O-RAN Network Energy Saving: RF Channel Switching


Massive Multiple-Input Multiple-Output (MMIMO) is one of the technologies that enable achieving high user throughput in the 5G and beyond networks [1].  Due to equipping Base Stations (BSs) with antenna arrays having up to hundreds of antenna elements, it is possible to formulate narrow spatial beams. Those beams can be used to simultaneously serve users using the same time-frequency resources, following the concept of spatial domain multiple access (SDMA). However, the cost to be paid is related to the hardware requirements, i.e., MMIMO requires multiple RF channels associated with each antenna. Every RF channel contributes to the total power consumption of the MMIMO BS. While having tens or even hundreds of such RF channels the amount of required energy becomes enormous. Moreover, the increased power consumption is not always compensated by the improved network throughput, i.e., under low traffic load scenarios the full advantage of MMIMO can be achieved with e.g., only 20%  of active RF channels. From this perspective, an intelligent adjustment of the active RF channels is crucial for energy-efficient 5G networks [2]. However, it is hard to be achieved in state-of-the-art mobile networks, where the interaction of the third-party software with the Radio Access Network (RAN) components is limited to the monitoring of some crucial Key Performance Indicators (KPIs), and setting the basic threshold for a few control actions, e.g., handovers. The deployment of the intelligent Energy Saving (ES) algorithms requires much more interaction with RAN, including unified mechanisms of reconfiguration of Radio Unit’s (RU’s) hardware, i.e., RF channels. This can be successfully achieved within the Open RAN (O-RAN) architecture [3].

In our previous blog posts (O-RAN as an Enabler for Energy Efficiency in 5G Networks, O-RAN Network Energy Saving: Cell Switching On/Off) we provided an overview of the Energy Efficiency (EE) in the context of the 5G networks with some possible solutions and discussed in more details the concept of the cell on/off switching. In the recent O-RAN ALLIANCE  Use-Cases-Analysis-Report [4], a new use case is indicated, aimed at providing ES within a single BS through load-dependent RF channel switching.

In this blog post, we provide the description of an algorithm that can be realized as an rApp in the O-RAN architecture aimed at improving the network’s EE through intelligent RF channels on/off switching. First, we provide a general framework for long-timescale EE optimization which can be done in Non-RT RIC. We then propose the implementation of the RF Channel Switching algorithm within the O-RAN framework. Finally, we discuss some opportunities and challenges.

RF Channel Switching

The traffic load in mobile networks varies over the daytime, usually, it is lower during the night when most people sleep and rises during the day when people utilize data transfer for work purposes. Taking into account the variations of the traffic load the number of RF channels can be adjusted so as to provide ES gains while satisfying users’ Quality of Service (QoS) demands. This procedure is depicted in Figure 1. As can be seen, during busy hours, all RF channels remain active. When the traffic load lowers to the level of a medium load, 40% of RF channels can be switched off to save energy. Finally, under low traffic, only 20% of active RF channels can provide users with satisfying QoS, the rest is switched off to increase energy savings even more. One should note that switching off RF channels require reconfiguration of the BS, e.g. while having fewer antennas, the number of the supported beams is reduced and the beams themselves change their shape.

Figure 1. Energy savings obtained through traffic-load-sensitive switching on/off RF channels

RF Channel Switching in the O-RAN Architecture

The high-level idea of switching RF channels on/off is relatively close to the concept of on/off switching of the entire cells. Although the O-RAN ALLIANCE specifications [5] do not define yet the framework for switching on/off RF channels, we expect it to be very similar to the one proposed in the figure therein. Because switching on/off RF channels require reconfiguration of BS (e.g., changing the number of supported beams), we expect it to operate in a slow control loop in the Non-Real-Time RAN Intelligent Controller (Non-RT RIC). The information flow between O-RAN entities for switching on/off RF channels based on ML inference in Non-RT RIC is depicted in Figure 2.

Figure 2. Switching on/off RF channels diagram: AI/ML inference via Non-RT RIC. Based on the diagrams for cell on/off switching in [5]

According to Figure 2, the process of RF channel switching in O-RAN architecture can be summarized as follows:

  • As the antenna array is expected to have several possible configurations, i.e., sub-arrays that can be activated or deactivated, at the first stage those configurations must be obtained from O-RU. They are reported to the Non-RT RIC by the Collection and Control Unit of the Service Management and Orchestration (SMO). The array configurations are reported to the O-DU (E2 Node) via the Open RAN Fronthaul Management Plane (O-FH M-Plane) and passed to the SMO via the O1 interface.
  • Next, the O-DU (E2 Node) is configured by the SMO to report the data necessary for energy-saving algorithms via the O1 Interface. Based on this configuration E2 Node utilizes O-FH M-Plane to retrieve the requested data. The data collected in the SMO are further retrieved by the Non-RT RIC through internal SMO communication.
  • In Non-RT RIC the collected data is subject to monitoring, i.e., some long-timescale statistics e.g., number of users per beam, user throughput, and related power consumption is obtained.
  • The obtained long-term statistics are used to train the ML model, and after the training, phases are utilized for the ML inference.
  • When ML inference results in a decision of switching certain RF channels on/off, the Non-RT RIC sends a proper execution request to the Collection and Control Unit via internal SMO communication.
  • After that, the Collection and Control unit proceeds with the proper configuration of the O-DU (E2 Node) through the O1 interface.
  • Based on the configuration from the  Collection and Control Unit the O-DU (E2 Node) updates the configuration of the related O-RU. One should note that changing the configuration of the O-RU’s antenna array requires reconfiguration of e.g., Synchronization Signal Blocks (SSBs), or a number of MMIMO layers.
  • Finally, the utilized ML model is subject to monitoring in the Non-RT RIC, e.g., to detect degradation of its performance, and to start a re-training procedure.

RF Channel Switching rApp

Now, we propose, a high-level concept of the RF Channel Switching rApp based on the general framework described in the previous section. The rApp performs ES optimization in the Non-RT control loop, by following the concept of Reinforcement Learning (RL) as depicted in Figure 3. First, the agent (rApp) recognizes the state, on the basis of parameters obtained via the O1 interface (e.g., number of users per beam, user throughput, power consumption ), and actual array configuration. Based on the state, the agent makes a decision on the action, i.e., selects one of the possible O-RU’s array configurations using the O1 interface to O-DU (E2 Node), which is coupled with O-FH M-Plane between O-DU (E2 Node) and O-RU. After that, the action (selected array configuration) is evaluated, and the resultant KPIs (power consumption, QoS metrics) constitute a reward used to update the action preferences. Then, the cycle repeats. It is also possible to utilize some Enrichment Information (EI), e.g., Location Information as in our work [6].

Figure 3. Implementation of RF Channel Switching rApp based on RL in the O-RAN architecture


The EE improvement is one of the key aspects when considering the BSs equipped with large antenna arrays, i.e., MMIMO BSs. The key challenge is to balance the energy consumption introduced by the multiple RF channels, and the throughput gains achieved by the beamforming. The deployment of such intelligent algorithms is possible only in the O-RAN architecture where third-party software can interact with RAN components at the level of hardware reconfiguration, i.e., activation/deactivation of some part of the antenna array.

During the design of the RF Channel Switching rApp, there are some challenges that should be taken into the account. First, switching off some RF channels changes the properties of the antenna array that enforces whole O-RU reconfiguration, e.g., the number of supported beams, and the number of possible layers. Second, the RL algorithm must be supported with some mechanisms that prevent testing of obviously wrong solutions, e.g., during the busy hour the configuration with only 20% of active RF channels should never be tested. Thirdly, the rApp must strictly cooperate with other xApps, e.g., with Rimedo Labs Traffic Steering (see Policy-based Traffic Steering xApp Implementation within O-RAN). Changing the configuration of the O-RU requires usually its restart, and the users should be temporarily offloaded to the neighboring cells to assure the service requirements fulfillment.


[1] A. Gupta and R. K. Jha, „A Survey of 5G Network: Architecture and Emerging Technologies,” in IEEE Access, vol. 3, pp. 1206-1232, 2015, DOI: 10.1109/ACCESS.2015.2461602.
[2] J. Hoydis, S. ten Brink and M. Debbah, „Massive MIMO: How many antennas do we need?,” 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011, pp. 545-550, DOI: 10.1109/Allerton.2011.6120214.
[3] M. Dryjanski, R. Lundberg, “The O-RAN Whitepaper; Overview, Architecture, and Traffic Steering Use Case”, 2021, https://www.rimedolabs.com/blog/the-o-ran-whitepaper/
[4] O-RAN Alliance, “O-RAN Working Group 1 Use Cases Analysis Report” v09.00, October 2022
[5] O-RAN Alliance, “O-RAN Working Group 1 Use Cases Detailed Specification” v09.00, October 2022
[6] M. Hoffmann and P. Kryszkiewicz, „Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching,” in IEEE Access, vol. 9, pp. 130329-130339, 2021, DOI: 10.1109/ACCESS.2021.3113461.

Related Rimedo Labs Resources

Author Bio

Marcin Hoffmann is a Senior R&D engineer at Rimedo Labs working on O-RAN software development solutions and spectrum sharing-related projects. Marcin is a Graduate Student Member, at IEEE and received an M.Sc. degree (Hons.) in electronics and telecommunication from Poznań University of Technology, in 2019, where he is currently pursuing a Ph.D. degree with the Institute of Radiocommunications. He is gaining scientific experience by involvement in both, national and international research projects. His research interests include utilizing machine learning and location-dependent information for network management. In addition to that Marcin works on massive MIMO and advanced beamforming techniques. His scientific articles are published in the top journals like IEEE Journal on Selected Areas in Communications, IEEE Transactions on Intelligent Transportation Systems, or IEEE Access. 

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