So why are mobile network operators (MNOs) not using PSFs to their full extent? We can mention three main reasons why mobile operators might not be using PSFs:

Traditional focus on RAN performance

Mobile network operators have traditionally put all their effort on RAN performance, competing to be the top performers in their respective markets’ benchmarks. This focus pushed the already developed OEM’s Radio PSFs into the background. Most MNOs have been reluctant to activate radio PSFs at all, as almost all engineering departments are measured only by network KPIs. In case of PSFs, there is always a lingering doubt about whether there is an impact on the performance.

Transition from legacy technologies

Another issue is the transition between technologies, which discourages the investment of energy-saving efforts in technologies that will soon be disconnected (3G before 2G), as well as in new technology (5G), in which the current focus is on deployment and market adoption.

Lack of optimization of PSF capabilities

Finally, there is PSF optimization. It is possible to have PSFs active at every RAN site but, like any other radio feature, most of them can be optimized. PSFs can get activated with default settings. This one-size-fits-all approach may be conservative enough to create confidence that no single site or cluster is degraded across the entire network, but it falls short in most sites in the energy savings target.

Despite these reasons, there is a huge potential for energy savings to be realized by ensuring that all possible actions are implemented and optimized. There are currently up to four technology generations consuming energy: even if some of them are going to be switched off soon, it is possible to cut down consumption starting today.

An ideal solution to control energy consumption

There are some recommendations that are worth discussing to maximize the use of PSFs. These recommendations are based on three essential concepts.

AI is the way

As stated in an NGNM report, “Here, Artificial Intelligence (AI) could play an important role. By predicting and learning the traffic behavior, AI algorithms define the activation/deactivation of sleep mode functionality and site energy management without impacting the overall performance, including Quality of Experience (QoE). AI is still in an early phase, and more development and research are needed to reach its full potential. AI-based energy saving solutions can greatly increase the energy performance of cellular networks.”

Low latency

The on-demand resource allocation requires minimum latency between the data collection that characterizes the current state of the system, the execution of the decision-making process, and the implementation of the corresponding action in the network.

Low latency in this observation-reaction cycle leads to a responsive network adaptation to traffic changes that ensures a minimum power consumption without impacting the user experience. For instance, when user traffic starts to increase significantly, then additional radio resources are seamlessly enabled.

Dynamic and multi-vendor orchestration

Another advantage of this ideal approach is the continuous orchestration of the different possible actions to take. The selection, sequence, and timing of actions are essential to maintaining the goal of optimally reduced energy consumption with no impact on the customer network quality.

Furthermore, an open orchestrator approach allows any MNO to implement their own power saving strategy, such as different levels of aggressiveness between technology and frequency layers.