According to a recent report, from analyst firm Rethink Technology Research, RAN automation – which is seen as crucial for supporting 5G business models – is grinding to a halt.
The report, which includes a survey with operators, suggests that target dates for achieving RAN automation have slipped, with the mid 2020s being a likely target. According to the authors, operators cited a number of reasons for this, such as “fear of organisational upheaval; immaturity of the technology; and an overall lack of urgency from operators.”
The impact of this will be seen in business uncertainty, increased costs and reduced flexibility – which, in turn, will further impact ambitions to capitalise on 5G. Of course, RAN automation is dependent on the increased adoption of Artificial Intelligence (AI) and Machine Learning (ML), both of which need to be incorporated into RAN solutions.
However, while the delay may be real, it doesn’t mean that there aren’t steps that operators can take to increase automation in the RAN in the meantime. One aspect of automation referenced in the report is RAN optimisation – the process of ensuring continuous performance management for coverage, capacity and service quality.
At Tambora, we’re helping operators to achieve this, via a closed loop process between device, network and cell-edge. This depends on real-time feedback from service performance in the RAN, which drives tuning of the quality delivered across the RAN connection, with dynamic, automated control depending on context and conditions.
For any future application of AI and ML, such closed loop processes are an essential first step. So, while, it’s disappointing to see that Rethink has not seen evidence of progress to this goal, we can point to deployments in which our closed loop optimisation is already delivering benefits.
This means that, contrary to perceptions, operators can already capitalise on proven techniques that drive RAN optimisation, and which will, ultimately support automation efforts. Don’t be put off – real-time, dynamic optimisation is already available and can help operators accelerate their automation efforts.
Contact the Tambora team to find out more.
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D&D core attributes
Our D&D core gathers meta-data across the ecosystem, from – the mobile, content servers and mobile network
Fuelling AI driven analytics and automation engines
Our D&D core provides a suite of micro-services based software modules
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Integrated RAN & core at the edge, saves access densification costs and enables new edge services
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