance: Leveraging AI and ML algorithms to predict potential network issues, allowing CSPs to take proactive steps to prevent service disruptions.
Root-Cause Analysis: Automatically diagnosing the underlying factors behind network problems, helping engineers to quickly resolve issues and minimize downtime.
Network Health Monitoring: Continuously monitoring the health and performance of network elements to ensure optimal operation and identify areas for improvement.
Adaptive Learning: Adapting the ML models to changes in the network environment and incorporating new data to continuously improve prediction accuracy and effectiveness.
Avanseus’ solution is designed to interwork and co-exist with existing equipment vendors’ specific fault and performance management systems, while also leveraging the service assurance features supported by these systems. This solution will deliver uniform and consistent predictive insights that assist CSPs in enhancing their decision-making and aligning actions across various network areas under monitoring and optimization.
As highlighted in the previous post, it is emphasized that the transformation journey necessitates CSPs to establish and record their fault and alert correlation policies tailored to their specific requirements. It is crucial to understand that not all faults and alarms hold equal significance and relevance; thus, CSPs must identify critical instances where the cost of failure outweighs inaction. Avanseus’ solution offers the capability to correlate and prioritize alarms, analyse fault impacts, and ascertain the root cause. These insights and actions empower CSPs to allocate optimal resources and processes for addressing critical events that may lead to service degradation or network outages.
A primary objective of this transformative journey is to automate network operations and management. Avanseus’ solution facilitates this goal by leveraging AI and ML algorithms to automate various processes, such as fault detection, root cause analysis, and trouble ticketing. This automation not only streamlines network operations but also reduces human intervention, leading to increased efficiency and cost savings.
The implementation of AI and ML driven service assurance approaches is becoming increasingly vital for CPSs as they aim to enhance network performance, minimize expenses, and boost customer satisfaction. By investing in these technologies, CSPs can transform their NOCs into more efficient and scalable organizations that are better equipped to handle the complexities of modern networks.
As AI and ML continue to advance, the potential for further innovation and optimized network management is considerable, making it essential for CSPs to remain at the forefront of adopting these cutting-edge solutions. By doing so, they can unlock the full potential of their networks and deliver the highest possible quality of service to their customers, while ensuring sustainable and profitable future for their businesses.
Ready to take your service assurance to the next level with AI-driven solutions? Contact us today to learn more.