AI in networks
AI is radically transforming how to manage, deploy, and evolve mobile networks. Below, we cover everything you need to know about AI in networks, including how it can help you to unleash mobile networks that are efficient, secure, resilient, and innovative.
AI in networks explained
Advanced AI network technologies harness ever-powerful forms of machine learning and reasoning, generative AI, and more, to deliver on complex and diverse business goals, taking service providers closer than ever to fully autonomous intent-based networks with minimal human touch.
This is achieved by deploying AI agents within the network itself, replacing existing rule-based algorithms, adding new AI-based components with new functionality, or by adding AI-based controls to existing networks components. These agents then leverage the massive real-time data generated by today’s networks to predict outcomes, detect anomalies, dynamically allocate network resources, optimize network quality of service, and more.
The impact is transformative; not only on network performance, but also in optimizing and reducing costs, resources, and energy across the network, as well as unleashing future business possibilities.
Everything is possible with AI in networks.
Key benefits
As networks grow increasingly complex, ensuring high network performance while meeting key business deliverables such as energy reduction, cost optimization and service enablement, is challenging. AI makes this possible by leveraging powerful new capabilities across anomaly detection, dynamic resource allocation, quality-of-service optimization, and more.
Improves performance
AI enables dynamic network, spectrum and computational resource allocation. This optimizes the routing of data to reduce congestion and improve throughput, bandwidth, and latency in high service demand areas.
Intent-based outcomes
Through closed loop automation that includes business intent capture, translation and activation, the network will continuously monitor and intuitively adjust to ensure that network performance aligns with your dedicated business goals.
Prevents faults
AI opens a new paradigm of self-healing networks, enabling pinpoint accuracy of root cause detection and reducing time to resolution. When deployed at telecom sites, AI also reduces the need for hazardous field work, remotely notifying engineers about failures or predicted service degradations.
Saves energy
AI improves the energy efficiency of networks, maximizing network utilization without impacting the performance of energy-saving features. AI can also act autonomously on real- time – or even predicted – traffic, helping CSPs reduce their energy consumption and lower their carbon footprint.
Evolves business models
Unleashing large language models on network behavioral data can produce a deeper understanding of customer intent. This enables opportunities for more targeted, personalized services. It also enhances tasks such as idea generation, concept validation, knowledge extraction from primary research and product documentation, and predictive modeling.
Enhances security
By detecting and preventing security threats, such as cyberattacks and fraud, AI enhances network security and protection of users' data. AI algorithms can also be used for fraud detection and prevention in areas like billing and subscriber management, helping CSPs reduce losses and improve their financial performance.
Dive deeper into the benefits
Essential technologies explained
The AI network space is moving fast, creating a new world of complex terms and concepts to those working in telecom. Take a crash course in some of the essential AI technologies below.
ML vs RL vs MR: Understanding the different Al technologies in networks
As a broad field, AI comprises many various technology concepts that, when combined, allow us to solve all manner of problems. This includes machine learning, reinforcement learning, and machine reasoning.
Machine learning applies advanced statistical techniques capable of finding subtle patterns in very large collections of data. In doing so, it can capture the hidden patterns needed to effectively predict outcomes. Read our machine learning use cases blog post.
Reinforcement learning takes this a step further, enabling AI agents to operate based on goal states rather than pre-defined programmed behaviors as with machine learning. In doing so, it enables networks to learn, optimize and automate almost entirely autonomously. Read our reinforcement learning report.
Machine reasoning is applied to integrate intent within those processes – bringing human-like common sense to analyze and translate vast knowledge and learned network data into clear explainable insights. Learn more about machine reasoning.
How and where to apply AI in networks
The emergence of cloud-native networks opens the entire network architecture to the possibilities of AI. Today, AI can be deployed across all network domains – from the network core to the RAN, and the network edge – and at every stage of the network lifecycle.
Top AI use cases in networks
Customer- and service experience
Cognitive tuning and optimization technologies contribute to a superior user experience, reducing poor quality areas through right-on-time expansions, timely launch and proactive network optimization. AI can also be deployed to improve customer service experience, with use cases that cover customer relationship management, channels, sales, and marketing.
Network planning and design
To deliver cost efficiency, AI-based network planning and design leverages deeper analytics that can accurately predict needs and optimize TCO. This includes high-accuracy traffic forecasts, KPI predictions, bottleneck identification and load balancing opportunities across the network lifecycle. Through live radio measurement utilization, such as subscriber traffic patterns, service providers can also ensure optimal 5G network expansion.
Network optimization
AI-based network diagnostics continually optimizes and improves network performance and efficiency, scanning all network cells in minutes – identifying issues quickly and with high precision. This makes it possible to proactively identify 50 percent more issues with up to 98 percent field-validated accuracy, increasing operational efficiency by up to 30 percent.
AI and cyber security
Through new innovative means and techniques, AI can enhance and automate the current security of 5G networks to detect zero-day attacks, as well as predict upcoming attacks, detect ongoing attacks, and test and deploy new defense mechanisms at run time. Read more: Ericsson drives research into AI-based cyber security.
Network operations
AI is revolutionizing the concept of network operations, taking service providers closer towards zero-touch end-to-end automation of the network. Step by step this is reducing manual operations, enabling improved business agility based on augmented decision making and data-driven, predictive and proactive operations.
What the experts are saying
Want to get ahead of the hype cycle? In our AI blog series, we cut through the noise around AI to bring you real, industry- leading insights to get your AI journey off the ground. Explore the latest insights below.
Digital twins: meet the latest disruptors in the network space
Digital twins are one of the most exciting technology concepts to emerge from the field of network AI. As dynamic virtual replicas of physical objects, processes and systems, digital twins make it possible to test and experiment with simulations without risk or disruption to its real-world counterpart.
Digital twins at the network edge and in enterprise applications have attracted most of the hype, with their ability to bring new levels of efficiency for logistical and industrial operations. However, they are also proving to be significant disruptors across network domains, enabling new, game-changing ways to deploy, manage, and optimize networks.
Network digital twins
Network digital twins optimize the ’invisible’ elements of a network: the signals, coverage, interference, and traffic behavior, including user mobility across frequency layers. By replicating an external virtual entity that mimics the behavior of the live network, the network digital twin creates a safe playground for experimentation.
Site digital twin
Site digital twins optimize the ’visible’ elements of a network: the towers, equipment and all other assets included in a physical site. This enables fully digitalized design and lifecycle management of on-site equipment through accurate 3D modeling of each site leveraging laser scanners (LiDAR), cameras and drones.
Subscriber digital twin
Subscriber digital twins enable real-time modeling of subscribers through 3D modeling of complex scenes that make it possible to experiment with network design in an interactive environment while mapping subscriber impact in real time.
Latest insights on digital twins
Getting the basics right: how to begin your AI journey
Before you embark on your Al journey with Ericsson, remember that there is no one-size-fits-all strategy. The investment you make will be defined by your choice to build, buy, partner or subscribe to managed services powered by Al. These decisions should be shaped and scaled to meet your unique business goals and network topology.
From our experience partnering with leading Al frontrunners across the industry, we have identified six enabling cornerstones that are crucial to get your Al network journey started:
01: Ensure access to quality data through good data architecture and governance
02: Create a roadmap that maps desired strategic outcomes with business relevance
03: Build your technology infrastructure, including cloud, storage and MLOps platforms
04: Develop your expertise and get buy-in between business and technical teams
05: Establish metrics to measure and improve all AI steps across every business area
06: Plan your full lifecycle of developing and deploying AI models, including ethical due diligence
Explore enabling technologies
Ericsson Al-driven solutions in action
Ericsson delivers an industry-leading suite of cognitive software solutions with multi-vendor support across radio access technologies and wide reach across the entire network lifecycle. Through unique integration of network design and optimization domain knowledge with advanced AI technologies, the Ericsson suite is designed to truly unleash the full potential of next-generation networks.
Customer success stories
Why choose Ericsson for your AI journey?
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