When deploying 5G, mobile operators aim to reduce time to market by automating the time-consuming and costly tasks of tuning and optimization. They also aim to maximize network deployment ROI by utilizing automated, AI-based solutions like Aglocell’s AI-based solutions for tuning and optimization. To achieve these goals in the face of increased network traffic and complexity, mobile operators are utilizing Aglocell’s AI-based solutions to increase the level of operational automation, for example by minimizing the use of drive tests.
Aglocell for Network Tuning
To accelerate the time to market of newly deployed mobile network infrastructure and reduce the number of customer complaints at new service launches, Mobile operators need to improve their site acceptance optimization processes. Most mobile operators continue to see drive testing as a core practice. However, drive testing is time-consuming with limited visibility into actual user experience, and the data is soon out of date. Aglocell’s automated, AI-based solutions present an alternative approach to drive testing with the benefits of speed, improved accuracy with geolocation, and reduced carbon dioxide emissions.
Aglocell’s AI-based solutions can cover wider areas and provide greater insights into the real experience of end-users. Aglocell algorithms (Figure 1) include, AI-based solutions that detect cells that are experiencing interference, detects if users intersect with multiple cells, and determines if enough users are impacted. In addition, Aglocell utilizes image classification using convolutional neural networks (CNNs) and deep neural networks to determine if cells are overshooting. Finally, the Aglocell automated AI-based solutions make network changes and collect feedback to verify the changes improved the condition.
Figure 1. Aglocell solution components
In addition, the solution utilizes ML to analyze the results to improve the algorithms to ensure the cells reach optimal performance. The result is faster time to market for high-quality services and reduced OPEX because less time is spent spotting and resolving issues after network deployment.
Aglocell for network optimization
The objective of every network optimization task is to ensure the network delivers high-quality services. It also ensures that deployed network assets are utilized to their maximum potential before expansion is considered. As the number of subscribers and the amount of network equipment have increased, optimizing the network has become increasingly challenging. Mobile operators need to improve the accuracy and speed of their network quality improvement efforts. They also need to identify cell performance issues and recommend changes with high precision to ensure that customer experience and network costs are balanced.
By correlating and analyzing a broad set of data, Aglocell’s automated AI-based solutions that utilize techniques such as deep learning can automatically optimize problematic cells based on parameters such as coverage, load, mobility, and uplink interference. These datasets include performance, configuration, and geolocation. Using these datasets, Aglocell’s AI-based solutions identify issues associated with problematic cells and automatically make configuration changes to resolve these issues.
Mobile network changes are automatically made to resolve these performance issues that are also tracked to ensure they improved the issue, including retuning network parameters such as antenna tilt angles, cell handover parameters, L3 algorithms (mobility), and L1/L2 algorithms (uplink power control, scheduling, etc.).
The complexity of mobile networks is a given and will demand a transformed approach to network tuning and optimization. Multi-vendor support, adopting a DevOps approach to team collaboration and utilizing open and cloud-native solutions will be key to this transformation.
Automation driven by AI and big data will also enable mobile operators to achieve their business objectives such as minimizing Capex while meeting their customers’ needs thanks to increased accuracy in determining network requirements.
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