AI Traffic Management Is Not an Employment Threat

We are becoming too many. Billions of phone users worldwide generate data, more importantly, create traffic. Telecom operators need help handling such traffic, and AI is here to help. In the current digital age, telecom services are a necessity. Telecommunication is a necessity for all essential services. Telecom companies must make sure their services are consistently available and uninterrupted. AI traffic management has become a helpful tool to ensure optimal operations. Managing telecom traffic is crucial since it allows for better operations at lower costs leading to better service overall.

Predictive Maintenance

You can train an ML algorithm to identify faults in different network devices, such as switches and routers, and then forecast when these faults will occur using just a few lines of code. You can instantly generate an alert when a flaw is found. The engineers’ time is freed up to concentrate on fixing issues rather than recording alerts and completing service tickets and support requests.

Telcos can use AI traffic management systems to trigger a predictive maintenance operation at specific points along your supply chain. For example, when new routers come into your data center, you could train an algorithm to detect when one stops sending traffic or has downtime that exceeds normal operating levels. This way, you know whether it’s time for routine maintenance or something more serious like hardware failure needs to be investigated further. By using predictive analytics to automate some basic maintenance tasks, you can save operational costs by reducing unscheduled downtime and increasing operational efficiency.

AI algorithms are good at spotting unusual circumstances. An AI algorithm can learn from data what normal and abnormal conditions are to detect deviations from the ideal functioning. Identifying hardware or software errors, unusual traffic patterns, traffic jams, intrusions, etc., are all examples of anomaly detection. Higher uptime, better service quality, reduced maintenance costs, and better risk management are just a few advantages of predictive maintenance.

Fault Detection and Resolution

In a telecom network, service interruptions and errors are unavoidable, making this a crucial area where AI can play a significant role. A functional network is more important than network optimization, which may be a secondary concern. High costs will frequently accompany faults, whether associated with operations and upkeep expenses or penalties for SLA violations.

Three KPIs for evaluating fault detection, prediction, and resolution arise in light of these issues:

1. Labor costs: the price of hiring engineers to repair the issue and customer service representatives to handle complaints

2. Quickness: measured as mean time to repair, this refers to how rapidly the telco can locate the issue and, consequently, fix it (MTTR)

3. Consumer encounter

All of the use cases we will go over attempt to directly or tangentially target one of these three KPIs.

AI to Optimize a Network

Network optimization deals with distributing workloads among the available infrastructure and resources to offer the highest quality service at the lowest cost. A manual network optimization is an option, but with thousands of radio sites, doing so would require the entire staff of network engineers to focus solely on network re-optimization. The advantage of a self-optimizing network that uses a process analogous to that for fault resolution is as follows:

The service degradation is highlighted by real-time, event-based network data and is related to a particular underlying cause (e.g., a sharp rise in traffic in a specific area)

To determine what the operator’s pre-defined intent is in the specific circumstance, the recommendation engine consults the policy engine (e.g., deliver as high quality of service as possible)

The recommendation engine then makes recommendations regarding which fixed policies, also kept in the policy engine, to apply to fulfill the intention while observing any limitations (e.g., boot up any assets on standby, re-route some traffic through longer paths to reduce congestion, prioritize SMS and calls over video streaming, etc.)

According to the suggestions, network equipment is re-optimized by an automatic system.

Concluding Thoughts

AI has become a big part of various sectors. Telecom is no different. Network optimization consists of several tasks that can be monotonous and tedious. And they require a predictive element. AI is coming to fill that gap and keep the human element away from boring tasks. Rest assured. AI traffic management is not here to take over traffic management in telecom; it’s merely there to help you.

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