Networking AI
Telecom networks generate massive amounts of data, and that is a gross understatement, so optimizing networks based on that information should be an easy task. Unfortunately it is not for a number of reasons, one being the amount of data is so large that it cannot be easily processed. Traditional manual or rule-based network management systems are insufficient for networks that are becoming increasingly complex and face consistently higher volumes of data, which leads to the concept of using AI or ML systems to utilize this vast base of network information to advance network reliability and efficiency.
That said, there are a number of stumbling blocks that offset the compelling case for AI network management and a recent survey of telecom decision makers by Fierce Networks indicated that the biggest challenge to adopting AI for network operations is that current legacy systems and infrastructure are not up to the task in terms of data consistency. Some telcos have already begun implementation of processes to organize and unify internal systems, but without a higher level of data uniformity, AI will be unable to manage much of the data and will fall short of goals. What typically has been a centralized or siloed approach to network data processing is not well suited to the real-time data needs of AI systems.
We note that telecom companies have been early AI adopters, but the rapid improvements in generative AI have pushed network managers to imagine AI as ‘the’ network manager, rather than a tool that sits on the network periphery. This entails building AI directly into the network core architecture, which means a fundamental rebuilding of networks with AI as the foundation rather than an add-on and that is a challenge both from a technical standpoint and a financial one, although the result would be the difference between a reactive network and ne that is predictive and self-optimizing. It might be a difficult business case to make in the short run but the ability of a network to anticipate failures and to proactively schedule maintenance should lower operating costs and extend equipment life over the long term.
The real objective however for most telecom decision makers is network optimization, essentially improving resource management and enhancing traffic management. ML systems that have demonstrated effective prediction abilities for network congestion using architectures like DRL (Deep Reinforcement Learning) are already being applied in 5G network slicing to enhance throughput and reduce latency by dynamically allocating resources and are proven to be superior to rules-based systems that are commonly used. As an additional plus, they can also reduce RAN energy consumption by dynamically deactivating frequency bands that are not in use.
AI can also be used to mine vast hordes of customer data, as current systems generate usage patterns, behavior inclinations, and feedback, something they are well equipped to do, and can use that data analysis to make recommendations that are tailored specifically for each customer based on their specific usage patterns. They can also anticipate potential issues that the customer has yet to realize. This can reduce churn and refocus the telco on building relationships rather than the more expensive process of constantly finding new customers.
In the same light as customer usage analysis, AI is also suited for security and fraud detection, able to spot anomalies that differ from learned traffic patterns or routing that differs from the norm. Ai is already in place as a fraud detection tool at a number of telcos, but again, it is built as an add-on and would be most effective as a core function that can quickly adapt to new exploits.
Again, the biggest choke point for Ai in telecom is the diversity of data sources that are part of current network architectures and poor data is going to lead to poor AI results. This points to a chicken-and-egg situation where the network industry’s own structural issues could be at the heart of an AI meltdown or at least disappointing results that could lead to a loss of faith in AI at the corporate level. Does one have to rip and replace existing network architecture to get the true benefits of AI, or is there a happy medium somewhere in between? 6G might be a savior, although its implementation is still years away, as its development will give organizations the ability to standardize systems and frameworks to avoid continuing the fragmented nature of the networking space and making AI/ML implementation more cost effective and efficient without compromising data privacy. Even in such a 6G scenario, AI might still have the issue of being an unproven ROI, but we see that as more an issue with the network operators rather than an issue with the capabilities of AI itself.
In fact the ideal solution, at least currently, is not the big infrastructure replacement and massive AI implementation but more of a service by service implementation that allows for small ‘wins’ that continue to build the confidence needed to continue the further expansion of AI, along with network architecture improvements. By gaining traction on a service basis, upper management can see that there is a tangible return on both AI and the network upgrades needed to embed Ai deeper in the core. AI in the network has many positive facets but it still has to justify its existence financially and there are only a very few who might have the confidence to rip and replace at this early stage in the AI game.
That said, there are a number of stumbling blocks that offset the compelling case for AI network management and a recent survey of telecom decision makers by Fierce Networks indicated that the biggest challenge to adopting AI for network operations is that current legacy systems and infrastructure are not up to the task in terms of data consistency. Some telcos have already begun implementation of processes to organize and unify internal systems, but without a higher level of data uniformity, AI will be unable to manage much of the data and will fall short of goals. What typically has been a centralized or siloed approach to network data processing is not well suited to the real-time data needs of AI systems.
We note that telecom companies have been early AI adopters, but the rapid improvements in generative AI have pushed network managers to imagine AI as ‘the’ network manager, rather than a tool that sits on the network periphery. This entails building AI directly into the network core architecture, which means a fundamental rebuilding of networks with AI as the foundation rather than an add-on and that is a challenge both from a technical standpoint and a financial one, although the result would be the difference between a reactive network and ne that is predictive and self-optimizing. It might be a difficult business case to make in the short run but the ability of a network to anticipate failures and to proactively schedule maintenance should lower operating costs and extend equipment life over the long term.
The real objective however for most telecom decision makers is network optimization, essentially improving resource management and enhancing traffic management. ML systems that have demonstrated effective prediction abilities for network congestion using architectures like DRL (Deep Reinforcement Learning) are already being applied in 5G network slicing to enhance throughput and reduce latency by dynamically allocating resources and are proven to be superior to rules-based systems that are commonly used. As an additional plus, they can also reduce RAN energy consumption by dynamically deactivating frequency bands that are not in use.
AI can also be used to mine vast hordes of customer data, as current systems generate usage patterns, behavior inclinations, and feedback, something they are well equipped to do, and can use that data analysis to make recommendations that are tailored specifically for each customer based on their specific usage patterns. They can also anticipate potential issues that the customer has yet to realize. This can reduce churn and refocus the telco on building relationships rather than the more expensive process of constantly finding new customers.
In the same light as customer usage analysis, AI is also suited for security and fraud detection, able to spot anomalies that differ from learned traffic patterns or routing that differs from the norm. Ai is already in place as a fraud detection tool at a number of telcos, but again, it is built as an add-on and would be most effective as a core function that can quickly adapt to new exploits.
Again, the biggest choke point for Ai in telecom is the diversity of data sources that are part of current network architectures and poor data is going to lead to poor AI results. This points to a chicken-and-egg situation where the network industry’s own structural issues could be at the heart of an AI meltdown or at least disappointing results that could lead to a loss of faith in AI at the corporate level. Does one have to rip and replace existing network architecture to get the true benefits of AI, or is there a happy medium somewhere in between? 6G might be a savior, although its implementation is still years away, as its development will give organizations the ability to standardize systems and frameworks to avoid continuing the fragmented nature of the networking space and making AI/ML implementation more cost effective and efficient without compromising data privacy. Even in such a 6G scenario, AI might still have the issue of being an unproven ROI, but we see that as more an issue with the network operators rather than an issue with the capabilities of AI itself.
In fact the ideal solution, at least currently, is not the big infrastructure replacement and massive AI implementation but more of a service by service implementation that allows for small ‘wins’ that continue to build the confidence needed to continue the further expansion of AI, along with network architecture improvements. By gaining traction on a service basis, upper management can see that there is a tangible return on both AI and the network upgrades needed to embed Ai deeper in the core. AI in the network has many positive facets but it still has to justify its existence financially and there are only a very few who might have the confidence to rip and replace at this early stage in the AI game.
RSS Feed