The Communication Service Providers (CSPs) are in a race today to develop and deploy new 5G networks and Edge applications. The accurate end-to-end visibility for management and optimization of network performance for their existing LTE and the new 5G networks are essential to winning this race. The importance of network optimization increases with the emphasis on network performance. With the unique functionalities powered by Artificial Intelligence (AI) and Machine Learning (ML), such as automated remedial actions and performance prediction, CSPs can now optimize their entire network. AI-driven automation is now a crucial function for network planning and connectivity.

As we inch towards the 5G era, network optimization will enable the CSPs to improve their end-users app experience by relating network KPIs and service KPIs.

Network optimization helps distinguish subpar performance in the network and optimize where it matters for the end user’s app experience. The end-to-end network optimization improves the users’ app experience while optimizing network performance. Apart from the conventional network-related KPIs, it focuses on service KPIs tailored to each market’s specific apps and services.

The information that correlates network KPIs with local service KPI objectives helps identify performance optimization opportunities to be explored and acted upon.

How Does AI Solve the Optimization Challenge?

AI helps CSPs build self-optimizing networks and automatically optimizes network quality based on traffic and service KPI information by region and time zone. These AI apps use advanced algorithms to find data patterns for detecting and anticipating network anomalies and then proactively fix problems before they can impact the customers.

A typical network optimization workflow consists of three steps to convert a subpar network to an optimized one. AI enhances this process by automating the optimizations steps to make a self-optimized network (SON).

Subpar Network ➱ Network Audit ➱ Benchmarking ➱ Parameter Tuning ➱ An Optimized Network

Some of the key AI/ML interventions include:

Network audit by analyzing network performance KPIs
Automated benchmark creation by correlating network performance KPIs and parameter values at the optimum levels
Auto-tuning configuration parameters to maintain an optimized network when the KPIs breach a certain threshold
Automatic Antenna configuration for solving coverage challenges

The above list is not comprehensive, and multiple AI-interventions are possible.

It is important to note that the optimized networks are high performing networks and necessary for an enhanced customer service experience. The idea is to leverage the AI/ML capabilities to create a Self Organized Network (SON), enabling closed-loop network management with self-planning, self-configuration, self-optimization, and self-healing. This is shown in the image below.

As 5G opens new avenues for the telecom industry, CSPs should cover the extra mile to optimize their networks. Leveraging AI/ML to automate this process and implementing SON will help them cover this gap quicker with higher efficiency and accuracy.

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Contribution by: Raunak Gupta