About this Research Topic
The radio access network (RAN), which is the part of cellular networks that connects users to the core network has been identified as the most energy consuming part of the cellular network and accounts for about 70-80% of the total energy consumption of cellular networks. Among other factors, to checkmate the energy consumption of cellular network due to network densifications, the RAN architecture has undergone a series of evolutions to improve its performance with the latest development being the O-RAN architecture. Although several potential gains have been associated with the O-RAN technology compared to the conventional RAN including open disaggregation, interoperability, and integration capability, cloudification, and standard-based compliance, the impact of this recent architecture on the energy consumption of cellular network is still yet to be fully ascertained. Since power usage is critical for both sustainability and cost efficient operation of cellular networks, and most of the energy consumption of mobile networks comes from the RAN, there is a need for the end-to-end energy efficiency of all domains of O-RAN to be addressed upfront in the design of O-RAN solutions so that the energy consumption overhead does not outweigh its potential benefits.
The goal of this Research Topic is to collect relevant and high-quality manuscripts on energy efficiency of O-RAN that will promote a more sustainable and cost efficient 5G and beyond cellular networks. To address the energy consumption challenges of O-RAN, there is a need to develop a holistic power consumption model that would quantify the amount of energy savings that can be obtained from potential energy efficiency techniques. Different approaches to achieving energy efficiency from both hardware and software perspectives must be considered to maximize the energy efficiency gain that is achievable with this new RAN technology. In addition, since O-RAN provides native support for artificial intelligence, the development of automated frameworks for optimizing the energy efficiency of O-RAN is also an area that should be explored.
Herein, both original manuscripts, extended versions of previously accepted/published conference papers, and survey papers are solicited for publication. The scope of this Research Topic includes (but is not limited to):
1. Power consumption modelling of O-RAN considering various functional split options
2. Machine learning, deep learning, and federated learning for energy-efficiency optimization in O-RAN 3. Energy-efficient CU, DU and RU placement in O-RAN
4. Energy-efficient functional split selection and migration in O-RAN
5. Energy-efficient resource allocation in O-RAN
6. Energy efficient user association in O-RAN
7. Energy efficient mobility management in O-RAN
8. Energy-efficiency design and optimization of O-RAN transport network
9. Energy-efficient dynamic activation and deactivation of O-RAN elements based on network load
10. Energy-efficient design and improvement of O-RAN hardware
11. Energy-aware traffic steering and load balancing
12. Energy efficient routing
The goal of this Research Topic is to collect relevant and high-quality manuscripts on energy efficiency of O-RAN that will promote a more sustainable and cost efficient 5G and beyond cellular networks. To address the energy consumption challenges of O-RAN, there is a need to develop a holistic power consumption model that would quantify the amount of energy savings that can be obtained from potential energy efficiency techniques. Different approaches to achieving energy efficiency from both hardware and software perspectives must be considered to maximize the energy efficiency gain that is achievable with this new RAN technology. In addition, since O-RAN provides native support for artificial intelligence, the development of automated frameworks for optimizing the energy efficiency of O-RAN is also an area that should be explored.
Herein, both original manuscripts, extended versions of previously accepted/published conference papers, and survey papers are solicited for publication. The scope of this Research Topic includes (but is not limited to):
1. Power consumption modelling of O-RAN considering various functional split options
2. Machine learning, deep learning, and federated learning for energy-efficiency optimization in O-RAN 3. Energy-efficient CU, DU and RU placement in O-RAN
4. Energy-efficient functional split selection and migration in O-RAN
5. Energy-efficient resource allocation in O-RAN
6. Energy efficient user association in O-RAN
7. Energy efficient mobility management in O-RAN
8. Energy-efficiency design and optimization of O-RAN transport network
9. Energy-efficient dynamic activation and deactivation of O-RAN elements based on network load
10. Energy-efficient design and improvement of O-RAN hardware
11. Energy-aware traffic steering and load balancing
12. Energy efficient routing
Keywords: Power consumption, energy efficiency, machine learning, mathematical modelling, O-RAN, 5G, Beyond 5G
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
