Greener connectivity
Finally, a key part of any major project today must include addressing sustainability issues.
Energy consumption makes up a significant portion of the mobile network operator’s OPEX, estimated at between 20% and 40% – a figure that is even higher in heavy diesel usage regions, like Southeast Asia and Africa.
In 2020, the annual global energy cost for running mobile networks was about $25 bn, a figure that is likely to be significantly higher now, due to subsequent global economic challenges, like the energy crisis and surging inflation.
RAN (Radio Access Network) accounts for the largest portion of this cost, almost 80% of the network’s energy consumption, making it a prime target for energy-saving efforts. Successful interventions reduce costs as well as the CO2 footprint.
Several intelligent RAN energy-saving mechanisms, such as cell and carrier sleep modes, massive Multiple-Input Multiple-Output (MIMO) Rx/Tx reconfiguration, traffic steering, power control, etc., have been introduced over the years to reduce energy consumption in the RAN's active units.
However, adding AI/ML enables dynamic learning to intelligently control those mechanisms and improve energy saving further, while maintaining an optimal user experience. Initial trials have shown up to 12% additional savings over traditional techniques. However, for industrializing RAN energy saving for operators, the solution must be scalable, reliable, and replicable across a multi-vendor network environment.
Industrialized RAN energy saving will not be easy to implement. The key hurdles are:
Technical complexity – the mobile network consists of multiple technologies (2G, 3G, 4G, 5G). Energy efficiency must be implemented across these technologies and respective vendors.
Performance and quality of service (QoS) preservation – operators must ensure that QoS is closely monitored, and that energy-saving measures do not impact user experience.
Compatibility and interoperability issues in a multi-vendor ecosystem – networks are commonly built with equipment from different vendors. The RAN energy-saving solution must adapt to the respective data formats, KPIs, energy-saving mechanisms and APIs. This challenge can be mitigated by improving the standardization of network KPIs (usage, electricity consumption, QoS) and energy-saving mechanisms and APIs.
Cost considerations – implementing energy saving solutions may involve upfront technology and infrastructure costs. Moreover, the energy-saving solution will have its own CO2 footprint. Operators should carefully assess the return on investment and develop strategies for cost-effective energy savings.
Measuring the savings – to measure accurately, operators need to separate the contribution brought in by the new intelligent energy saving solution from the energy servings already built into vendor hardware and software.
To evaluate the potential benefits and gain confidence in their ability to overcome challenges, operators must engage in trials, starting with simulations based on real network data.
A simulation is risk-free, with no impact on the network, and can help evaluate returns. That can help make the case for field trials, and to deploy a better-tuned, industrial solution.
With Open RAN, it is possible to introduce near-real-time energy saving techniques in O-RAN Radio Units (O-RU), such as intelligent discontinuous transmission (DTx) - known as ‘micro-sleep’, intelligent discontinuous reception (DRx), intelligent radio resource control (RRC) inactive state, intelligent scheduling, and more.
These use cases provide deeper and more advanced energy-saving techniques that are not possible in traditional RAN. Additionally, O-RAN can optimize the energy consumption of the cloud infrastructure in which it operates, through intelligent workload optimization and CPU tuning.
The advent of 5G-Advanced and anticipated 6G technology will bring even bigger opportunities. New technologies are on the way, like IRS (Intelligent Reflecting Surface) and adaptive beamforming using sensing techniques also known as JCAS (Joint Communication and Sensing).
Together, these will make future networks more energy efficient and natively sustainable. Capgemini is actively engaged in research on these technologies with academic and industry partners, as part of our commitment to address climate change.