In conventional antenna systems, the antennas are centrally collocated, whereas in DAS the remote antennas are distributed over the cell. In DAS, the cell is divided into multiple sectors and the remote antennas are connected to the BS via optical fiber or cable [70].

DAS systems are categorized into three classes namely, active, passive, and hybrid DAS. Passive DAS utilizes only passive components such as coaxial cables, splitters, combin-ers, and filters. These systems are easy to design and is resis-tant in harsh environments. The main disadvantage of passive DAS is the high loss in transmission. Therefore, a high-power BS is required to feed a system of coaxial cables and hence high noise level exists in the uplink. Unlike the passive DAS where only passive components are used, in active DAS active components such as optical fiber, master unit, and remote unit to transmit signal from BS to the antenna network are used. In active DAS, the total RF loss is much lower than that of passive DAS. As a chain of RF to optical interfaces and optical to RF interfaces are used to propagate the RF signal over long distances using fiber instead of coaxial cables, the uplink and downlink RF amplifiers are located after the optical to RF interface and closer to the antenna because of which transmission power required from the amplifiers is much lower. Furthermore, active DAS is more flexible to change of design after the initial installation. Hybrid DAS works similar to active DAS. It inherits the advantages of both passive and active DAS. It uses fibers but still relies on passive coaxial cables for signal distribution. Hybrid DAS could be a potential solution for medium-sized buildings, tunnels, or areas, where multiple passive DASs can be linked by fiber cables. In the current DAS equipment design, there is no transmission time interval (TTI) or symbol-level sleeping features. However, utilizing energy saving features such as power control, excess layer shut down, and antenna sleeping can save significant energy in the network.

In SooGREEN, we studied multi-vendor multi-band indoor mobile coverage systems that use fiber DAS consisting of master unit, RU, antenna network and operator-controlled BSs. We consider a tunnel scenario in which leaky feeder cables are used instead of antennas. To assess the energy saving benefit/potential of such features, we implemented the setup in two tunnels, a road tunnel and a train tunnel, in Stockholm, Sweden. In these two case studies, we explore the power saving opportunities using time-domain sleeping and layer shut down during the low traffic hours. In order to investigate the power consumption of the system, 47 RUs and 2 master units have been measured along a duration of two weeks.

### 1) ROAD TUNNEL

In this case study, we consider a 10-km long road-tunnel with five sectors where we use the same leaky feeder for downlink and uplink. In each sector, one leaky cable is connected to all the RUs of different bands of different operators. In total, there are 16 BSs that belong to three different operators. In this network, operators are providing 2G, 3G, and 4G

<table><caption>TABLE 4. DAS Case study I: Road tunnel.</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>Length</td><td>10 km</td></tr><tr><td>Number of operators</td><td>3</td></tr><tr><td>Number of sectors</td><td>5</td></tr><tr><td>Number of BSs</td><td>16</td></tr><tr><td>Number of base station interface units (BIUs)</td><td>30</td></tr><tr><td>Number of fiber optic interfaces (FOIs)</td><td>16</td></tr><tr><td>Number of RUs</td><td>30</td></tr><tr><td>Length of leaky feeder</td><td>10 km</td></tr></tbody></table>

(a) Studied Parameters

(b) Measured Power Consumption

<table><thead><tr><th rowspan="2"></th><th colspan="3">Power consumption (kW)</th></tr><tr><th>2G</th><th>3G</th><th>4G</th></tr></thead><tbody><tr><td>BSs</td><td>3.12</td><td>3.37</td><td>4.6</td></tr><tr><td>RUs</td><td>4.2</td><td></td><td>5.7</td></tr><tr><td>BIU</td><td></td><td>0.45</td><td></td></tr><tr><td>FOI</td><td></td><td>0.24</td><td></td></tr><tr><td>Total</td><td></td><td>21.67</td><td></td></tr></tbody></table>

services. Both GSM (2G) and 3G are for voice traffic in the tunnel which is required to operate with full functionality during the rush hours. Otherwise, GSM RUs can be turned off when the traffic is not very high. As a part of this study, the power consumption of all active components was studied for a time duration of two weeks. More details of the tunnel and installed equipment are also listed in Table 4a and the corresponding measurement of energy consumption is reported in Table 4b.

By turning off the RUs, 4.2 kW power saving is reported which corresponds to decreasing the overall energy consumption of the entire tunnel network by 19.3%. Additional energy savings can be achieved by also turning off the GSM BS, thus resulting in an additional 3.12 kW power saving, leading to a total of 33.7% energy saving during low traffic hours.

### 2) TRAIN TUNNEL

In this case study, we investigate an 8.7 km train tunnel with separate leaky feeder for downlink and uplink. These types of tunnels are slightly different when compared to road tunnels since there exists no traffic in between the trains passing the tunnel. As per the information provided by the transport agency, there are maximum 10 trains per hour in 2016 at peak times. Considering a train travelling at 200 km/h, it takes around 2.5 minutes to cross the tunnel. Hence, the tunnel has traffic only 41% of the time. The details of the tunnel and installed equipment have been listed in Table 5a and the corresponding measurement of energy consumption has been reported in Table 5b. This means that if appropriate schemes can be devised, the power consumption can be reduced to half (12.5 kW) when all the layers can be shut down or sent to deep sleep mode when there is no passing train. This yields 64.600 kWh of energy saving per year. There are currently around 150 long railway tunnels (> 5km) in Europe [71].
<table><caption>TABLE 5. DAS case study II: Train tunnel.</caption><caption>(a) Studied Parameters</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>Length</td><td>8.7 km</td></tr><tr><td>Frequency of trains</td><td>10 trains/h</td></tr><tr><td>Speed of trains</td><td>140-200 km/h</td></tr><tr><td>Number of sectors</td><td>3</td></tr><tr><td>Number of operators</td><td>5</td></tr><tr><td>Number of BSs</td><td>11</td></tr><tr><td>Number of BIU</td><td>33</td></tr><tr><td>Number of RUs</td><td>63</td></tr><tr><td>Number of FOI</td><td>63</td></tr><tr><td>Length of leaky feeder</td><td>17.4 km</td></tr></tbody></table>

<table><caption>(b) Measured Power Consumption</caption><thead><tr><th rowspan="2"></th><th colspan="3">Power consumption (kW)</th></tr><tr><th>2G</th><th>3G</th><th>4G</th></tr></thead><tbody><tr><td>BSs</td><td>2.6</td><td>2.38</td><td>4.75</td></tr><tr><td>RUs</td><td>5.88</td><td></td><td>7.94</td></tr><tr><td>BIU</td><td></td><td>0.5</td><td></td></tr><tr><td>FOI</td><td>0.32</td><td>0.32</td><td>0.32</td></tr><tr><td>Total</td><td></td><td></td><td>24.988</td></tr></tbody></table>

Assuming similar settings and equipment, the savings could amount up to 9.690 MWh per year. For a European home which uses 175 kWh/m2 heating energies per year [72], this energy could heat close to 700 homes with a size of 80 m2.

The analysis of this study revealed the overall power consumption of a DAS in road and train tunnels and helps us identify that the remote units have a major share of power consumption in the network and that the power consumption of the remote units can depend on the coverage area and number of operators in a network. Furthermore, this study showed that there are considerable energy saving opportunities in such indoor networks, since the traffic in road and train tunnels is intermittent and hence the demand for coverage may go down to zero over some periods of time.

## VI. CHALLENGES, FUTURE TRENDS, AND RECOMMENDATIONS

Research and collaborative projects targeting green wireless networks are paving all sectors including mobile, fixed and backbone networks. The purpose of this article is to present what is a green mobile network and what are the current trends in this field. Different degrees of freedom have been emphasized to highlight the enablers for a green wireless access network and to tackle with the ICT sector commitment in terms of energy and environmental impact. This article also has the ambition to clarify research trends and KPIs in radio and of mobile network deployments. We have addressed existing and emerging technologies, and the convergence of the different network facilities towards greener resource consumption. However, device evolution has not been considered and should be addressed in dedicated research efforts. The article is also intended to foster discussions within the different stakeholders involved in the subject across organizations and activities especially operators. The main part of

the mobile network consumption is in the radio access and a lot of efforts (like RE, sleep-mode and passive cooling) are performed at this stage. However, other effects on the mobile core network and its backhaul shall not be neglected especially when new architectures are considered.

### A. GREEN MOBILE NETWORKS

Mobile networks are today responsible for about 20 - 25% of the aggregate information technology network (ITN) consumption including the data center, and will dramatically increase in the next decade due to new networks deployments and traffic demand exponential increase (about 30 to 40% per year).

In a short-term view, trends are moving towards making energy consumption more traffic load dependent. Actual macro BS is highly consuming even for low traffic load. A special care should be maintained for site-sharing initiatives (including outsourcing towards Tower Companies) to maintain assets in the green field. The technical environment (cooling, energy backup) shall not be neglected as they are highly consuming. Ventilation should be stated as a default installation configuration rather than active cooling. Passive (free) cooling can reduce the site cooling energy consumption by a factor of 8-10.

In a long-term vision, a global top-down approach is fundamental to balance the energy consumption regarding the traffic evolution in the next decade. A global approach allows to sustainably manage the evolution of our networks taking into account environmental impacts. Trends are towards new architectures that allow:

* Connecting large number of various devices and objects including critical ones.

* Geographical and social inclusion.

* Deployment everywhere.

* Efficient energy and natural resource usage.

* Trust and security.

* Collecting, processing and transporting a huge amount of data.

Those attractive concepts should be carefully studied mainly to avoid rebound effect on the backhaul and the core networks. Furthermore, hybrid energy renewable sources (PV, wind, batteries) should be optimized for diet-radio BSs especially for AMEA (Asia, Middle East and Africa) in accordance with natural energy assets in those countries.

Globally, the question of energy-free objects and network massive densification managed and orchestrated using artificial intelligence (AI) shall be the main driver for future networks. There is no magic response that should be applicable for any situation. This important question shall be considered regarding future disruptive services and corresponding networks urbanism regarding social acceptability and ethics. Indeed, we strongly believe that the future progress in mobile networks will face the question of privacy and security as a main driver.

Finally, intensive involvement in the standardization of green networks is the key to guarantee that green solutions
<table><caption>TABLE 5. DAS case study II: Train tunnel.</caption><caption>(a) Studied Parameters</caption><thead><tr><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>Length</td><td>8.7 km</td></tr><tr><td>Frequency of trains</td><td>10 trains/h</td></tr><tr><td>Speed of trains</td><td>140-200 km/h</td></tr><tr><td>Number of sectors</td><td>3</td></tr><tr><td>Number of operators</td><td>5</td></tr><tr><td>Number of BSs</td><td>11</td></tr><tr><td>Number of BIU</td><td>33</td></tr><tr><td>Number of RUs</td><td>63</td></tr><tr><td>Number of FOI</td><td>63</td></tr><tr><td>Length of leaky feeder</td><td>17.4 km</td></tr></tbody></table>

<table><caption>(b) Measured Power Consumption</caption><thead><tr><th rowspan="2"></th><th colspan="3">Power consumption (kW)</th></tr><tr><th>2G</th><th>3G</th><th>4G</th></tr></thead><tbody><tr><td>BSs</td><td>2.6</td><td>2.38</td><td>4.75</td></tr><tr><td>RUs</td><td>5.88</td><td></td><td>7.94</td></tr><tr><td>BIU</td><td></td><td>0.5</td><td></td></tr><tr><td>FOI</td><td>0.32</td><td>0.32</td><td>0.32</td></tr><tr><td>Total</td><td></td><td></td><td>24,988</td></tr></tbody></table>

Assuming similar settings and equipment, the savings could amount up to 9.690 MWh per year. For a European home which uses 175 kWh/m2 heating energies per year [72], this energy could heat close to 700 homes with a size of 80 m2.

The analysis of this study revealed the overall power consumption of a DAS in road and train tunnels and helps us identify that the remote units have a major share of power consumption in the network and that the power consumption of the remote units can depend on the coverage area and number of operators in a network. Furthermore, this study showed that there are considerable energy saving opportunities in such indoor networks, since the traffic in road and train tunnels is intermittent and hence the demand for coverage may go down to zero over some periods of time.

## VI. CHALLENGES, FUTURE TRENDS, AND RECOMMENDATIONS

Research and collaborative projects targeting green wireless networks are paving all sectors including mobile, fixed and backbone networks. The purpose of this article is to present what is a green mobile network and what are the current trends in this field. Different degrees of freedom have been emphasized to highlight the enablers for a green wireless access network and to tackle with the ICT sector commitment in terms of energy and environmental impact. This article also has the ambition to clarify research trends and KPIs in radio and of mobile network deployments. We have addressed existing and emerging technologies, and the convergence of the different network facilities towards greener resource consumption. However, device evolution has not been considered and should be addressed in dedicated research efforts. The article is also intended to foster discussions within the different stakeholders involved in the subject across organizations and activities especially operators. The main part of

the mobile network consumption is in the radio access and a lot of efforts (like RE, sleep-mode and passive cooling) are performed at this stage. However, other effects on the mobile core network and its backhaul shall not be neglected especially when new architectures are considered.

### A. GREEN MOBILE NETWORKS

Mobile networks are today responsible for about 20 - 25% of the aggregate information technology network (ITN) consumption including the data center, and will dramatically increase in the next decade due to new networks deployments and traffic demand exponential increase (about 30 to 40% per year).

In a short-term view, trends are moving towards making energy consumption more traffic load dependent. Actual macro BS is highly consuming even for low traffic load. A special care should be maintained for site-sharing initiatives (including outsourcing towards Tower Companies) to maintain assets in the green field. The technical environment (cooling, energy backup) shall not be neglected as they are highly consuming. Ventilation should be stated as a default installation configuration rather than active cooling. Passive (free) cooling can reduce the site cooling energy consumption by a factor of 8-10.

In a long-term vision, a global top-down approach is fundamental to balance the energy consumption regarding the traffic evolution in the next decade. A global approach allows to sustainably manage the evolution of our networks taking into account environmental impacts. Trends are towards new architectures that allow:

* Connecting large number of various devices and objects including critical ones.

* Geographical and social inclusion.

* Deployment everywhere.

* Efficient energy and natural resource usage.

* Trust and security.

* Collecting, processing and transporting a huge amount of data.

Those attractive concepts should be carefully studied mainly to avoid rebound effect on the backhaul and the core networks. Furthermore, hybrid energy renewable sources (PV, wind, batteries) should be optimized for diet-radio BSs especially for AMEA (Asia, Middle East and Africa) in accordance with natural energy assets in those countries.

Globally, the question of energy-free objects and network massive densification managed and orchestrated using artificial intelligence (AI) shall be the main driver for future networks. There is no magic response that should be applicable for any situation. This important question shall be considered regarding future disruptive services and corresponding networks urbanism regarding social acceptability and ethics. Indeed, we strongly believe that the future progress in mobile networks will face the question of privacy and security as a main driver.

Finally, intensive involvement in the standardization of green networks is the key to guarantee that green solutions
will be inter-operable, cost-efficient and ready in time. This requires more coordination efforts and a large consensus among the major stakeholders.

## B. FUTURE NETWORK ARCHITECTURES

The undergoing virtualization of mobile networks has not yet delivered the full promises on TCO gains and energy savings. In addition to normal transformation and learning curve, this can be explained by the lack of openness in the currently proposed architecture/solutions, resulting in heavy integration processes and limited interoperability.

To achieve the promise of becoming the ubiquitous fabric for universal connectivity, with ultra-dynamic slicing capabilities, future mobile networks will have to evolve towards far more open architectures, fully adopting the cloud-native principles, refactoring current VNFs into smaller pieces (micro-services), easily deployable and composable on Platforms (as a Service, PaaS) by highly-automated service/slice orchestrators. Substantial energy gains will be achievable only if such micro-services will have a sufficient granularity to allow very dynamic creation/scaling/deletion as function of e.g. service demand, traffic load, number of connected IoT objects,...etc. However, some critical challenges to such evolution still necessitate suitable solutions.

Full decomposition into fine-grained microservices of RAN functions, in particular those related to real-time PHY and MAC processing, is still to come. So far such functions are distributed as single applications, or at best packaged into two containers, grouping lower and higher layer functions. This considerably limits the flexibility gains of cloudification: by occupying fixed amount of resources (CPU, memory...), dimensioned at peak hour, this approach prevents the adoption of more energy-efficient scale-to-load solutions. However, moving into finer granularity requires to deal with specific hard constraints, in both processing time and inter-process delay, with available budget under the µsec scale. Microservice-RAN necessitates new inter-process communication means, as well as a smoother and cheaper integration of hardware accelerators.

Additional challenges come from the necessity of dealing with legacy network functions to ensure coexistence, interworking, refactoring and a smooth migration. Standardization fora, such as 3GPP, will have to integrate solutions from open-source communities, especially from the IT sector. Even if more political than technological, this remains as a major challenge to be tackled in the future.

## C. SMART GRIDS AND RENEWABLE ENERGY

5G networks go far beyond previous standards in data rates, diversity and other performance indicators, including EE which is the present focus of this work. In this paper, we analyzed how smart grids, RE sources and modern batteries such as Lithium along with suitable energy efficiency oriented KPIs allow significant energy savings and sustainability. This trend shall continue and even get amplified, thanks to the technological advances on one side, notably in terms of RE

production and storage, but also public awareness on the need to consume in a smarter and greener manner. Based on this, new proposals will always be needed not only to operate future networks in the most energy economical ways, but also to be able to produce and store “clean” energy.

If smart grids and RE can help improving energy efficiency towards and along 5G, 5G networks can conversely play a key role in the undergoing evolution of electrical grids. The electrical grid is getting smarter and more flexible giving access to new players to be part of the electricity supply chain in order to ensure an efficient balancing between supply and demand and optimize the transmission and the distribution of the electricity. This results in complex grids with high adaptation dynamics. The monitoring and the management of such smart grid networks will require a huge number of connections (wired and wireless) between customers and energy aggregators. Such high dynamics will require low latency in order to allow fast adaptation of the grid. In this context, thanks to the network slicing concept, mobile networks (5G and beyond) will play a key role to provide the required connectivity. The mobile operators are working on the definition of the dedicated network slices for the smart grid network carrying this massive M2M communications. These slices will need to be dynamically created and configured in order to respond to the smart grid service requirements enabling the aggregation of many players and many sensors for better centralized view on the grid and better power management.

## D. INDOOR SYSTEMS

In global wireless access over the upcoming years, the number of Wi-Fi indoor systems will continue to maintain and grow its strategic importance. In fact, according to Cisco Visual Networking Index [73], by 2021, Wi-Fi will account for 45.5% of global IP traffic, against 37.1% for wired and 17.4% for cellular. Therefore, in Wi-Fi, the need for energy efficient mechanisms remains to be a very relevant issue. For indoor systems, a significant improvement will arrive with the new Wi-Fi 6 (IEEE 802.11ax) standard. This standard is intended to achieve a closer interaction with cellular technologies, namely 5G, as both technologies are expanding their capabilities and will be able to take advantage of one another. In the same way as 5G, Wi-Fi 6 targets dense environments, such as stadiums, airports, and college campuses, along with a focus on IoT scenarios. Technologies such as Wi-Fi Offloading, Multipath TCP, Target Wake Time mechanism and SIM authentication with Passpoint, are a set of different ways in which 5G and Wi-Fi networks can provide better services to users and, simultaneously, to empower the development of advanced energy efficient systems.

Another emerging communication technology for indoor deployments is Light Communication, whom adoption curve is exiting the exploratory phase to enter the pre-industrialization phase, with a growing ecosystem (e.g. Light Communication Alliance) and some solid industrial use cases and deployments (for example for communication
will be inter-operable, cost-efficient and ready in time. This requires more coordination efforts and a large consensus among the major stakeholders.

## B. FUTURE NETWORK ARCHITECTURES

The undergoing virtualization of mobile networks has not yet delivered the full promises on TCO gains and energy savings. In addition to normal transformation and learning curve, this can be explained by the lack of openness in the currently proposed architecture/solutions, resulting in heavy integration processes and limited interoperability.

To achieve the promise of becoming the ubiquitous fabric for universal connectivity, with ultra-dynamic slicing capabilities, future mobile networks will have to evolve towards far more open architectures, fully adopting the cloud-native principles, refactoring current VNFs into smaller pieces (micro-services), easily deployable and composable on Platforms (as a Service, PaaS) by highly-automated service/slice orchestrators. Substantial energy gains will be achievable only if such micro-services will have a sufficient granularity to allow very dynamic creation/scaling/deletion as function of e.g. service demand, traffic load, number of connected IoT objects,...etc. However, some critical challenges to such evolution still necessitate suitable solutions.

Full decomposition into fine-grained microservices of RAN functions, in particular those related to real-time PHY and MAC processing, is still to come. So far such functions are distributed as single applications, or at best packaged into two containers, grouping lower and higher layer functions. This considerably limits the flexibility gains of cloudification: by occupying fixed amount of resources (CPU, memory...), dimensioned at peak hour, this approach prevents the adoption of more energy-efficient scale-to-load solutions. However, moving into finer granularity requires to deal with specific hard constraints, in both processing time and inter-process delay, with available budget under the µsec scale. Microservice-RAN necessitates new inter-process communication means, as well as a smoother and cheaper integration of hardware accelerators.

Additional challenges come from the necessity of dealing with legacy network functions to ensure coexistence, interworking, refactoring and a smooth migration. Standardization fora, such as 3GPP, will have to integrate solutions from open-source communities, especially from the IT sector. Even if more political than technological, this remains as a major challenge to be tackled in the future.

## C. SMART GRIDS AND RENEWABLE ENERGY

5G networks go far beyond previous standards in data rates, diversity and other performance indicators, including EE which is the present focus of this work. In this paper, we analyzed how smart grids, RE sources and modern batteries such as Lithium along with suitable energy efficiency oriented KPIs allow significant energy savings and sustainability. This trend shall continue and even get amplified, thanks to the technological advances on one side, notably in terms of RE

production and storage, but also public awareness on the need to consume in a smarter and greener manner. Based on this, new proposals will always be needed not only to operate future networks in the most energy economical ways, but also to be able to produce and store “clean” energy.

If smart grids and RE can help improving energy efficiency towards and along 5G, 5G networks can conversely play a key role in the undergoing evolution of electrical grids. The electrical grid is getting smarter and more flexible giving access to new players to be part of the electricity supply chain in order to ensure an efficient balancing between supply and demand and optimize the transmission and the distribution of the electricity. This results in complex grids with high adaptation dynamics. The monitoring and the management of such smart grid networks will require a huge number of connections (wired and wireless) between customers and energy aggregators. Such high dynamics will require low latency in order to allow fast adaptation of the grid. In this context, thanks to the network slicing concept, mobile networks (5G and beyond) will play a key role to provide the required connectivity. The mobile operators are working on the definition of the dedicated network slices for the smart grid network carrying this massive M2M communications. These slices will need to be dynamically created and configured in order to respond to the smart grid service requirements enabling the aggregation of many players and many sensors for better centralized view on the grid and better power management.

## D. INDOOR SYSTEMS

In global wireless access over the upcoming years, the number of Wi-Fi indoor systems will continue to maintain and grow its strategic importance. In fact, according to Cisco Visual Networking Index [73], by 2021, Wi-Fi will account for 45.5% of global IP traffic, against 37.1% for wired and 17.4% for cellular. Therefore, in Wi-Fi, the need for energy efficient mechanisms remains to be a very relevant issue. For indoor systems, a significant improvement will arrive with the new Wi-Fi 6 (IEEE 802.11ax) standard. This standard is intended to achieve a closer interaction with cellular technologies, namely 5G, as both technologies are expanding their capabilities and will be able to take advantage of one another. In the same way as 5G, Wi-Fi 6 targets dense environments, such as stadiums, airports, and college campuses, along with a focus on IoT scenarios. Technologies such as Wi-Fi Offloading, Multipath TCP, Target Wake Time mechanism and SIM authentication with Passpoint, are a set of different ways in which 5G and Wi-Fi networks can provide better services to users and, simultaneously, to empower the development of advanced energy efficient systems.

Another emerging communication technology for indoor deployments is Light Communication, whom adoption curve is exiting the exploratory phase to enter the pre-industrialization phase, with a growing ecosystem (e.g. Light Communication Alliance) and some solid industrial use cases and deployments (for example for communication
inside airplanes). This technology, specific to indoor communications, provides very high data rates in short-range conditions. In addition, its low power consumption, coupled with strong resistance to interference, makes it a strong candidate to improve energy efficiency in indoor communications. A smooth integration of these new technologies with cellular networks, for example as additional supported radio interfaces, is a challenging yet highly promising opportunity for improving the overall energy efficiency of wireless communications.

Actually, regardless of the wireless technology used, an indoor network has a growth margin for more efficiency, both in spectrum and energy resources. In view of that, Automation and AI frameworks are now valuable emerging tools that will help the achievement of superior link performance and network energy efficiency. With these tools, new and intelligent green mechanisms can be designed, maximizing spectrum efficiency and network capacity during periods of peak usage, while offloading and providing minimum resources during periods of low usage. AI/ML-based algorithms are also expected to help in improving location of user, which in turns enable precise and dynamic activation/deactivation of radio links, a further opportunity for energy reduction. However all these techniques must consider the additional costs of training and using AI-models, and attentive studies must prove the expected energy gains.

## E. USE OF ARTIFICIAL INTELLIGENCE

To capitalize on the long exerted efforts in SooGREEN summarized in this article, another EU Celtic-Plus project has been approved and started this year focusing to further employ the recent AI advances for greener networks. The envisioned efforts in the new project, entitled AI for Green Networks or AI4Green for short, target core and access network segments alike. We briefly highlight in the following some directions where AI is seen to offer remarkable savings and smarter control of network energy consumption.

The main objective of the AI4Green project is to leverage the available huge datasets of network measurements at the different network operators and equipment vendors, along with the rich literature and powerful tools of machine learning, big data and AI to optimally provision, operate, and maintain mobile networks. One direction is to perform techno-economic studies that investigate potential benefits of operators to reduce their energy consumption using marketplaces. Another direction is AI-based network planning and modeling of uncertain environments that cannot be analyzed otherwise using tractable analytical models. In this context, AI-based techniques prove to be efficient in learning them and drawing meaningful conclusions and recommendations. A third direction is to employ AI-based techniques for data-driven estimation and optimization of service energy consumption, power flow management, AI-based control of powering and cooling facilities, managing real-time

data transmission and processing, site analysis, the joint planning of computational and communication resources, among others.

## VII. CONCLUSION

In this article, ongoing efforts and future challenges on turning current mobile networks into green networks are discussed, with emphasis on those exerted and lessons learned in the recently concluded EU Celtic-Plus SooGREEN project. The discussions and contributions spanned a wide range of network segments and services, including means to monitor and evaluate the energy efficiency of networks, and several innovative solutions to reduce energy consumption. Proposed solutions for 5G networks and beyond included virtualized architectures for densified infrastructure, efficient polar coding for fronthauling, new designs for edge hardware accelerators, use of RE in mobile networks with local storage and smart grid integration, passive cooling for central offices, and enabling smart sleep modes for indoor WiFi networks and DAS systems. The efforts did not only include technical recommendations for network evolution, but also techno-economic studies for the migration of networks to these proposed solutions. Such studies are aimed to foster further technical discussions of the different stakeholders in the ICT sector, unifying academic and industrial expertise, to offer more attainable sustainability and cost efficiency in the currently fast growing and massively densified networks. Future trends are further emphasized, discussing the promising potential of AI in offering such a network smart agility to control real-time network monitoring and enable traffic-dependent network utilization.

## ACKNOWLEDGMENT

The authors would like to thank Abdulrahman Alabbasi, Didier Marquet, Mamdouh El-Tabach, Mats Nilsson, Wilfried Yoro and Xinbo Wang for their contributions and active participation in the EU Celtic-Plus SooGREEN Project and its associated publication, dissemination and standardization efforts. Authors P. Silva and N. T. Almeida would like to address a special thank to Dr. Rui Campos, from INESC TEC, for his inestimable and continuous contribution to the developed work as well as to Tiago Oliveira and Saravanan Kandasamy for their valuable participation in the initial phase of the project. They also would like to thank Pedro Rodrigues, José Fonseca, and Diogo Costa, all from Eurico Ferreira, for the helpful discussions and accomplished collaborations along the realization of this work.

## REFERENCES

[1] 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Release 15 Description; Summary of Rel-15 Work Items (Release 15), document TR 21.915 V0.6.0, 3GPP, Feb. 2019.

[2] M. Olsson, Ed., "Deliverable 6.4: Final integrated concept," EARTH, Tech. Rep. INFSO-ICT-247733, 2012.

[3] Green Touch Foundation, "GreenTouch final results from Green Meter research study," GreenTouch Found., Wakefield, MA, USA, White Paper V1.0, Jun. 2015. [Online]. Available: https://pdfs.semanticscholar.org/5343/fbf14ad7c4affeb56166ad9f0cd54681d64d.pdf
inside airplanes). This technology, specific to indoor communications, provides very high data rates in short-range conditions. In addition, its low power consumption, coupled with strong resistance to interference, makes it a strong candidate to improve energy efficiency in indoor communications. A smooth integration of these new technologies with cellular networks, for example as additional supported radio interfaces, is a challenging yet highly promising opportunity for improving the overall energy efficiency of wireless communications.

Actually, regardless of the wireless technology used, an indoor network has a growth margin for more efficiency, both in spectrum and energy resources. In view of that, Automation and AI frameworks are now valuable emerging tools that will help the achievement of superior link performance and network energy efficiency. With these tools, new and intelligent green mechanisms can be designed, maximizing spectrum efficiency and network capacity during periods of peak usage, while offloading and providing minimum resources during periods of low usage. AI/ML-based algorithms are also expected to help in improving location of user, which in turns enable precise and dynamic activation/deactivation of radio links, a further opportunity for energy reduction. However all these techniques must consider the additional costs of training and using AI-models, and attentive studies must prove the expected energy gains.

## E. USE OF ARTIFICIAL INTELLIGENCE

To capitalize on the long exerted efforts in SooGREEN summarized in this article, another EU Celtic-Plus project has been approved and started this year focusing to further employ the recent AI advances for greener networks. The envisioned efforts in the new project, entitled AI for Green Networks or AI4Green for short, target core and access network segments alike. We briefly highlight in the following some directions where AI is seen to offer remarkable savings and smarter control of network energy consumption.

The main objective of the AI4Green project is to leverage the available huge datasets of network measurements at the different network operators and equipment vendors, along with the rich literature and powerful tools of machine learning, big data and AI to optimally provision, operate, and maintain mobile networks. One direction is to perform techno-economic studies that investigate potential benefits of operators to reduce their energy consumption using marketplaces. Another direction is AI-based network planning and modeling of uncertain environments that cannot be analyzed otherwise using tractable analytical models. In this context, AI-based techniques prove to be efficient in learning them and drawing meaningful conclusions and recommendations. A third direction is to employ AI-based techniques for data-driven estimation and optimization of service energy consumption, power flow management, AI-based control of powering and cooling facilities, managing real-time

data transmission and processing, site analysis, the joint planning of computational and communication resources, among others.

## VII. CONCLUSION

In this article, ongoing efforts and future challenges on turning current mobile networks into green networks are discussed, with emphasis on those exerted and lessons learned in the recently concluded EU Celtic-Plus SooGREEN project. The discussions and contributions spanned a wide range of network segments and services, including means to monitor and evaluate the energy efficiency of networks, and several innovative solutions to reduce energy consumption. Proposed solutions for 5G networks and beyond included virtualized architectures for densified infrastructure, efficient polar coding for fronthauling, new designs for edge hardware accelerators, use of RE in mobile networks with local storage and smart grid integration, passive cooling for central offices, and enabling smart sleep modes for indoor WiFi networks and DAS systems. The efforts did not only include technical recommendations for network evolution, but also techno-economic studies for the migration of networks to these proposed solutions. Such studies are aimed to foster further technical discussions of the different stakeholders in the ICT sector, unifying academic and industrial expertise, to offer more attainable sustainability and cost efficiency in the currently fast growing and massively densified networks. Future trends are further emphasized, discussing the promising potential of AI in offering such a network smart agility to control real-time network monitoring and enable traffic-dependent network utilization.

## ACKNOWLEDGMENT

The authors would like to thank Abdulrahman Alabbasi, Didier Marquet, Mamdouh El-Tabach, Mats Nilsson, Wilfried Yoro and Xinbo Wang for their contributions and active participation in the EU Celtic-Plus SooGREEN Project and its associated publication, dissemination and standardization efforts. Authors P. Silva and N. T. Almeida would like to address a special thank to Dr. Rui Campos, from INESC TEC, for his inestimable and continuous contribution to the developed work as well as to Tiago Oliveira and Saravanan Kandasamy for their valuable participation in the initial phase of the project. They also would like to thank Pedro Rodrigues, José Fonseca, and Diogo Costa, all from Eurico Ferreira, for the helpful discussions and accomplished collaborations along the realization of this work.

## REFERENCES

[1] 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Release 15 Description; Summary of Rel-15 Work Items (Release 15), document TR 21.915 V0.6.0, 3GPP, Feb. 2019.

[2] M. Olsson, Ed., "Deliverable 6.4: Final integrated concept," EARTH, Tech. Rep. INFSO-ICT-247733, 2012.

[3] Green Touch Foundation, "GreenTouch final results from Green Meter research study," GreenTouch Found., Wakefield, MA, USA, White Paper V1.0, Jun. 2015. [Online]. Available: https://pdfs.semanticscholar.org/5343/fbf14ad7c4affeb56166ad9f0cd54681d64d.pdf
[4] M. Aubree, D. Marquet, S. Le Masson, H. Louahlia, A. Chehade, J. David, and F. Van Goethem, "OPERA-Net 2 project—An environmental global approach for radio access networks—achievements for off-grid systems," in *Proc. IEEE 36th Int. Telecommun. Energy Conf. (INTELEC)*, Sep./Oct. 2014, pp. 1–7.

[5] M. Olsson, C. Cavdar, P. Frenger, S. Tombaz, D. Sabella, and R. Janti, "5GReEEN: Towards green 5G mobile networks," in *Proc. IEEE 9th Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob)*, Oct. 2013, pp. 212–216.

[6] G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson, M. A. Imran, D. Sabella, M. J. Gonzalez, O. Blume, and A. Fehske, "How much energy is needed to run a wireless network?" *IEEE Trans. Wireless Commun.*, vol. 18, no. 5, pp. 40–49, Oct. 2011.

[7] M. M. A. Hossain, R. Jäntti, and C. Cavdar, "Dimensioning of PA for massive MIMO system with load adaptive number of antennas," in *Proc. IEEE Globecom Workshops (GC Wkshps)*, Dec. 2014, pp. 1102–1108.

[8] A. A. W. Ahmed, J. Markendahl, C. Cavdar, and A. Ghanbari, "Study on the effects of backhaul solutions on indoor mobile deployment 'macrocell vs. femtocell'," in *Proc. IEEE PIMRC*, Sep. 2013, pp. 2444–2448.

[9] A. A. W. Ahmed, J. Markendahl, and C. Cavdar, "Interplay between cost, capacity and power consumption in heterogeneous mobile networks," in *Proc. 21st Int. Conf. Telecommun. (ICT)*, May 2014, pp. 98–102.

[10] P. Frenger, M. Olsson, and E. Eriksson, "A clean slate radio network designed for maximum energy performance," in *Proc. IEEE PIMRC*, Sep. 2014, pp. 1300–1304.

[11] China Mobile, "C-RAN: The road towards green RAN," China Mobile Res. Inst., White Paper, Version 2.5, Oct. 2011, vol. 2, pp. 1–10. [Online]. Available: https://pdfs.semanticscholar.org/ea3/ca62c9d5653e4f2318aed9ddb8992a505d3c.pdf

[12] *Environmental Engineering (EE); Assessment of Mobile Network Energy Efficiency*—Annex B: *Cloud RAN Energy Efficiency*, Standard ETSI ES 203 228 V1.2.1, Apr. 2017.

[13] X. Wang, L. Wang, C. Cavdar, M. Tornatore, G. B. Figueiredo, H. S. Chung, H. H. Lee, S. Park, and B. Mukherjee, "Handover reduction in virtualized cloud radio access networks using TWDM-PON fronthaul," *IEEE/OSA J. Opt. Commun. Netw.*, vol. 8, no. 12, pp. B124–B134, Dec. 2016.

[14] X. Wang, C. Cavdar, L. Wang, M. Tornatore, Y. Zhao, H. S. Chung, H. H. Lee, S. Park, and B. Mukherjee, "Joint allocation of radio and optical resources in virtualized cloud RAN with CoMP," in *Proc. IEEE GLOBECOM*, Dec. 2016, pp. 1–6.

[15] X. Wang, L. Wang, S. E. Elayoubi, A. Conte, B. Mukherjee, and C. Cavdar, "Centralize or distribute? A techno-economic study to design a low-cost cloud radio access network," in *Proc. IEEE ICC*, May 2017, pp. 1–7.

[16] X. Wang, A. Alabbasi, and C. Cavdar, "Interplay of energy and bandwidth consumption in CRAN with optimal function split," in *Proc. IEEE ICC*, May 2017, pp. 1–6.

[17] A. Alabbasi and C. Cavdar, "Delay-aware green hybrid CRAN," in *Proc. 15th Int. Symp. Modeling Optim. Mobile, Ad Hoc, Wireless Netw. (WiOpt)*, May 2017, pp. 1–7.

[18] X. Wang, C. Cavdar, L. Wang, M. Tornatore, H. S. Chung, H. H. Lee, S. M. Park, and B. Mukherjee, "Virtualized cloud radio access network for 5G transport," *IEEE Commun. Mag.*, vol. 55, no. 9, pp. 202–209, Sep. 2017.

[19] A. Alabbasi, X. Wang, and C. Cavdar, "Optimal processing allocation to minimize energy and bandwidth consumption in hybrid CRAN," *IEEE Trans. Green Commun. Netw.*, vol. 2, no. 2, pp. 545–555, Jun. 2018.

[20] M. Masoudi, B. Khamidehi, and C. Cavdar, "Green cloud computing for multi cell networks," in *Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)*, Mar. 2017, pp. 1–6.

[21] M. Masoudi and C. Cavdar, "Device vs edge computing for mobile services: Delay-aware decision making to minimize power consumption," 2017, arXiv:1711.03771. [Online]. Available: https://arxiv.org/abs/1711.03771

[22] A. Sriram, M. Masoudi, A. Alabbasi, and C. Cavdar, "Joint functional splitting and content placement for green hybrid CRAN," in *Proc. IEEE 30th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC)*, Istanbul, Turkey, 2019, pp. 1–7.

[23] H. A. H. Hassan, L. Nuaymi, and A. Pelov, "Renewable energy in cellular networks: A survey," in *Proc. IEEE Online Conf. Green Commun. (OnlineGreenComm)*, Oct. 2013, pp. 1–7.

[24] A. El-Amine, H. A. H. Hassan, and L. Nuaymi, "Analysis of energy and cost savings in hybrid base stations power configurations," in *Proc. IEEE 87th Veh. Technol. Conf. (VTC Spring)*, Jun. 2018, pp. 1–7.

[25] A. El-Amine, H. A. H. Hassan, and L. Nuaymi, "Services KPI-based energy management strategies for green wireless networks," in *Proc. IEEE 29th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC)*, Sep. 2018, pp. 1–7.

[26] A. El-Amine, H. A. H. Hassan, and L. Nuaymi, "Energy and resource allocations for battery aging-aware green cellular networks," in *Proc. IEEE Global Commun. Conf. (GLOBECOM)*, Dec. 2018, pp. 1–7.

[27] W. Labidi, T. Chahed, and S.-E. Elayoubi, "Battery management for demand-response in mobile networks powered by a smart grid," in *Proc. IEEE 29th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC)*, Sep. 2018, pp. 1–7.

[28] W. Labidi, T. Chahed, and S. E. Elayoubi, "Optimal battery management strategies in mobile networks powered by a smart grid," *IEEE Trans. Green Commun. Netw.*, vol. 2, no. 3, pp. 859–867, Sep. 2018.

[29] C. Nadjahi, H. Louahlia, A. Conte, and S. Le Masson, "Passive cooling and thermal management in data centers," in *Proc. IEEE Int. Telecommun. Energy Conf. (INTELEC)*, Oct. 2017, pp. 85–90.

[30] C. Nadjahi, H. Louahlia, and S. Lemasson, "A review of thermal management and innovative cooling strategies for data center," *Sustain. Comput., Inform. Syst.*, vol. 19, pp. 14–28, Sep. 2018.

[31] H. Rocha, T. Caçoilo, P. Rodrigues, S. Kandasamy, and R. Campos, "Wi-Green: Optimization of the power consumption of Wi-Fi networks sensitive to traffic patterns invited paper," in *Proc. 15th Int. Symp. Modeling Optim. Mobile, Ad Hoc, Wireless Netw. (WiOpt)*, May 2017, pp. 1–5.

[32] P. Silva, N. T. Almeida, and R. Campos, "Energy consumption management for dense Wi-Fi networks," in *Proc. 11th Annu. Wireless Days Conf., Manchester, U.K.*, Apr. 2019, pp. 1–8.

[33] *Environmental Engineering (EE); Metrics and Measurement Method for Energy Efficiency of Wireless Access Network Equipment*, Standard ETSI ES 202 706-1 V1.5.0, Oct. 2016.

[34] A. Bourdon, A. Noureddine, R. Rouvoy, and L. Seinturier, "PowerAPI: A software library to monitor the energy consumed at the process-level," in *ERCIM News* (Special Theme: Smart Energy Systems), vol. 92. ERCIM, Jan. 2013, pp. 43–44. [Online]. Available: https://hal.inria.fr/hal-00772454

[35] A. Noureddine, R. Rouvoy, and L. Seinturier, "A review of energy measurement approaches," *SIGOPS Oper. Syst. Rev.*, vol. 47, no. 3, pp. 42–49, Nov. 2013. doi: 10.1145/2553070.2553077.

[36] A. Maeder, M. Lalam, A. De Domenico, E. Pateromichelakis, D. Wübben, J. Bartelt, R. Fritzsche, and P. Rost, "Towards a flexible functional split for cloud-RAN networks," in *Proc. Eur. Conf. Netw. Commun. (EuCNC)*, Jun. 2014, pp. 1–5.

[37] J. Liu, S. Zhou, J. Gong, Z. Niu, and S. Xu, "Graph-based framework for flexible baseband function splitting and placement in C-RAN," in *Proc. ICC*, London, U.K., Jun. 2015, pp. 1958–1963.

[38] A. Checko, A. P. Avramova, M. S. Berger, and H. L. Christiansen, "Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings," *J. Commun. Netw.*, vol. 18, no. 2, pp. 162–172, Apr. 2016.

[39] J. Liu, S. Xu, S. Zhou, and Z. Niu, "Redesigning fronthaul for next-generation networks: Beyond baseband samples and point-to-point links," *IEEE Wireless Commun.*, vol. 22, no. 5, pp. 90–97, Oct. 2015.

[40] A. Checko, A. C. Juul, H. L. Christiansen, and M. S. Berger, "Synchronization challenges in packet-based cloud-RAN fronthaul for mobile networks," in *Proc. ICCW*, London, U.K., Jun. 2015, pp. 2721–2726.

[41] M. Vincenzi, A. Antonopoulos, E. Kartsakli, J. Vardakas, L. Alonso, and C. Verikoukis, "Cooperation incentives for multi-operator C-RAN energy efficient sharing," in *Proc. ICC*, Paris, France, May 2017, pp. 1–6.

[42] G. Tseliou, F. Adelantado, and C. Verikoukis, "Scalable RAN virtualization in multitenant LTE-A heterogeneous networks," *IEEE Trans. Veh. Technol.*, vol. 65, no. 8, pp. 6651–6664, Sep. 2016.

[43] H. Beyranvand, M. Lévesque, M. Maier, J. A. Salehi, C. Verikoukis, and D. Tipper, "Toward 5G: FiWi enhanced LTE-A HetNets with reliable low-latency fiber backhaul sharing and WiFi offloading," *IEEE/ACM Trans. Netw.*, vol. 25, no. 2, pp. 690–707, Apr. 2017.

[44] A. Bousia, E. Kartsakli, A. Antonopoulos, L. Alonso, and C. Verikoukis, "Multiobjective auction-based switching-off scheme in heterogeneous networks: To bid or not to bid?" *IEEE Trans. Veh. Technol.*, vol. 65, no. 11, pp. 9168–9180, Nov. 2016.

[45] Z. Kaleem, M. Yousaf, A. Qamar, A. Ahmad, T. Q. Duong, W. Choi, and A. Jamalipour, "UAV-empowered disaster-resilient edge architecture for delay-sensitive communication," *IEEE Netw.*, to be published.
[4] M. Aubree, D. Marquet, S. Le Masson, H. Louahlia, A. Chehade, J. David, and F. Van Goethem, "OPERA-Net 2 project—An environmental global approach for radio access networks—achievements for off-grid systems," in *Proc. IEEE 36th Int. Telecommun. Energy Conf. (INTELEC)*, Sep./Oct. 2014, pp. 1–7.

[5] M. Olsson, C. Cavdar, P. Frenger, S. Tombaz, D. Sabella, and R. Janti, "5GReEEN: Towards green 5G mobile networks," in *Proc. IEEE 9th Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob)*, Oct. 2013, pp. 212–216.

[6] G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson, M. A. Imran, D. Sabella, M. J. Gonzalez, O. Blume, and A. Fehske, "How much energy is needed to run a wireless network?" *IEEE Trans. Wireless Commun.*, vol. 18, no. 5, pp. 40–49, Oct. 2011.

[7] M. M. A. Hossain, R. Jäntti, and C. Cavdar, "Dimensioning of PA for massive MIMO system with load adaptive number of antennas," in *Proc. IEEE Globecom Workshops (GC Wkshps)*, Dec. 2014, pp. 1102–1108.

[8] A. A. W. Ahmed, J. Markendahl, C. Cavdar, and A. Ghanbari, "Study on the effects of backhaul solutions on indoor mobile deployment 'macrocell vs. femtocell'," in *Proc. IEEE PIMRC*, Sep. 2013, pp. 2444–2448.

[9] A. A. W. Ahmed, J. Markendahl, and C. Cavdar, "Interplay between cost, capacity and power consumption in heterogeneous mobile networks," in *Proc. 21st Int. Conf. Telecommun. (ICT)*, May 2014, pp. 98–102.

[10] P. Frenger, M. Olsson, and E. Eriksson, "A clean slate radio network designed for maximum energy performance," in *Proc. IEEE PIMRC*, Sep. 2014, pp. 1300–1304.

[11] China Mobile, "C-RAN: The road towards green RAN," China Mobile Res. Inst., White Paper, Version 2.5, Oct. 2011, vol. 2, pp. 1–10. [Online]. Available: https://pdfs.semanticscholar.org/ea3/ca62c9d5653e4f2318aed9ddb8992a505d3c.pdf

[12] *Environmental Engineering (EE); Assessment of Mobile Network Energy Efficiency*—Annex B: *Cloud RAN Energy Efficiency*, Standard ETSI ES 203 228 V1.2.1, Apr. 2017.

[13] X. Wang, L. Wang, C. Cavdar, M. Tornatore, G. B. Figueiredo, H. S. Chung, H. H. Lee, S. Park, and B. Mukherjee, "Handover reduction in virtualized cloud radio access networks using TWDM-PON fronthaul," *IEEE/OSA J. Opt. Commun. Netw.*, vol. 8, no. 12, pp. B124–B134, Dec. 2016.

[14] X. Wang, C. Cavdar, L. Wang, M. Tornatore, Y. Zhao, H. S. Chung, H. H. Lee, S. Park, and B. Mukherjee, "Joint allocation of radio and optical resources in virtualized cloud RAN with CoMP," in *Proc. IEEE GLOBECOM*, Dec. 2016, pp. 1–6.

[15] X. Wang, L. Wang, S. E. Elayoubi, A. Conte, B. Mukherjee, and C. Cavdar, "Centralize or distribute? A techno-economic study to design a low-cost cloud radio access network," in *Proc. IEEE ICC*, May 2017, pp. 1–7.

[16] X. Wang, A. Alabbasi, and C. Cavdar, "Interplay of energy and bandwidth consumption in CRAN with optimal function split," in *Proc. IEEE ICC*, May 2017, pp. 1–6.

[17] A. Alabbasi and C. Cavdar, "Delay-aware green hybrid CRAN," in *Proc. 15th Int. Symp. Modeling Optim. Mobile, Ad Hoc, Wireless Netw. (WiOpt)*, May 2017, pp. 1–7.

[18] X. Wang, C. Cavdar, L. Wang, M. Tornatore, H. S. Chung, H. H. Lee, S. M. Park, and B. Mukherjee, "Virtualized cloud radio access network for 5G transport," *IEEE Commun. Mag.*, vol. 55, no. 9, pp. 202–209, Sep. 2017.

[19] A. Alabbasi, X. Wang, and C. Cavdar, "Optimal processing allocation to minimize energy and bandwidth consumption in hybrid CRAN," *IEEE Trans. Green Commun. Netw.*, vol. 2, no. 2, pp. 545–555, Jun. 2018.

[20] M. Masoudi, B. Khamidehi, and C. Cavdar, "Green cloud computing for multi cell networks," in *Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)*, Mar. 2017, pp. 1–6.

[21] M. Masoudi and C. Cavdar, "Device vs edge computing for mobile services: Delay-aware decision making to minimize power consumption," 2017, arXiv:1711.03771. [Online]. Available: https://arxiv.org/abs/1711.03771

[22] A. Sriram, M. Masoudi, A. Alabbasi, and C. Cavdar, "Joint functional splitting and content placement for green hybrid CRAN," in *Proc. IEEE 30th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC)*, Istanbul, Turkey, 2019, pp. 1–7.

[23] H. A. H. Hassan, L. Nuaymi, and A. Pelov, "Renewable energy in cellular networks: A survey," in *Proc. IEEE Online Conf. Green Commun. (OnlineGreenComm)*, Oct. 2013, pp. 1–7.

[24] A. El-Amine, H. A. H. Hassan, and L. Nuaymi, "Analysis of energy and cost savings in hybrid base stations power configurations," in *Proc. IEEE 87th Veh. Technol. Conf. (VTC Spring)*, Jun. 2018, pp. 1–7.

[25] A. El-Amine, H. A. H. Hassan, and L. Nuaymi, "Services KPI-based energy management strategies for green wireless networks," in *Proc. IEEE 29th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC)*, Sep. 2018, pp. 1–7.

[26] A. El-Amine, H. A. H. Hassan, and L. Nuaymi, "Energy and resource allocations for battery aging-aware green cellular networks," in *Proc. IEEE Global Commun. Conf. (GLOBECOM)*, Dec. 2018, pp. 1–7.

[27] W. Labidi, T. Chahed, and S.-E. Elayoubi, "Battery management for demand-response in mobile networks powered by a smart grid," in *Proc. IEEE 29th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC)*, Sep. 2018, pp. 1–7.

[28] W. Labidi, T. Chahed, and S. E. Elayoubi, "Optimal battery management strategies in mobile networks powered by a smart grid," *IEEE Trans. Green Commun. Netw.*, vol. 2, no. 3, pp. 859–867, Sep. 2018.

[29] C. Nadjahi, H. Louahlia, A. Conte, and S. Le Masson, "Passive cooling and thermal management in data centers," in *Proc. IEEE Int. Telecommun. Energy Conf. (INTELEC)*, Oct. 2017, pp. 85–90.

[30] C. Nadjahi, H. Louahlia, and S. Lemasson, "A review of thermal management and innovative cooling strategies for data center," *Sustain. Comput., Inform. Syst.*, vol. 19, pp. 14–28, Sep. 2018.

[31] H. Rocha, T. Caçoilo, P. Rodrigues, S. Kandasamy, and R. Campos, "Wi-Green: Optimization of the power consumption of Wi-Fi networks sensitive to traffic patterns invited paper," in *Proc. 15th Int. Symp. Modeling Optim. Mobile, Ad Hoc, Wireless Netw. (WiOpt)*, May 2017, pp. 1–5.

[32] P. Silva, N. T. Almeida, and R. Campos, "Energy consumption management for dense Wi-Fi networks," in *Proc. 11th Annu. Wireless Days Conf., Manchester, U.K.*, Apr. 2019, pp. 1–8.

[33] *Environmental Engineering (EE); Metrics and Measurement Method for Energy Efficiency of Wireless Access Network Equipment*, Standard ETSI ES 202 706-1 V1.5.0, Oct. 2016.

[34] A. Bourdon, A. Noureddine, R. Rouvoy, and L. Seinturier, "PowerAPI: A software library to monitor the energy consumed at the process-level," in *ERCIM News* (Special Theme: Smart Energy Systems), vol. 92. ERCIM, Jan. 2013, pp. 43–44. [Online]. Available: https://hal.inria.fr/hal-00772454

[35] A. Noureddine, R. Rouvoy, and L. Seinturier, "A review of energy measurement approaches," *SIGOPS Oper. Syst. Rev.*, vol. 47, no. 3, pp. 42–49, Nov. 2013. doi: 10.1145/2553070.2553077.

[36] A. Maeder, M. Lalam, A. De Domenico, E. Pateromichelakis, D. Wübben, J. Bartelt, R. Fritzsche, and P. Rost, "Towards a flexible functional split for cloud-RAN networks," in *Proc. Eur. Conf. Netw. Commun. (EuCNC)*, Jun. 2014, pp. 1–5.

[37] J. Liu, S. Zhou, J. Gong, Z. Niu, and S. Xu, "Graph-based framework for flexible baseband function splitting and placement in C-RAN," in *Proc. ICC*, London, U.K., Jun. 2015, pp. 1958–1963.

[38] A. Checko, A. P. Avramova, M. S. Berger, and H. L. Christiansen, "Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings," *J. Commun. Netw.*, vol. 18, no. 2, pp. 162–172, Apr. 2016.

[39] J. Liu, S. Xu, S. Zhou, and Z. Niu, "Redesigning fronthaul for next-generation networks: Beyond baseband samples and point-to-point links," *IEEE Wireless Commun.*, vol. 22, no. 5, pp. 90–97, Oct. 2015.

[40] A. Checko, A. C. Juul, H. L. Christiansen, and M. S. Berger, "Synchronization challenges in packet-based cloud-RAN fronthaul for mobile networks," in *Proc. ICCW*, London, U.K., Jun. 2015, pp. 2721–2726.

[41] M. Vincenzi, A. Antonopoulos, E. Kartsakli, J. Vardakas, L. Alonso, and C. Verikoukis, "Cooperation incentives for multi-operator C-RAN energy efficient sharing," in *Proc. ICC*, Paris, France, May 2017, pp. 1–6.

[42] G. Tseliou, F. Adelantado, and C. Verikoukis, "Scalable RAN virtualization in multitenant LTE-A heterogeneous networks," *IEEE Trans. Veh. Technol.*, vol. 65, no. 8, pp. 6651–6664, Sep. 2016.

[43] H. Beyranvand, M. Lévesque, M. Maier, J. A. Salehi, C. Verikoukis, and D. Tipper, "Toward 5G: FiWi enhanced LTE-A HetNets with reliable low-latency fiber backhaul sharing and WiFi offloading," *IEEE/ACM Trans. Netw.*, vol. 25, no. 2, pp. 690–707, Apr. 2017.

[44] A. Bousia, E. Kartsakli, A. Antonopoulos, L. Alonso, and C. Verikoukis, "Multiobjective auction-based switching-off scheme in heterogeneous networks: To bid or not to bid?" *IEEE Trans. Veh. Technol.*, vol. 65, no. 11, pp. 9168–9180, Nov. 2016.

[45] Z. Kaleem, M. Yousaf, A. Qamar, A. Ahmad, T. Q. Duong, W. Choi, and A. Jamalipour, "UAV-empowered disaster-resilient edge architecture for delay-sensitive communication," *IEEE Netw.*, to be published.
[46] OpenCL. Open Computing Language. Accessed: 2016. [Online]. Available: https://www.khronos.org/opencl/

[47] T. S. Czajkowski, U. Aydonat, D. Denisenko, J. Freeman, M. Kinsner, D. Neto, J. Wong, P. Yiannacouras, and D. P. Singh, "From openCL to high-performance hardware on FPGAS," in *Proc. 22nd Int. Conf. Field Program. Logic Appl. (FPL)*, Aug. 2012, pp. 531–534.

[48] HSA Foundation. Accessed: 2016. [Online]. Available: www.hsafoundation.com/

[49] J. Cao and M. Yang, "Energy Internet—Towards smart grid 2.0," in *Proc. 4th Int. Conf. Netw. Distrib. Comput.*, Dec. 2013, pp. 105–110.

[50] R. Ramamonjison and V. K. Bhargava, "Energy allocation and cooperation for energy-efficient wireless two-tier networks," *IEEE Trans. Wireless Commun.*, vol. 15, no. 9, pp. 6434–6448, Sep. 2016.

[51] M. Mendil, A. De Domenico, V. Heiries, R. Caire, and N. Hadjsaid, "Battery-aware optimization of green small cells: Sizing and energy management," *IEEE Trans. Green Commun. Netw.*, vol. 2, no. 3, pp. 635–651, Sep. 2018.

[52] E. Altman, *Constrained Markov Decision Processes*, vol. 7. Boca Raton, FL, USA: CRC Press, 1999.

[53] D. P. Bertsekas, *Dynamic Programming and Optimal Control*, 4th ed. Belmont, MA, USA: Athena Scientific, 2005.

[54] D. Marquet, B. Françoise, O. Foucault, S. Vaija, E. Philipot, and C. Campion, "How to transform innovative battery opportunities in field operational solutions for telecom/IT," in *Proc. IEEE Int. Telecommun. Energy Conf. (INTELEC)*, Oct. 2018, pp. 1–8.

[55] *Innovative Energy Storage Technology for Stationary Use—Part 1: Overview of Energy Storage*, document Rec. ITU-T L.1220, Aug. 2017.

[56] *Environmental Engineering (EE); Powering of Equipment in Access Network*, Standard ETSI EN 302 099 V2.1.0, Jun. 2014.

[57] *Progressive Migration of a Telecommunication/Information and Communication Technology Site to 400 VDC Sources and Distribution*, document Rec. ITU-T L.1207, May 2018.

[58] *Sustainable Power Feeding Solutions for 5G Network*, document ITU-T L.5G, 2019.

[59] J. G. Koomey, "Worldwide electricity used in data centers," *Environ. Res. Lett.*, vol. 3, no. 3, 2008, Art. no. 034008.

[60] *Electricity Information*, Int. Energy Agency, Paris, France, 2010.

[61] S. V. Garimella, L.-T. Yeh, and T. Persoons, "Thermal management challenges in telecommunication systems and data centers," *IEEE Trans. Compon., Packag., Manuf. Technol.*, vol. 2, no. 8, pp. 1307–1316, Aug. 2012.

[62] C. Belady, A. Rawson, J. Pfleuger, and T. Cader, "Green grid data center power efficiency metrics: PUE and DCIE," Green Grid, Beaverton, OR, USA, White Paper WP #6, 2008.

[63] D. Redford, "Thermal management supporting networks in transition," in *Proc. Workshop Therm. Manag. Telecommun. Syst. Data Centers*, 2010, pp. 25–26.

[64] *Hitachi Europe*, Press Release, Amsterdam, The Netherlands, Sep. 2010.

[65] S. Greenberg, E. Mills, B. Tschudi, and P. Rumsey, "Myatt. Best practices for data centers: Results from benchmarking 22 datacenters," in *Proc. ACEEE Summer Study Energy Efficiency Buildings*, vol. 16, 2006.

[66] *ABI Research*. (2015). *In-Building Mobile Data Traffic Forecast*. Austin, Tx, USA. [Online]. Available: https://www.abiresearch.com/marketresearch/product/1023888-in-building-mobile-data-trafficforecast/

[67] L. M. Hilty, V. Coroama, M. O. de Ecker, T. F. Ruddy, and E. Müller, "The role of ICT in energy consumption and energy efficiency," Rep. Eur. Commission, DG INFSO, Project ICT ENSURE, Eur. ICT Environ. Sustainability Res., Graz Univ., Graz, Austria, Tech. Rep., 2009, vol. 1, pp. 1–60.

[68] A. P. Jardosh, K. Papagiannaki, E. M. Belding, K. C. Almeroth, G. Iannaccone, and B. Vinnakota, "Green WLANs: On-demand WLAN infrastructures," *Mobile Netw. Appl.*, vol. 14, pp. 798–814, Dec. 2009.

[69] *IEEE Standard for Information Technology—Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications*, IEEE Standard 802.11-2016 (Revision of IEEE Std 802.11-2012), Dec. 2016, pp. 1–3534.

[70] J. Wang, H. Zhu, and N. J. Gomes, "Distributed antenna systems for mobile communications in high speed trains," *IEEE J. Sel. Areas Commun.*, vol. 30, no. 4, pp. 675–683, May 2012.

[71] E. F. J. de Mulder, C. C. D. F. van Ree, and H. R. G. K. Hack, "Geo-environmental aspects of European underground infrastructure," in *Environmental Security of the European Cross-Border Energy Supply Infrastructure*. Dordrecht, The Netherlands: Springer, 2015, pp. 133–153.

[72] OVO Energy. *How Much Energy Do You Use to Heat Your Home?* [Online]. Available: https://www.ovoenergy.com/guides/energy-guides/how-muchheating-energy-do-you-use.html

[73] *Cisco Global Cloud Index: Forecast and Methodology, 2016–2021*, Cisco, San Jose, CA, USA, 2016.

<figure><img src="image_33.png" /></figure>

**MEYSAM MASOUDI** (S'16) received the B.Sc. and M.Sc. degrees in electrical engineering from the Amirkabir University of Technology, Iran, in 2013 and 2016, respectively. He is currently pursuing the Ph.D. degree with the Department of Communication Systems, KTH Royal Institute of Technology, Sweden. His research interests include mobile cloud computing, 5G network architectures, and optimization.

<figure><img src="image_34.png" /></figure>

**MOHAMMAD GALAL KHAFAGY** (S'06–M'15–SM'17) received the B.Sc. degree in electrical engineering from the Faculty of Engineering, Alexandria University, Egypt, in 2009, the M.Sc. degree in wireless communications from the School of Communication and Information Technology, Nile University, Egypt, in 2011, and the Ph.D. degree from the King Abdullah University of Science and Technology (KAUST), Thuwal, Makah, Saudi Arabia, in 2016. From 2016 to 2018,

he was a Postdoctoral Researcher at Qatar University, Doha, Qatar. He joined the Wireless Intelligent Networks Center (WINC), Nile University, Egypt, as a Graduate Research Assistant, in 2009. He is currently a Postdoctoral Fellow with the Division of Communication Systems, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, Stockholm, Sweden. His research interests include multiple-antenna techniques, relay-aided communication, performance analysis and protocol design over fading channels, buffer-aided cooperation, and statistical signal processing for communications.

<figure><img src="image_35.png" /></figure>

**ALBERTO CONTE** (M'10) received the M.S. degree in corporate communications and telecommunication engineering from the Institut Eurécom, France, in 1996, and the Politecnico di Torino, Italy, in 1997. He joined the Software Department of Bell Labs, initially working on embedded systems and VoIP. He then shifted his research interests to wireless/mobile technologies for both enterprises (Wi-Fi) and public/operated cellular systems. He and his team pioneered the

definition of 4G end-to-end mobile networks and delivered solutions for improving their energy efficiency. He is currently the Head of the Cloud-Native Telecom Platforms Automation Department at Nokia Bell Labs, France. He participates in several collaborative EU projects and is an Active Lecturer in LTE technology with Telecom ParisTech, INSA-Lyon and Supelec. He has authored several publications and patents. His current research interests focus on 5G networks with the application of software defined networking (SDN), cloudification, and machine learning.

<figure><img src="image_36.png" /></figure>

**ALI EL-AMINE** (S'16) received the B.E. degree in electrical engineering and the M.S. degree in computer engineering from Lebanese American University (LAU), in 2013 and 2016, respectively. He is currently pursuing the Ph.D. degree in computer and networking with IMT Atlantique (formerly Telecom Bretagne), Rennes, France. His research interests focus on green wireless communication, cellular networks, energy efficiency, and machine learning.
[46] OpenCL. Open Computing Language. Accessed: 2016. [Online]. Available: https://www.khronos.org/opencl/

[47] T. S. Czajkowski, U. Aydonat, D. Denisenko, J. Freeman, M. Kinsner, D. Neto, J. Wong, P. Yiannacouras, and D. P. Singh, "From openCL to high-performance hardware on FPGAS," in *Proc. 22nd Int. Conf. Field Program. Logic Appl. (FPL)*, Aug. 2012, pp. 531–534.

[48] HSA Foundation. Accessed: 2016. [Online]. Available: www.hsafoundation.com/

[49] J. Cao and M. Yang, "Energy Internet—Towards smart grid 2.0," in *Proc. 4th Int. Conf. Netw. Distrib. Comput.*, Dec. 2013, pp. 105–110.

[50] R. Ramamonjison and V. K. Bhargava, "Energy allocation and cooperation for energy-efficient wireless two-tier networks," *IEEE Trans. Wireless Commun.*, vol. 15, no. 9, pp. 6434–6448, Sep. 2016.

[51] M. Mendil, A. De Domenico, V. Heiries, R. Caire, and N. Hadjsaid, "Battery-aware optimization of green small cells: Sizing and energy management," *IEEE Trans. Green Commun. Netw.*, vol. 2, no. 3, pp. 635–651, Sep. 2018.

[52] E. Altman, *Constrained Markov Decision Processes*, vol. 7. Boca Raton, FL, USA: CRC Press, 1999.

[53] D. P. Bertsekas, *Dynamic Programming and Optimal Control*, 4th ed. Belmont, MA, USA: Athena Scientific, 2005.

[54] D. Marquet, B. Françoise, O. Foucault, S. Vaija, E. Philipot, and C. Campion, "How to transform innovative battery opportunities in field operational solutions for telecom/IT," in *Proc. IEEE Int. Telecommun. Energy Conf. (INTELEC)*, Oct. 2018, pp. 1–8.

[55] *Innovative Energy Storage Technology for Stationary Use—Part 1: Overview of Energy Storage*, document Rec. ITU-T L.1220, Aug. 2017.

[56] *Environmental Engineering (EE); Powering of Equipment in Access Network*, Standard ETSI EN 302 099 V2.1.0, Jun. 2014.

[57] *Progressive Migration of a Telecommunication/Information and Communication Technology Site to 400 VDC Sources and Distribution*, document Rec. ITU-T L.1207, May 2018.

[58] *Sustainable Power Feeding Solutions for 5G Network*, document ITU-T L.5G, 2019.

[59] J. G. Koomey, "Worldwide electricity used in data centers," *Environ. Res. Lett.*, vol. 3, no. 3, 2008, Art. no. 034008.

[60] *Electricity Information*, Int. Energy Agency, Paris, France, 2010.

[61] S. V. Garimella, L.-T. Yeh, and T. Persoons, "Thermal management challenges in telecommunication systems and data centers," *IEEE Trans. Compon., Packag., Manuf. Technol.*, vol. 2, no. 8, pp. 1307–1316, Aug. 2012.

[62] C. Belady, A. Rawson, J. Pfleuger, and T. Cader, "Green grid data center power efficiency metrics: PUE and DCIE," Green Grid, Beaverton, OR, USA, White Paper WP #6, 2008.

[63] D. Redford, "Thermal management supporting networks in transition," in *Proc. Workshop Therm. Manag. Telecommun. Syst. Data Centers*, 2010, pp. 25–26.

[64] *Hitachi Europe*, Press Release, Amsterdam, The Netherlands, Sep. 2010.

[65] S. Greenberg, E. Mills, B. Tschudi, and P. Rumsey, "Myatt. Best practices for data centers: Results from benchmarking 22 datacenters," in *Proc. ACEEE Summer Study Energy Efficiency Buildings*, vol. 16, 2006.

[66] *ABI Research*. (2015). *In-Building Mobile Data Traffic Forecast*. Austin, Tx, USA. [Online]. Available: https://www.abiresearch.com/marketresearch/product/1023888-in-building-mobile-data-trafficforecast/

[67] L. M. Hilty, V. Coroama, M. O. de Ecker, T. F. Ruddy, and E. Müller, "The role of ICT in energy consumption and energy efficiency," Rep. Eur. Commission, DG INFSO, Project ICT ENSURE, Eur. ICT Environ. Sustainability Res., Graz Univ., Graz, Austria, Tech. Rep., 2009, vol. 1, pp. 1–60.

[68] A. P. Jardosh, K. Papagiannaki, E. M. Belding, K. C. Almeroth, G. Iannaccone, and B. Vinnakota, "Green WLANs: On-demand WLAN infrastructures," *Mobile Netw. Appl.*, vol. 14, pp. 798–814, Dec. 2009.

[69] *IEEE Standard for Information Technology—Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications*, IEEE Standard 802.11-2016 (Revision of IEEE Std 802.11-2012), Dec. 2016, pp. 1–3534.

[70] J. Wang, H. Zhu, and N. J. Gomes, "Distributed antenna systems for mobile communications in high speed trains," *IEEE J. Sel. Areas Commun.*, vol. 30, no. 4, pp. 675–683, May 2012.

[71] E. F. J. de Mulder, C. C. D. F. van Ree, and H. R. G. K. Hack, "Geo-environmental aspects of European underground infrastructure," in *Environmental Security of the European Cross-Border Energy Supply Infrastructure*. Dordrecht, The Netherlands: Springer, 2015, pp. 133–153.

[72] OVO Energy. *How Much Energy Do You Use to Heat Your Home?* [Online]. Available: https://www.ovoenergy.com/guides/energy-guides/how-muchheating-energy-do-you-use.html

[73] *Cisco Global Cloud Index: Forecast and Methodology, 2016–2021*, Cisco, San Jose, CA, USA, 2016.

<figure><img src="image_33.png" /></figure>

**MEYSAM MASOUDI** (S'16) received the B.Sc. and M.Sc. degrees in electrical engineering from the Amirkabir University of Technology, Iran, in 2013 and 2016, respectively. He is currently pursuing the Ph.D. degree with the Department of Communication Systems, KTH Royal Institute of Technology, Sweden. His research interests include mobile cloud computing, 5G network architectures, and optimization.

<figure><img src="image_34.png" /></figure>

**MOHAMMAD GALAL KHAFAGY** (S'06–M'15–SM'17) received the B.Sc. degree in electrical engineering from the Faculty of Engineering, Alexandria University, Egypt, in 2009, the M.Sc. degree in wireless communications from the School of Communication and Information Technology, Nile University, Egypt, in 2011, and the Ph.D. degree from the King Abdullah University of Science and Technology (KAUST), Thuwal, Makah, Saudi Arabia, in 2016. From 2016 to 2018,

he was a Postdoctoral Researcher at Qatar University, Doha, Qatar. He joined the Wireless Intelligent Networks Center (WINC), Nile University, Egypt, as a Graduate Research Assistant, in 2009. He is currently a Postdoctoral Fellow with the Division of Communication Systems, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, Stockholm, Sweden. His research interests include multiple-antenna techniques, relay-aided communication, performance analysis and protocol design over fading channels, buffer-aided cooperation, and statistical signal processing for communications.

<figure><img src="image_35.png" /></figure>

**ALBERTO CONTE** (M'10) received the M.S. degree in corporate communications and telecommunication engineering from the Institut Eurécom, France, in 1996, and the Politecnico di Torino, Italy, in 1997. He joined the Software Department of Bell Labs, initially working on embedded systems and VoIP. He then shifted his research interests to wireless/mobile technologies for both enterprises (Wi-Fi) and public/operated cellular systems. He and his team pioneered the

definition of 4G end-to-end mobile networks and delivered solutions for improving their energy efficiency. He is currently the Head of the Cloud-Native Telecom Platforms Automation Department at Nokia Bell Labs, France. He participates in several collaborative EU projects and is an Active Lecturer in LTE technology with Telecom ParisTech, INSA-Lyon and Supelec. He has authored several publications and patents. His current research interests focus on 5G networks with the application of software defined networking (SDN), cloudification, and machine learning.

<figure><img src="image_36.png" /></figure>

**ALI EL-AMINE** (S'16) received the B.E. degree in electrical engineering and the M.S. degree in computer engineering from Lebanese American University (LAU), in 2013 and 2016, respectively. He is currently pursuing the Ph.D. degree in computer and networking with IMT Atlantique (formerly Telecom Bretagne), Rennes, France. His research interests focus on green wireless communication, cellular networks, energy efficiency, and machine learning.
<figure><img src="image_37.png" /></figure>

**BRIAN FRANÇOISE** received the Diploma degree in electrical engineering from the ESME Sudria School, Paris, France, in 2015. He joined Sofrecom (Orange Gardens) as an Energy and Green ITN Consultant, in 2016, where he involved in the Orange Smart Metering project and is currently a Technical Expert with Orange Labs working on R&D projects. His fields of interests are sustainable power feeding solutions for telecommunications networks, energy storage, renewable energy, and energy efficiency.

<figure><img src="image_38.png" /></figure>

**CHAYAN NADJAHI** received the engineering degree from the École Nationale Supérieure d'Electricité et de Mécanique, Nancy, France, in 2014, the master's degree in research from the University of Lorraine, Nancy, in 2014, the Ph.D. degree from the Normandy University, Saint-Lô, France, in 2018, where he is currently a Researcher. His research study focuses on the passive cooling of data centers.

<figure><img src="image_39.png" /></figure>

**FATMA EZZAHRA SALEM** received the engineering degree from the Higher School of Communications of Tunis (Sup'Com), Tunisia, and the master's research degree in wireless communications from CentraleSupelec, France, in 2015. She is currently pursuing the Ph.D. degree with Orange Gardens and Telecom SudParis, France. Her research focuses on green wireless networks, especially the management of advanced sleep modes for 5G networks.

<figure><img src="image_40.png" /></figure>

**WAEL LABIDI** received the engineering degree from École Supérieure des Communications de Tunis (Sup'Com), Tunisia, in 2013. He is currently pursuing the Ph.D. degree with Institut Mines-Telecom, Telecom SudParis, France. He was a Research Engineer with Commissariat de l'Énergie Atomique, France, from 2014 to 2015, specializing on resource allocation in wireless networks. His current work is on optimization of energy planning for mobile operator empowered by smart grid, both technical and economical.

<figure><img src="image_41.png" /></figure>

**ALTUĞ SÜRAL** was born in Ankara, Turkey, in 1990. He received the B.S. and M.S. degrees from the Electrical and Electronics Engineering Department, Bilkent University, where he is currently pursuing the Ph.D. degree. He is also an R&D Engineer with Polaran Ltd. His research interests include error-correcting codes, hardware architecture design and optimization, and VLSI implementation of polar codes.

<figure><img src="image_42.png" /></figure>

**AZEDDINE GATI** received the Ph.D. degree from the University of Pierre and Marie Curie (Paris VI), Paris, France, in 2000. Since 2001, he has been with Orange Gardens, France, where he has been involved in research in the fields of applied electromagnetic for telecommunications systems. He is also an Engineer in telecommunication and signal processing. From 1997 to 2001, he was engaged in studying circuit optimization and electromagnetic (EM) computational methods. In 2001, he has been involved in research on radio planning. In 2007, he leads research projects on sustainable development, including EMF, body area networks, wireless network planning tools, and energy in information, communication technology, and service solutions (ICT). Since 2011, he has been leading a research program dedicated to energy efficiency of wireless and fixed telecom systems and is currently responsible of a department dedicated to wireless networks in Orange Labs. He is currently involved in celtic plus SooGreen Project and the French research project AN spatial modulation. He has coauthored more than 30 papers and 100 international conferences. Dr. Gati is a member of the Union Radio Scientifique International, France. He is a Senior Member of SEE. He has been involved in European Celtic plus OPERA-Net 1 & 2 projects and the Vice-Chair of the wireless group in GreenTouch (www.greentouch.org). He is currently involved in Celtic OPERA-Net 1 & 2 and SooGreen projects and was elected as Vice-Chair of the technical committee leading the GreenTouch project (www.greentouch.org).

<figure><img src="image_43.png" /></figure>

**DOMINIQUE BODÉRÉ** received the engineering degree in IT systems integration from the Conservatoire National des Arts et Métiers, in 1995. He is currently a Soogreen Project Coordinator and an ITN Energy Optimization & Eco-efficiency Project Manager with Orange Labs. After the degree, he joined Orange (previously France Télécom R&D) and the Fixed Access Networks Department of Orange Labs, initially working on access networks development for Orange group. He then shifted to green transformation, data knowledge, traffic & resources modeling division of Orange Labs. His team's current study focuses on ITN Energy Optimization & Eco-efficiency. He participates in several collaborative EU projects.

<figure><img src="image_44.png" /></figure>

**ERDAL ARIKAN** (S'84–M'89–SM'94–F'11) received the B.S. degree in electrical engineering from the California Institute of Technology, Pasadena, CA, USA, in 1981, and the M.S. and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 1982 and 1985, respectively. Since 1987, he has been with the Electrical-Electronics Engineering Department, Bilkent University, Ankara, Turkey, where he is a Professor. He is also the Founder of Polaran Ltd, a company specializing in polar coding products. He was a recipient of the 2010 IEEE Information Theory Society Paper Award, the 2013 IEEE W.R.G. Baker Award, the 2018 IEEE Hamming Medal, and the 2019 Claude E. Shannon Award.
<figure><img src="image_37.png" /></figure>

**BRIAN FRANÇOISE** received the Diploma degree in electrical engineering from the ESME Sudria School, Paris, France, in 2015. He joined Sofrecom (Orange Gardens) as an Energy and Green ITN Consultant, in 2016, where he involved in the Orange Smart Metering project and is currently a Technical Expert with Orange Labs working on R&D projects. His fields of interests are sustainable power feeding solutions for telecommunications networks, energy storage, renewable energy, and energy efficiency.

<figure><img src="image_38.png" /></figure>

**CHAYAN NADJAHI** received the engineering degree from the École Nationale Supérieure d'Electricité et de Mécanique, Nancy, France, in 2014, the master's degree in research from the University of Lorraine, Nancy, in 2014, the Ph.D. degree from the Normandy University, Saint-Lô, France, in 2018, where he is currently a Researcher. His research study focuses on the passive cooling of data centers.

<figure><img src="image_39.png" /></figure>

**FATMA EZZAHRA SALEM** received the engineering degree from the Higher School of Communications of Tunis (Sup'Com), Tunisia, and the master's research degree in wireless communications from CentraleSupelec, France, in 2015. She is currently pursuing the Ph.D. degree with Orange Gardens and Telecom SudParis, France. Her research focuses on green wireless networks, especially the management of advanced sleep modes for 5G networks.

<figure><img src="image_40.png" /></figure>

**WAEL LABIDI** received the engineering degree from École Supérieure des Communications de Tunis (Sup'Com), Tunisia, in 2013. He is currently pursuing the Ph.D. degree with Institut Mines-Telecom, Telecom SudParis, France. He was a Research Engineer with Commissariat de l'Énergie Atomique, France, from 2014 to 2015, specializing on resource allocation in wireless networks. His current work is on optimization of energy planning for mobile operator empowered by smart grid, both technical and economical.

<figure><img src="image_41.png" /></figure>

**ALTUĞ SÜRAL** was born in Ankara, Turkey, in 1990. He received the B.S. and M.S. degrees from the Electrical and Electronics Engineering Department, Bilkent University, where he is currently pursuing the Ph.D. degree. He is also an R&D Engineer with Polaran Ltd. His research interests include error-correcting codes, hardware architecture design and optimization, and VLSI implementation of polar codes.

<figure><img src="image_42.png" /></figure>

**AZEDDINE GATI** received the Ph.D. degree from the University of Pierre and Marie Curie (Paris VI), Paris, France, in 2000. Since 2001, he has been with Orange Gardens, France, where he has been involved in research in the fields of applied electromagnetic for telecommunications systems. He is also an Engineer in telecommunication and signal processing. From 1997 to 2001, he was engaged in studying circuit optimization and electromagnetic (EM) computational methods. In 2001, he has been involved in research on radio planning. In 2007, he leads research projects on sustainable development, including EMF, body area networks, wireless network planning tools, and energy in information, communication technology, and service solutions (ICT). Since 2011, he has been leading a research program dedicated to energy efficiency of wireless and fixed telecom systems and is currently responsible of a department dedicated to wireless networks in Orange Labs. He is currently involved in celtic plus SooGreen Project and the French research project AN spatial modulation. He has coauthored more than 30 papers and 100 international conferences. Dr. Gati is a member of the Union Radio Scientifique International, France. He is a Senior Member of SEE. He has been involved in European Celtic plus OPERA-Net 1 & 2 projects and the Vice-Chair of the wireless group in GreenTouch (www.greentouch.org). He is currently involved in Celtic OPERA-Net 1 & 2 and SooGreen projects and was elected as Vice-Chair of the technical committee leading the GreenTouch project (www.greentouch.org).

<figure><img src="image_43.png" /></figure>

**DOMINIQUE BODÉRÉ** received the engineering degree in IT systems integration from the Conservatoire National des Arts et Métiers, in 1995. He is currently a Soogreen Project Coordinator and an ITN Energy Optimization & Eco-efficiency Project Manager with Orange Labs. After the degree, he joined Orange (previously France Télécom R&D) and the Fixed Access Networks Department of Orange Labs, initially working on access networks development for Orange group. He then shifted to green transformation, data knowledge, traffic & resources modeling division of Orange Labs. His team's current study focuses on ITN Energy Optimization & Eco-efficiency. He participates in several collaborative EU projects.

<figure><img src="image_44.png" /></figure>

**ERDAL ARIKAN** (S'84–M'89–SM'94–F'11) received the B.S. degree in electrical engineering from the California Institute of Technology, Pasadena, CA, USA, in 1981, and the M.S. and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 1982 and 1985, respectively. Since 1987, he has been with the Electrical-Electronics Engineering Department, Bilkent University, Ankara, Turkey, where he is a Professor. He is also the Founder of Polaran Ltd, a company specializing in polar coding products. He was a recipient of the 2010 IEEE Information Theory Society Paper Award, the 2013 IEEE W.R.G. Baker Award, the 2018 IEEE Hamming Medal, and the 2019 Claude E. Shannon Award.
<figure><img src="image_45.png" /></figure>

**FRED AKLAMANU** received the B.Sc. degree in telecommunications engineering from the Ghana Technology University College, Ghana, in 2012, and the M.Sc. degree in computer science for communication networks from Télécom SudParis, France, in 2016. He is currently pursuing the Ph.D. degree in telecom engineering with Nokia Bell Labs and Télécom SudParis, France. He joined Huawei Technologies, Ghana, as a Radio Network Optimization Engineer, from 2012 to 2014. He joined Akka Technologies, France, briefly after his master's degree, as a software developer. His research interests include 5G networks, intent-based networking, software defined networks (SDN), and network function virtualization (NFV).

<figure><img src="image_46.png" /></figure>

**LOUTFI NUAYMI** (S'97–M'01–SM'14) received the Diploma degree in telecommunication engineering from Saint Joseph University, Beirut, Lebanon, in 1993 and the Ph.D. degree in telecommunication from the École Nationale Supérieure des Télécommunications (ENST), now Telecom Paris Tech, Paris, France, in 2001. He is currently a Full Professor at IMT Atlantique (previously Telecom Bretagne and ENST Bretagne), Rennes, France. He has authored WiMAX, published by (Wiley, in 2007) and many journal and conference papers. His fields of interest are radio resource management, energy-efficiency, and machines cellular coverage in wireless networks.

<figure><img src="image_47.png" /></figure>

**HASNA LOUAHLIA-GUALOUS** received the Ph.D. degree in two-phase flow from the University of Franche-Comté and the HDR diploma degree from Belfort Montbéliard University. She is currently a Full Professor in heat and mass transfer with the UNICAEN, Normandie University. She has published more than 100 international journals and conference papers and supervised 20 Ph.D. students. Her research interests include fuel cell, microfluidics, and passive cooling.

<figure><img src="image_48.png" /></figure>

**LUC MEUNIER** received the Diploma degree in industrial engineering from INPG - Grenoble, France. He started working in the telecom sector, in 1998, optimizing the network resources and is currently with at Tele2, Sweden, involved in the Business Sustainable Development and Energy Efficiency. His research study focuses on how to serve telecom customers in the best way with the least impact on the planet.

<figure><img src="image_49.png" /></figure>

**JULIEN LALLET** received the Ph.D. degree in electrical engineering from the University of Rennes, in 2008. He joined the company Alcatel-Lucent, in 2011. He was a Postdoctoral Fellow with the University of Bielefeld, Germany, from 2009 to 2010. He has been a Research Engineer at Nokia Bell-Labs, since 2016. His research interests include efficient processing in the context of cloud computing and hardware acceleration on FPGA. He has published papers in computing architectures and FPGA systems.

<figure><img src="image_50.png" /></figure>

**PAULO SILVA** received the M.Sc. degree in electric and computers engineering from the University of Porto, in 2017. He is currently a Researcher with INESC TEC, Porto, Portugal, in the area of wireless communications. His research interests include IEEE 802.11 and computer networks, being involved in projects related with energy efficiency in Wi-Fi networks, the IoT reference architectures, and the NB-IoT SLAs.

<figure><img src="image_51.png" /></figure>

**KULDEEP PAREEK** received the joint master's degree in internet technology and architecture from UPMC, Paris and Telecom Paritech and the master's degree in communication system design from KTH, Stockholm. He is an experienced RF expert with a demonstrated history of working in the telecommunications industry, involved in many key projects in Sweden which include European Spallation Source and many underground tunnels. He is currently with MIC Nordic AB, Kista, Sweden. He skilled in 3G, LTE, RF planning, universal mobile telecommunications systems (UMTS), and radio frequency (RF).

<figure><img src="image_52.png" /></figure>

**NUNO T. ALMEIDA** received the degree in electrical and telecommunications engineering, and the Ph.D. degree in electrical and computer engineering from the University of Porto. He is currently a Professor with the Electrical and Computer Engineering Department, Faculty of Engineering, University of Porto, and a Senior Researcher at the Institute for Systems and Computer Engineering (INESC TEC), Porto, Portugal. Since the early 90's, he has been involved in several European and national research projects, being his main research interests related to wireless LAN communications, particularly the aspects associated to error control and energy efficiency in dense environment WLANs.
<figure><img src="image_45.png" /></figure>

**FRED AKLAMANU** received the B.Sc. degree in telecommunications engineering from the Ghana Technology University College, Ghana, in 2012, and the M.Sc. degree in computer science for communication networks from Télécom SudParis, France, in 2016. He is currently pursuing the Ph.D. degree in telecom engineering with Nokia Bell Labs and Télécom SudParis, France. He joined Huawei Technologies, Ghana, as a Radio Network Optimization Engineer, from 2012 to 2014.

He joined Akka Technologies, France, briefly after his master's degree, as a software developer. His research interests include 5G networks, intent-based networking, software defined networks (SDN), and network function virtualization (NFV).

<figure><img src="image_46.png" /></figure>

**LOUTFI NUAYMI** (S'97–M'01–SM'14) received the Diploma degree in telecommunication engineering from Saint Joseph University, Beirut, Lebanon, in 1993 and the Ph.D. degree in telecommunication from the École Nationale Supérieure des Télécommunications (ENST), now Telecom Paris Tech, Paris, France, in 2001. He is currently a Full Professor at IMT Atlantique (previously Telecom Bretagne and ENST Bretagne), Rennes, France. He has authored WiMAX, published by

(Wiley, in 2007) and many journal and conference papers. His fields of interest are radio resource management, energy-efficiency, and machines cellular coverage in wireless networks.

<figure><img src="image_47.png" /></figure>

**HASNA LOUAHLIA-GUALOUS** received the Ph.D. degree in two-phase flow from the University of Franche-Comté and the HDR diploma degree from Belfort Montbéliard University. She is currently a Full Professor in heat and mass transfer with the UNICAEN, Normandie University. She has published more than 100 international journals and conference papers and supervised 20 Ph.D. students. Her research interests include fuel cell, microfluidics, and passive cooling.

<figure><img src="image_48.png" /></figure>

**LUC MEUNIER** received the Diploma degree in industrial engineering from INPG - Grenoble, France. He started working in the telecom sector, in 1998, optimizing the network resources and is currently with at Tele2, Sweden, involved in the Business Sustainable Development and Energy Efficiency. His research study focuses on how to serve telecom customers in the best way with the least impact on the planet.

<figure><img src="image_49.png" /></figure>

**JULIEN LALLET** received the Ph.D. degree in electrical engineering from the University of Rennes, in 2008. He joined the company Alcatel-Lucent, in 2011. He was a Postdoctoral Fellow with the University of Bielefeld, Germany, from 2009 to 2010. He has been a Research Engineer at Nokia Bell-Labs, since 2016. His research interests include efficient processing in the context of cloud computing and hardware acceleration on FPGA. He has published papers in computing

architectures and FPGA systems.

<figure><img src="image_50.png" /></figure>

**PAULO SILVA** received the M.Sc. degree in electric and computers engineering from the University of Porto, in 2017. He is currently a Researcher with INESC TEC, Porto, Portugal, in the area of wireless communications. His research interests include IEEE 802.11 and computer networks, being involved in projects related with energy efficiency in Wi-Fi networks, the IoT reference architectures, and the NB-IoT SLAs.

<figure><img src="image_51.png" /></figure>

**KULDEEP PAREEK** received the joint master's degree in internet technology and architecture from UPMC, Paris and Telecom Paritech and the master's degree in communication system design from KTH, Stockholm. He is an experienced RF expert with a demonstrated history of working in the telecommunications industry, involved in many key projects in Sweden which include European Spallation Source and many underground tunnels. He is currently with MIC Nordic AB,

Kista, Sweden. He skilled in 3G, LTE, RF planning, universal mobile telecommunications systems (UMTS), and radio frequency (RF).

<figure><img src="image_52.png" /></figure>

**NUNO T. ALMEIDA** received the degree in electrical and telecommunications engineering, and the Ph.D. degree in electrical and computer engineering from the University of Porto. He is currently a Professor with the Electrical and Computer Engineering Department, Faculty of Engineering, University of Porto, and a Senior Researcher at the Institute for Systems and Computer Engineering (INESC TEC), Porto, Portugal. Since the early 90's, he has been involved in

several European and national research projects, being his main research interests related to wireless LAN communications, particularly the aspects associated to error control and energy efficiency in dense environment WLANs.
<figure><img src="image_53.png" /></figure>

**TIJANI CHAHED** received the B.S. and M.S. degrees in electrical and electronics engineering from Bilkent University, Turkey, and the Ph.D. and Habilitation a Diriger des Recherches (HDR) degrees in computer science from the University of Versailles and the University of Paris 6, France, respectively. He is currently a Professor with the Networks and Services Department, Telecom SudParis, France. His research interests include resource allocation and quality of service, notably in wireless networks.

<figure><img src="image_54.png" /></figure>

**TORD SJÖLUND** received the master's degree in computer science from LuTH and has prolonged experience with the wireless telecommunications industry. Since 2005, he has been with IBS (In-Building Solutions), and has together with colleagues and partners developed the In-Building Wireless business with a focus on energy-saving and a reduced impact on carbon dioxide in these systems. He is currently with MIC Nordic AB, Kista, Sweden.

<figure><img src="image_55.png" /></figure>

**CICEK CAVDAR** received the Ph.D. degree in computer science from the University of California at Davis and Istanbul Technical University, Turkey, in 2009. She is currently an Assistant Professor with the Division of Communication Systems, KTH Royal Institute of Technology. She has been leading the Intelligent Network Systems Research Group with the Radio Systems Laboratory focusing on design and planning of intelligent network architectures, direct air to ground communications and Internet of Things. After her Ph.D., she was an Assistant Professor with the Computer Engineering Department, Istanbul Technical University. She served as the Chair of the Green Communication Systems and Networks Symposium in ICC 2017, Paris. She has been chairing several workshops on the green mobile broadband technologies and Green 5G Mobile Networks last few years co-located with IEEE ICC and GLOBECOM. At Wireless@KTH research center, she has been leading EU EIT Digital projects such as 5GrEEn: Towards Green 5G Mobile Networks and ICARO-EU: Seamless Direct Air to Ground Communications in Europe. She is serving as the Leader of Swedish cluster for the EU Celtic-Plus project SooGREEN—Service Oriented Optimization of Green Mobile Networks and AI4Green —Artificial Intelligence for Green Mobile Networks.

...
<figure><img src="image_53.png" /></figure>

**TIJANI CHAHED** received the B.S. and M.S. degrees in electrical and electronics engineering from Bilkent University, Turkey, and the Ph.D. and Habilitation a Diriger des Recherches (HDR) degrees in computer science from the University of Versailles and the University of Paris 6, France, respectively. He is currently a Professor with the Networks and Services Department, Telecom SudParis, France. His research interests include resource allocation and quality of service, notably in wireless networks.

<figure><img src="image_54.png" /></figure>

**TORD SJÖLUND** received the master's degree in computer science from LuTH and has prolonged experience with the wireless telecommunications industry. Since 2005, he has been with IBS (In-Building Solutions), and has together with colleagues and partners developed the In-Building Wireless business with a focus on energy-saving and a reduced impact on carbon dioxide in these systems. He is currently with MIC Nordic AB, Kista, Sweden.

<figure><img src="image_55.png" /></figure>

**CICEK CAVDAR** received the Ph.D. degree in computer science from the University of California at Davis and Istanbul Technical University, Turkey, in 2009. She is currently an Assistant Professor with the Division of Communication Systems, KTH Royal Institute of Technology. She has been leading the Intelligent Network Systems Research Group with the Radio Systems Laboratory focusing on design and planning of intelligent network architectures, direct air to ground communications and Internet of Things. After her Ph.D., she was an Assistant Professor with the Computer Engineering Department, Istanbul Technical University. She served as the Chair of the Green Communication Systems and Networks Symposium in ICC 2017, Paris. She has been chairing several workshops on the green mobile broadband technologies and Green 5G Mobile Networks last few years co-located with IEEE ICC and GLOBECOM. At Wireless@KTH research center, she has been leading EU EIT Digital projects such as 5GrEEn: Towards Green 5G Mobile Networks and ICARO-EU: Seamless Direct Air to Ground Communications in Europe. She is serving as the Leader of Swedish cluster for the EU Celtic-Plus project SooGREEN—Service Oriented Optimization of Green Mobile Networks and AI4Green —Artificial Intelligence for Green Mobile Networks.

...