Planning and designing networks
Artificial Intelligence can help CSPs to enhance their network design for enabling better capacity and throughput prediction for short-term and long-term needs of their consumer base. Highly capable in working as a recommendation engine, AI capabilities are just more than enough to see traffic trends and behavioural flow of their consumers through AI-based analytics solutions and then better understand on how to optimize networks to give best Quality of Experience (QoE) to their users.
Embrace AI-driven tools for service operations
From reporting of problems to responding to event and incident, AI-driven tools for network operations assist network engineers automate and streamline network activities and incident management. ‘AIOps’ is the next level of automation with intelligent 5G when cellular networks meet artificial intelligence and is being deployed in telecom industry aimed at empowering software tools to take quick actions and respond immediately enabling predictive maintenance with the utilization AI technology for any operational events / incidents, security issues – that also without the intervention of humans.
Place AI at the heart of CAPEX agenda
With the deployment of 5G networks, operators’ network expenditure is anticipated to rise significantly over the years as they have to perform multiple iterations of testing with 5G network testing equipment & tools. While pricing pressure prevails, new concepts like private networks may challenge legacy value chain. As a solution to these challenges, operators are taking a smarter approach to plan their Capital Expenditure (CAPEX) and deliver better experience to consumers, satisfy regulators and drive enhancements in return on investments. Standardized AI based technology can deliver critical data with capabilities of deep learning and dynamic modelling in capex planning tools. According to a report from Ericsson, 48% of CSPs are on the stage of testing AI, focusing on how to reduce capex by this new-age technology.
Analysing logs of data with AI
With 5G, millions of IoT devices are getting connected and data is generated in huge bulks from internal procedures, server logs, applications, network controllers and other equipment. Conventional method used to accumulate data in logs and not much of accessibility was there. In the wake of AI, the network management systems are now automated, can analyse data, get results and extract insights to enhance network performance on regular intervals thus reducing downtime. Additionally, the speed in which an activity is performed is faster than humans.
Automatically prioritising critical network traffic
Networks are now guaranteed with timely delivery of critical traffic as they are built with AI-enabled smart switching. The inception of COVID-19 drastically changed the network service demands and operational processes due to movement restrictions and work from home policies. AI will enable network with proactive and dynamic resource allocation to allocate resources in desired locations based on real-time demand unlike static resource allocation method used in existing networks till now. This can resolve the issue of sudden shift in traffic demands from major cities to small towns and rural areas straining RAN and backhaul network.
Furthermore, Artificial Intelligence to manage network traffic of 5G wireless networks are used to forecast traffic and identify cell outages enabling network to amend new configurations. It can be done by switching off unoperative base stations or turn on self-healing functionalities of self-organizing networks (SON). AI will also optimise proactive handover required to manage critical emergency situations wherein patients are attended via video conferencing/voice communications and avoid network disruption or in case switching connections from one cell tower to another while care professionals are in the ambulance on their way to hospitals.
AI increases flexibility of cloud-enabled networks
CSPs are under pressure to curb operational and capital costs as they accelerate 5G wireless network rollouts. Technologies like Artificial Intelligence and Machine Learning are gaining traction in the network management field and assisting operators to manage 5G networks. Initially, reluctant to place their data on public clouds, operators have started optimising network through AI algorithms on public cloud due to its multiple key benefits. Due to the increase in complexity of 5G architecture, use of AI is certainly becoming a prerequisite to manage and optimise such network.
It is not only about the complexity of the architecture but also, addition of 5G on top of the layer stack comprising previous cellular technologies 2G, 3G, 4G. Moreover, modern use cases and abilities such as network slicing adds on to its intricacy. This is something humans cannot perform efficiently and require huge manpower which results in additional costs for CSPs. According to a survey conducted from 50 CSPs, 56% of CSPs come across data quality issues, 77% struggle with data storage and accumulation, and only 35% of them agreed to embrace AI to deploy intelligent ‘data capture’ methods. Analysing this study, it can be said that CSPs need to associate with AI vendors to assist them in reducing risks and up-front investments while providing top-notch data security and user experience. Implementation of AI-as-a-service (AAAS) approach can ensure flexibility and operators can pay based on what they consumed and get customised AI solutions from service providers to detect faults and auto-resolution addressal without human involvement.