## 5 Obtained Results and Evaluation
### Experiment-Based Handover Performance
Even though the performance of location-aware handover is better than a particular realization of the RSSI-based scheme, it is still not as efficient as the performance of handover scheme (3), the \"preferred\" option (illustrated as the red line in Figure 8a,b). However, delaying a handover may lead to high packet losses and potential disconnections due to small LoS-NLoS region on the border of the mmWave coverage areas. For example, it is observed in Figure 8a that after approximately 30 s the mmWave data are not collected due to a connection loss. Further, the performance of the LHO algorithm can be improved or degraded largely depending on the accuracy of the location awareness. In other words, when the positioning accuracy is high and reliable enough according to the environment and requirement, the LHO in general can achieve higher throughput than the RSSI-based handover. In the experiment, we apply the proposed GBP algorithm to compute only the initial location of the robot, thereafter, DR is applied for the location estimation of the rest of the trajectory. This is done because of the large errors of the measured AoA at the output of the antenna array, as shown in Figure 9a. By comparing Figure 9a,b, it is clear that the AoA measurements acquired by the robot do not reflect the changes of the AoA with a sufficient accuracy. Hence, standalone-GBP cannot provide adequate AoA accuracy for a reliable positioning accuracy and is therefore applied only to calculate the initial robot location. In terms of the reasons for the large errors of measured AoA, we provide the following elaborations. First, the employed mmWave equipment does not allow for continuous and \"smooth\" control over the array directivity. Instead, the used array has several fixed configurations, each corresponding to a particular vertical and horizontal direction of the main lobe. Each configuration covers approximately 10 degrees in the horizontal and vertical planes, which significantly decreases the accuracy of the LRMs, i.e., the AoAs. Second, we do not have access to the beam-searching algorithm, which makes it difficult to evaluate if the utilized configuration corresponds to a direct link or to a reflection from the wall or another object. In Figure 9b, the estimated robot locations along the trajectory are plotted. For the DR-only case, we manually measure the true initial location of the robot, and the subsequent locations are measured by using robot IMU and wheel encoders data, i.e., the DR. In the \"GBP+DR\" case, the GBP (see Algorithm 1) is utilized to calculate the initial location, while the rest of the algorithm operates in the same way as in the DR-only case. The array configuration data received from the mmWave AP allows us to estimate the initial location of the robot with approximately one meter precision, which is sufficient to make adequate handover decisions in our case. However, we did not use the standalone-GBP method to estimate the location due to the precision-related issues as discussed above. It should also be noted that the precision requirements depend on the building layout and the robot dimensions. In summary, our experiments and the corresponding results validate the feasibility of the LHO for mobile communications in a multi-radio environment, the observed performance gap of the considered handover schemes can be exploited for a more efficient handover scheme design enabling a self-organized communication system with autonomous vehicles in mmWave-ready HetNets, where both the link robustness and data throughput can be reliably guaranteed and sufficiently provided.