Strategies for Telecom Operators to Boost Operations through AI in 2024:
Artificial Intelligence (AI) Transforming the Telecom Landscape: From Network Planning to Optimization and Fault Resolution
In the fast-paced telecom sector, AI has emerged as a game-changer, promising to reshape the landscape while driving efficiency and enhancing quality for users. With almost two-thirds (60%) of C-suite executives planning to integrate AI into their operations by 2024, the potential is undeniable.
This piece dives into how AI will shake up the telecom world by focusing on three critical areas: network planning, optimization, and fault identification and resolution.
Network Planning
AI can revolutionize network planning by offering a heightened level of responsiveness and the ability to correlate numerous factors. The demand for operators to keep pace with demands is typically fulfilled by relying on historical data to forecast growth. However, human planners often struggle to recognize emerging patterns and deviations from past trends. AI can help transcend these limitations by employing cutting-edge algorithms to analyze enormous datasets in real-time, enabling operators to accurately anticipate changing demands and optimize network architecture.
The added capability allows AI to trigger capacity upgrades in specific locations and optimize network infrastructure accordingly. This is why a recent study found that 70% of solution providers foresee the highest returns from AI adoption in network planning. Moreover, AI can identify underserved areas and develop targeted deployment strategies to address network disparity.
However, AI must tackle concerns over data privacy, algorithmic biases, and the need for skilled humans to interpret the results. Additionally, it's challenging to introduce AI into existing systems and ensure compatibility with legacy infrastructures. As a solution, the rise of disaggregated systems may pave the way for more effortless integration.
Network Optimization
Telcos traditionally struggle with network optimization due to its manual and labor-intensive nature, complicated by the massive volume of nodes, equipment types, and subscribers. However, AI systems have transformed these tasks by leveraging real-time data to predict user behaviour and fine-tune network performance accordingly. Consequently, the same network team can now manage networks that are four times larger than before with the use of AI. This enables telcos to proactively adjust bandwidth allocation, optimize network traffic, and minimize congestion in real-time, resulting in improved user experiences and enhanced operational efficiency.
Fault Resolution
Faults and equipment failures are inevitable in any network. Fortunately, by using AI for detecting hidden faults and identifying complex root causes, the risk of failures can be significantly reduced. This empowers telecom providers to take preventive steps to fix problems and prevent outages. For example, some companies use AI to predict network congestion and reroute traffic proactively to avoid outages. Some CSPs are even building self-optimizing networks (SONs) to support this growth, allowing them to optimize network quality based on region and time zone, resulting in networks that adapt dynamically to changing conditions. Overall, AI's most significant strength lies in its ability to predict and proactively resolve faults before they occur, thereby enhancing network reliability and minimizing disruptions.
AI in Disaggregated Networks
For AI to reach its full potential in improving networks, how can we ensure that it doesn't fall behind? Network disaggregation, which divides hardware and software components, offers a straightforward and rapid data source for networks. By using bare-metal switches and managing hardware with software from various vendors, AI can access more data at higher speeds. Moreover, the cloud-native Network Operating Systems (NOS) can simplify processes by enabling AI systems to subscribe to events and receive instant notifications, allowing for faster responses to network changes. The microservices inherent in a cloud-native NOS offer visibility into network functions, making it easier for AI to learn behaviors, correlate interactions, and facilitate predictive maintenance, fault diagnosis, resource optimization, and threat prevention. The quality of input data significantly impacts AI performance, emphasizing the crucial role of network disaggregation in enhancing AI capabilities within telecommunications.
In conclusion, as with any venture, the quality of input directly impacts the output. This also holds true for AI operations. With network disaggregation, AI can help prioritize quality data input to ensure optimal benefits for telcos and innovative experiences for consumers.
-Hannes Gredler, CTO, RTBrick
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In the realm of network planning, AI's adoption is anticipated to generate the highest returns, revolutionizing the telescopic landscape by employing real-time analysis and predictive capabilities, enabling operators to optimize network architecture and address network disparities.
AI's ability to transform fault resolution processes in the telecom sector is noteworthy, as it empowers providers to proactively predict and resolve faults before they occur, thereby enhancing network reliability and minimizing disruptions.
