5G and AI: Scaling Networks Smarter, Faster, and Greener

Drawing on my background in AI, I’ve been analyzing how 5G integration with AI will reshape smart cities. The potential of these technological symbioses is staggering. If the 5G network contains AI, its speed, reliability, data transfer, and data processing capabilities will enable telecommunications infrastructure firms to easily manage infrastructures, growing 5G networks with AI's precision and agility.

With this in mind, I wrote a post outlining the best approaches for using AI in 5G to advance network administration. But first, let's reveal the issues of expanding 5G networks and what AI technology can do about it.

Breaking Barriers in 5G Expansion with AI

As a provider of shared network infrastructure, you confront the obstacles of implementing infrastructure in a congested urban area, extending coverage to rural areas, and introducing 5G within underground systems like subways. All of this while also striving to create a greener future by improving energy efficiency across your network.?

How can AI solve 5G network services scaling issues? It all comes down to how quickly and accurately AI solutions can sift data. AI/ML implementation and integration ensure that the infrastructure systems can dynamically optimize 5G deployment, modify bandwidth delivery in real-time, and foresee maintenance requirements before they cause interruptions.

Best Approaches to Using AI and 5G for Network Architecture Expansion

The main point of using AI in 5G deployment is proactivity and responsiveness in real life. In smart cities, this capability allows AI to optimize shared infrastructure by managing traffic, reducing energy use, improving public safety, and even forecasting equipment breakdowns before they occur.

Here are several compelling use cases illustrating the impact of AI on optimizing 5G network allocation:

AI for dynamic infrastructure planning

Imagine a bustling, high-density metropolis at rush hour: cars weaving through traffic jams, commuters pouring out of subway stations, and mobile users sending messages via their gadgets. Now imagine AI and 5G work in pairs. AI solutions can identify hot spots where bandwidth demand is high and reallocate network resources to these locations, ensuring all users have uninterrupted connectivity.

For example, Fujitsu, a Japanese multinational information and communications technology company, recently released its new AI-powered network applications to arm operators with enhanced network performance. The application uses neural network modeling to tame the complexity of 5G+ networks. This makes it easier for Mobile Network Operators (MNOs) to optimize network performance and handle the increasing challenges of modern networks, such as the growing number of connected devices and the scalability challenges.

Fujitsu's AI-based app processed data from hundreds of thousands of network nodes and end points, identifying the root causes and precise locations of thousands of network anomalies within minutes. This ability to adjust and optimize bandwidth allocation dynamically can also be applied to dynamic infrastructure planning for expanding and managing 5G networks, especially in high-density urban environments.

AI for energy efficiency in 5G networks

Managing signal transmission efficiently within 5G networks is critical for reducing energy consumption, especially in high-demand environments like smart cities. AI can assist by optimizing how signals (or millimeter-wave beams) are directed between the network and devices, ensuring that energy is allocated efficiently and only where needed.

For example, Qualcomm, a multinational semiconductor and telecommunications equipment company,? demonstrated how AI can intelligently predict the best beam paths to connect devices, eliminating the need to measure all possible beams continuously. This reduces energy consumption while maintaining strong, reliable connections. This approach supports sustainability in 5G applications by optimizing energy usage in heavy workload environments

AI-driven solutions can help address 5G expansion challenges by enabling proactive, real-time analysis of vast quantities of data

AI for improved network coverage in rural areas

AI helps telecom providers efficiently extend 5G coverage into rural areas by predicting user demand and optimizing infrastructure placement.

One illustrative use case is AI-enhanced CSI (Channel State Information) feedback, which can improve network performance in rural areas where base stations are spaced further apart and signal strength is often weaker. AI’s ability to dynamically learn and adjust to the environment helps optimize communication links, particularly in edge-cell scenarios frequently encountered in rural settings.

For example, the authors of the October 2024 paper titled ‘Performance Evaluation of AI-based CSI Feedback Techniques for 5G Advance and 6G Networks’ introduced the M-CsiNet model, an AI-based technique for compressing and reconstructing CSI. M-CsiNet outperforms traditional methods, like Type-I CSI feedback, by achieving better link-level throughput and lower block error rates (BLER). It operates with a 10-15 dB lower signal-to-noise ratio (SNR), allowing it to perform effectively in noisier, low-signal environments, much like those seen in rural areas.?

For telecommunications infrastructure providers, the symbiosis of 5G and AI could amplify their current 5G network ecosystem by providing better link-level throughput and lower block error rates (BLER), particularly in areas with limited network capacity.

AI for 5G infrastructure maintenance

One of the most impactful applications of AI in 5G is its ability to predict equipment failures before they occur, enabling a shift from reactive to proactive maintenance strategies.

Here are the best practices for using AI for 5G infrastructure maintenance:

1. Predictive Analytics

In the context of 5G infrastructure maintenance, predictive models are used to scrutinize info from multiple data points to forecast when and where infrastructure failures might occur.

Key elements of predictive analytics in 5G infrastructure maintenance include:

• Historical performance data
• Real-time sensor inputs from 5G base stations and equipment
• Environmental factors like network load or weather conditions

For instance, operators can perform maintenance before major disruptions by predicting when equipment failures might occur, reducing downtime and enhancing network reliability.

B. Machine Learning Models for Failure Prediction in 5G

Artificial intelligence and machine learning in the 5G network can be applied to monitor and predict failures within connectivity framework. Key models include:

• Regression models: predict the remaining useful life of 5G base stations or antennas based on real-time data and operational history.
• Classification models: categorize network components, such as towers, base stations, or small cells, into risk ones, such as high, medium, or low likelihood of failure, allowing operators to focus maintenance efforts on high-risk areas.
• Time series models: analyze historical patterns in 5G traffic data to predict future load increases, helping operators prevent potential failures due to overcapacity.
• Anomaly detection models: spot unusual patterns in network traffic or equipment performance that might indicate forthcoming failures or cyberattacks, enabling outrunning interference.

C. Real-time Monitoring and Data Collection for 5G Networks

IoT sensors on 5G base stations, antennas, and other infrastructure components can collect data on parameters like signal strength, temperature, and power usage. These sensors transmit data to AI systems, allowing operators to immediately detect anomalies, degradation in performance, or emerging network issues.?

Practical illustrations and case studies of AI-driven predictive maintenance:

• Predictive maintenance for roadside infrastructure

The AIoT-CitySense system integrates 5G, AI and IoT technologies to facilitate roadside infrastructure maintenance. The solution is designed for city-scale sensing in collaboration with a local Australian government. AIoT-CitySense collects city-wide data using mobile assets like waste trucks, public transport, and police vehicles. AI is used to analyze the data in real time to further identify and prioritize the maintenance tasks.

• AI for waste management in smart cities

The research covers the use of AI in waste management, including predictive maintenance for waste management systems. It revolves around technologies such as intelligent garbage cans, classification robots, predictive models, and wireless detection. These innovations allow for real-time waste bin monitoring, forecasting waste collection needs, and optimizing the efficiency of waste processing facilities through predictive maintenance.?

Bottom Line

In 2024 and beyond, integrating 5G and artificial intelligence will be a winning duo for boosting smart city connectivity, efficiency, and quality of life. The only thing I would like to emphasize is that the success lies in assessing the AI and data infrastructure required for a specific business case. This involves identifying relevant data sources, ensuring a robust connectivity infrastructure, and selecting the right AI models to tackle the issues, focusing on areas where AI can drive improvements in the 5G network. Finally, partnering with a tech provider who understands AI technology and your business needs ensures you get the right support for efficiently scaling your 5G infrastructure.

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