Future Ai Driven Network
An inaccurate evaluation would result in expensive fines for Ericsson and eventually undermine customer confidence. Therefore, accuracy is a critical need for the network dimensioning assignment. Ericsson is now in the process of developing a new data-driven network dimensioning tool. This tool will use Machine Learning (ML) to generate models that accurately estimate resource needs. In addition, the ML solution makes it easier to include telecom domain knowledge in the modeling process, resulting in models that are both more explainable and trustworthy from a telco point of view.
AI Changes Everything
The driving force behind this convergence is the field of artificial intelligence (AI). We will not be able to keep up with the complexity and scope of next-generation networks unless we use robots to supplement the abilities that humans now possess. According to the paper, networking has already made its first foray into a new field, the effects of which are impossible to anticipate in their entirety. Artificial intelligence (AI) is the revolutionary technology underlying this transformation, influencing every aspect of information technology (IT), including security, mobility, user experience, and IT management.
Key AI Technologies
Machine learning (ML) refers to using algorithms to process data, learn from it, and make a conclusion or prediction without needing explicit instructions from the user. Artificial intelligence cannot be practical without ML. Deep learning, or DL for short, is a machine learning that uses neural networks to achieve even higher levels of automation and insight than before. This development was made possible by recent breakthroughs in computing and data storage technologies. Another trend that has propelled recent advancements in artificial intelligence is natural language processing or NLP for short.
Traditional O&M
Using in-house specialists to manage all network alerts and defects is usual for the traditional routine of RAN network improvement. This procedure needs an extensive Operations and Management (O&M) staff since all data collection and analysis are done manually. Because of this, it is a labor-intensive process. In addition, most CSPs only have access to a certain amount of resources; as a result, only the “top N” problematic cells throughout an entire network are often chosen for optimization.
Leveraging AI-driven RAN Intelligence
When it comes to providing comprehensive management over a portfolio of multi-standard, multi-band 2G to 5G networks, traditional O&M is no longer an option for CSPs. Instead, communication service providers (CSPs) must resort to new AI-based RAN intelligence solutions. These solutions will, in the future, play a crucial role in assisting CSPs in managing complex, interconnected networks, ultimately leading to increased network performance.
Conclusion
Workers in every region of the world find themselves in significantly different work conditions compared to what they were experiencing at the beginning of the year. To emphasize employee safety and better adapt to the sheltered lives of their workforce, several businesses have established long-term (and even permanent) work-at-home policies. And when employees, students, shoppers, and other persons come back on-site, there will be a need to assure their safety using new methods, such as social distancing and contact tracking. This will be necessary to protect those folks.