Artificial Intelligence and Network Optimisation

Network Optimisation is a process used by communication services providers (CSPs) to assess for, identify and resolve problems throughout the system. The goal of this is to ensure that the network is able to deliver high quality services and this is achieved with the integration of various technologies. Network optimisation can help companies to improve user experience without necessarily expending more on hardware upgrades. Network optimisation is essential for several reasons. A telecommunications company's ability to compete with others in the market depends on how reliable customers consider its network to be. In addition, the general performance of telcos impacts the productivity of jobs that depend greatly on online access which represents most jobs, especially as the COVID-19 pandemic has caused a surge in remote jobs. 

AI provides vast and multiple opportunities for the process of network optimisation. Using AI, the steps required for network optimization can be improved in speed and efficiency allowing for rapid identification and elimination of hurdles to adequate network delivery. 

Anomaly detection: Part of network optimisation involves properly distributing traffic across available network routes. AI enabled technologies can be used in this regard to properly study the load, detect abnormalities in connections and propose possible configurations that balance out the system while minimizing additional cost related to system reconstruction. 

Predictive maintenance: Advanced systems can use the deep learning capabilities of AI algorithms to analyze the data collected from monitoring networks. This can then be worked on to predict possible system failures and propose solutions in advance to prevent and mitigate the effects of network problems. Beyond this, the results of monitoring networks can be applied in rearranging the network for better performance or proposing better structures for building future networks. 

Selective prioritisation: AI can be used to selectively assess problems within a network discovered during monitoring and determine which problems may be self resolving by advanced system capabilities. By doing this, human resources and time can be tailored toward resolving more complex issues and consequently save valuable time and cost for telecommunications companies. Faults in network systems can also be localised, making it easier to support workers in repairing the network.

Network predictions: When AI systems are integrated within a network, it becomes easier to study and predict the peak periods of demand along the network. This can be used to control supply along the network chain during busy periods by ensuring that network capabilities are automatically reinforced and traffic redirected before those times in order to ensure satisfactory delivery to customers. This can grow to include other factors such as weather patterns and other external factors in certain industries. 

Hardware and resource management: AI systems can be connected to hardware and make commands to repair network interruptions. By troubleshooting problems, the systems are able to make suggestions based on previous situations and implement those suggestions. This will help to reduce delays and the need to get workers to physical locations to resolve network issues. Algorithms can also be used to point to which areas require further work and allocation of resources can be made more judiciously to ensure maximum results for the lowest possible cost. 

Essentially, network optimisation carries the idea of improving and sustaining the performance of networks. In a world where several activities and multiple jobs are moving online, network optimisation is an important step not only in ensuring customer satisfaction but also in contributing to the success of the industries that depend on them. 


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