## 5 Related Work  
The efficiency of an operator's network is then obtained by averaging the energy spent over a wide range of deployment and operational conditions, from busy hours in metropolitan areas, down to rural areas at nighttime with very low traffic demands. Network traffic patterns can be analyzed, and representative traffic models can then be defined for all the use cases, including user densities as well as the anticipated, near-exponential rise in mobile data traffic between 2010 and 2020, and beyond.  
Most of the approaches [18; 19; 20; 21] useful for increasing the energy efficiency of wireless networks can be grouped under four broad categories as follows:  
(i) Resource allocation: This intends to increase the energy efficiency of a wireless communication system via allocating the system radio resources in a way to maximize the  
Figure 7: Energy consumption for different eMBB slices in conjunction with URLLC slicing configurations.  
energy efficiency rather than the throughput. This approach has been shown to provide substantial energy efficiency gains at the price of a moderate throughput reduction [22]. The literature is rich in contributions dealing with the design of resource allocation strategies aimed at the optimization of system energy efficiency, and the common message is that, by accepting a moderate reduction in the data rates that could otherwise be achieved, large energy savings can be attained.  
RESPOND-A's contribution: The tentative mechanism measures the energy impact on different slicing allocations for EMBB and URLLC. In related work, resource allocation is usually addressed in the cloud infrastructure (more specifically, in the VNF domain). In the described case, allocation is performed in the RAN domain.  
(ii) Network planning and deployment: The second technique is to deploy infrastructure nodes in order to maximize the covered area as per the energy consumed, rather than just the covered area. In addition, the use of base station (BS) switch-on/switch-off algorithms and antenna muting techniques to adapt to the traffic conditions, can further reduce energy consumption [23]. The underlying concept is that, since networks have been designed to meet peak-hour traffic, energy can be saved by (partially) switching off BSs when they have no active users, or simply very low traffic; however, as there are different degrees of hibernation available for a BS, attention must be paid in order to avoid unpleasant coverage holes.  
RESPOND-A's contribution: In the related work, the presented experimental results are limited in the simulation domain, whereas in the presented case the tests were performed in a real-world 5G NR infrastructure and validated the energy efficiency of 5G when network slicing is leveraged in an intelligent manner.  
(iii) Energy harvesting and transfer: The third technique is to operate communication systems by harvesting energy from the environment [24]. This applies to both renewable and clean energy sources, such as sun or wind energy, and to the radio signals present over the air. This is of major interest in developing countries lacking a reliable and ubiquitous power grid, but it is also intriguing more broadly, as it allows \"drop and play\" small-cell deployment [25; 26; 27] (if wireless backhaul is available) rather than \"plug and play\".  
RESPOND-A's contribution: This topic is of great interest and certainly falls within the future scope of the presented work; however, in this study, energy harvesting is out of the scope of the experimental tests, as the main goal was to evaluate 5G's energy efficiency.