AI in Telecommunications
Opportunities and Challenges Ahead
Why AI ?
Infrastructure and end users (customers) are significantly growing for telecom service providers as a result of the development of the telecommunications generations from 1G to 5G until today and the upcoming generations (6G). The two most important factors from a business standpoint for service providers are quality of service and zero downtime. With growing data velocity, AI can assist or detect data anomalies.
AI models can be developed and used to successfully identify breaches based on patterns and automate the detection of anomalies from expected behaviour. This will aid in early detection, forecasting, and resolutions without compromising an organisation’s important operational areas.
It is challenging to identify anomalies manually, so software systems with artificial intelligence/machine learning can play a crucial role in reading data, identifying anomalies automatically, and helping to identify the precise root cause of the issue to take quicker actions for resolutions.
Potential of AI in Telecommunications
Traditional applications and AI based applications
Almost it is challenging to define threshold limits for different KPIs or metrics, manual threshold limits must be specified in the traditional application for anomaly detection. For telecom service providers to offer their customers better services, this requires constant manual monitoring. As telecom generations progress, infrastructure will grow adequately, necessitating greater workforce for ongoing monitoring in all areas relating to RAN (Radio Access Network) as well as applications across all telecom generations.(2G,3G,4G,5G)
AI can analyse the data across several dimensions for auto anomaly detection. Organizations can reduce a huge amount of time by automatically analysing the data for fault anomalies, correlation of anomalies across domains, and root cause analysis in order to restore services and identify service impact scenarios or outages.
Strengths:
Autonomous monitoring is more accurate. AI-powered anomaly detection is 100% autonomous for 100% of the data. Rather than setting manual thresholds, these solutions rely on machine learning algorithms to autonomously create a dynamic baseline for each metric. They continuously analyse 100% of the network data (regardless of the CSPs original data scheme or silos) to understand normal metric behaviour under different conditions and seasons.
Real-time analysis provides faster time to resolution. Correlations are crucial for understanding metrics in context, and with dynamic baselines for network data, advanced anomaly detection can correlate incidents to root causes faster than traditional monitoring tools. Events are correlated across metrics, dimensions and other concurrent processes. Once root causes are identified, real-time analysis provides a prioritized set of opportunities to cut time to remediation.
Cross-silo monitoring provides holistic visibility of the network. By correlating between metrics across network layers, applications, databases, storage, CRMs, monitoring and analytics tools, advanced monitoring solutions sees beyond traditional data silos, enabling faster time to resolution, improved network availability and a streamlined customer experience.
New AI products or tools are being developed in everyday life to handle large amount of data of various contexts. Few such products in market are Anodot, Erricson E-ADF, JIO ATOM, Nokia AVA Tool,
Challenges:
Organised Data:
CSPs generate large amount of data and it is crucial that data is clean and organised.
Data Velocity:
Rate of change of data and timeliness are crucial challenges faced which requires continuous training and upgrading of models.
Regulatory and ethical considerations:
AI in telecom involves processing sensitive data, which raises concerns around privacy and security. Additionally, there are regulatory considerations around the use of AI in the telecom industry, which companies need to navigate carefully.
Lack of standardization:
There is currently no industry-wide standard for AI implementation in telecom, which means that different companies are using different AI technologies and approaches. This lack of standardization makes it difficult for companies to benchmark their performance against others and to collaborate effectively.
DoT’s AI Standardisation Committee’s AI Stack paper highlights five major horizontal pillars and one main vertical pillar — thus covering some of the most crucial aspects in AI deployment today including security, data storage, privacy, customer experience and computing.
Reference list:
· Anodot ,What is anomaly detection in telecom networks https://www.anodot.com/learning-center/network-anomaly-detection/#:~:text=AI%2Dbased%20anomaly%20detection%20solutions,reduction%20and%20root%20cause%20analysis.
· Barrera, N. (2020). How to make anomaly detection more accessible. https://www.ericsson.com/en/blog/2020/7/how-to-make-anomaly-detection-more-accessible .
· DoT’s AI Standardisation Committee releases Indian AI Stack discussion paper. https://indiaai.gov.in/news/dot-s-ai-standardisation-committee-releases-indian-ai-stack-discussion-paper
· Jio Platforms Ltd. (n.d.). Automation & AI/ML Platforms. https://www.jio.com/platforms/offerings/automation-platforms
· Nokia. (n.d.). AVA Anomaly Detection in Telecom. https://www.nokia.com/networks/bss-oss/anomaly-detection/.
· NVIDIA (n.d.). Applications of AI for Anomaly Detection. https://www.nvidia.com/content/dam/en-zz/Solutions/deep-learning/deep-learning-education/Applications_of_AI_for_Anomaly_Detection.pdf
