## 2 State-of-the-Art Overview and Problem Formulation
### State-of-the-Art in the Location-Aware Communications and Handover Schemes
Furthermore, with the objective of allowing the mmWave networks to operate at scale, the authors in [15] shared their thoughts on the scalability challenges in mmWave networks and also presented positioning algorithms (network-based and device-based) for achieving high accuracy of location awareness. Such positioning algorithms can be not only employed to enable location-based services, but also help reduce the handover costs and make optimized handover decisions. In addition to the handover schemes based on location- and mobility-awareness, other contextual information, such as quality of experience (QoE) and radio environment map (REM), has also been employed and exploited to improve the handover performance. Targeting at improved UE's QoE, the authors in [16] have presented a Q-learning based algorithm for vertical handoff in the HetNets. Via continuous interaction with the environment, an optimized handoff strategy is achieved, i.e., the QoE is maximized based on created mappings between QoS metrics and achieved subjective experience on the side of UEs). Meanwhile, in [17], the REM as well as the UE trajectories are considered to predict the quality of network connectivity that is then exploited for handover process. It is seen that the handover performance improves when the time-to-trigger, the UE velocity, and the location error are low. However, the addressed carrier lies in sub-6 GHz band, and REM of mmWave APs was not considered. Further work using REM for handover can be found in [18], where positioning and radio maps have been combined together for intra-frequency handover. Applying two location prediction methods, the handover decision was made by determining the BS that may offer the highest reference signal received power (RSRP) based on the radio map. However, unconditionally switching to the BS with the highest RSRP might result in frequent handovers and a high ping-pong rate [11]. Contrary to the handover that chooses the BS with the highest RSRP, the authors of [37] proposed to perform a handover only if the signal-to-interference-plus-noise ratio (SINR) at the UE side is lower than a certain threshold over the entire time-to-trigger period. Accordingly, a handover is not triggered even if the RSSI from the serving BS is not the highest, thus, reducing the unnecessary handovers. However, the impact produced by the prediction error of the RSSI was not considered therein, which is the case in practical HetNets. In addition to service-based architecture (SBA) and CUPS, both the core network and the radio access network (RAN) of 5G NR support network slicing (NS) [38] as another enhancement compared to evolved packet core (EPC) ([39] Ch. 6). Essentially, NS serves as the key enabler for the deployment of multiple virtual networks operating on a shared physical network/infrastructure, and each virtual network can therefore be configured to support different specific network functions indicated by the SBA. Consequently, a RAN slicing-based handover scheme was proposed to provide better UE QoS during a handover [40]. Based on the envisioned hierarchical control model, a handover was triggered based on the current link quality as well as the network condition. More importantly, the handover decision not only indicated the target BS, but also suggested a target RAN slice that better satisfied the UE QoS. Given a highly virtualized network, such a handover scheme is capable of more flexible resource utilization and allocation. However, the performance of such scheme is limited by the available bandwidth/resources of the corresponding RAN as well as the respective propagation condition. In the case of realistic situation, such as, blockage/NLoS, a vertical handover between cmWave and mmWave RATs remains crucial to ensure the reception/decoding of signals from both control and user planes, thus, is more efficient than NS-based handover. More related works using similar RATs and addressing industrial use cases can be found in [41; 42; 43].