# Energy-Efficient BWP Configuration for Multi-Slice Users
## 2 Related Works  
In the state of the art (SOTA), most works focus on the dynamic allocation of the slice radio resources for users connected to a single slice such as our previous work [6] and the work in [7]. However, these works do not consider the full energy efficiency aspect nor the users' multi-user-slice connectivity, which is the case of our work. In fact, few works such as the work in [8] consider users connected to multiple slices. In the latter, the authors used Deep Reinforcement Learning (DRL) to attribute the radio resources for each service to these users connected to multiple slices. Nevertheless, the radio resource allocation problem is addressed from a PRB allocation perspective, and the energy efficiency aspect is not taken into account. This is different from our work where the radio resources are allocated based on an energy-efficient BWP configuration selection. Other works from SOTA tackle the BWP subject by studying the BWP switching process such as the work in [9], where the impact of BWP switching on the network's performance is assessed. Moreover, BWP switching related to the BWP inactivity timer is addressed in the work in [10], where a new method is proposed to manage the BWP Inactivity Timer by reducing latency and increasing throughput. Also, the authors in [11] propose an energy-efficient joint mechanism that combines BWP and Discontinous Reception (DRX). However, our work relies on a BWP configuration selection to reduce energy consumption instead of using existing methods such as DRX and the BWP Inactivity Timer. In addition, the mentioned works do not consider users connected to multiple slices. For the energy-efficiency aspect, the work in [12] provides an overview of new features in 5G which help reduce power consumption and increase energy efficiency. In addition, some works propose energy-efficient virtual resource allocation methods using Deep Reinforcement Learning such as the work in [13]. Other works propose an energy-efficient routing protocol in 5G such as the work in [14], whereas the authors in [15] introduce an energy-efficient scheme by solving an optimization problem that aims to reduce power consumption. Nevertheless, these works do not tackle the energy-efficient aspect from a BWP radio resource allocation perspective and do not consider the multi-slice users. In fact, the main focus of our work is to optimize the energy efficiency for users connected to multiple slices in order to ensure a balance between QoS satisfaction and energy efficiency, which is rarely considered in the SOTA. In fact, for these users, two options are available. The first is using a single numerology and a single BWP for all services and slices. With this option, energy consumption is reduced since BWP switching is avoided as well as scanning a larger bandwidth. However, it is not optimal to use a single BWP for all services, as each service may require the use of different numerologies for QoS performance satisfaction. For example, the URLLC service typically requires a higher numerology than the eMBB service [16]. The second option would be to use different BWPs and numerologies for each service, which helps to optimize the services' QoS but at the cost of increasing energy consumption and inflicting additional delays due to BWP switching. Hence, a satisfactory compromise is needed between QoS satisfaction and energy efficiency. For this reason, we consider a scheme that flexibly selects one of these two options for each UE depending on the UE characteristics, since resorting to a single choice is detrimental to UE performance. In fact, opting for a single numerology will reduce the energy consumption of the UE relative to BWP scanning and BWP switching. Nonetheless, a single numerology bestows lower data rates on the UE, which increases its sojourn time, leading to higher energy consumption. Conversely, a multi-numerology reduces the UE sojourn time owing to higher QoS satisfaction and data rates but in turn increases energy consumption because of costly BWP scanning and switching.