## 2 Related Works
### Machine Learning Based Handover Management  
In [6], the authors present a machine-learning-based handover management scheme for LTE to improve the QoE of file download for users. They proposed two QoE metrics, the probability of successful file download and the completed download time of the file, in their research. A two-level neural network scheme was proposed in their paper. In the first level, the authors used the past QoE in terms of the RSRP and RSRQ values measured by the UE and the past QoE as the input and in terms of download complete or non-complete as output. In the second level, the UE measurement report was used as input, and the download completion time was taken as the output when the file was complete. The machine learning architecture used in [6] includes 12 inputs, one hidden layer, two biases and one output. The general representative model is drawn in Figure 3. The main model used was feed forward neural network (FFNN), in which 75% of the data was used as a training dataset and 25% of the data was used as a test dataset. Their proposed handover algorithm then triggered the handover to the target eNB when the file download was expected to download successfully. If there was more than one target, it would switch to the target eNB with the lowest file download time. The authors in [6] proposed two QoE metrics, which were the probability and time to download the files successfully. However, we believe that these QoE metrics do not match the main applications of next-generation Internet. The challenge of next-generation Internet is real-time Internet services, such as video streaming over the Internet, real-time interactive streaming over the Internet, and Internet telephony services. Therefore, the analysis of QoE in this paper will focus on the Skype network real-time streaming services. In this paper, we propose a deep-learning-based MOS-aware mobile network handover mechanism to determine the appropriate handover time for real-time video conferencing services in mobile networks. Figure 3: The general representative model with 12 inputs, 1 hidden layer, 2 biases and 1 output. In [7], the authors proposed a vertical handover prediction scheme based on the signal-to-noise ratio (SNR) for LTE-WLAN heterogeneous networks. The minimum value of SNR allowed for each bandwidth was determined based on the specifications of the IEEE 802.11 standard. From the first scheme, their research required continuous reports of the strength of the nearby network signals scanned by the MS. Similarly, in the second scheme, the strength of the nearby network signals and the data rate of the MS and the available bandwidth of the neighboring sites were continuously reported. To perform the handover prediction, the authors used a threshold and handover zone (HOzone) for the computation. Their proposed prediction scheme used two independent thresholds. The first threshold was defined as the signal strength of the current service station. The second threshold was obtained from the signal strength of the candidate target station measured by the MS. HOzone represented the time interval between the selection of another base station (BS) and access point (AP) as candidate targets, such that the mobile station (MS) would possess enough time to perform the prediction. The proposed vertical handover prediction schemes achieved a prediction success with 99% percentage in the predefined surrounding areas using a random movement manner. However, the signal strength and available bandwidth were used to determine whether the handover was needed. The difference in bandwidth and delay requirements between different network services, however, was ignored. It may not be sufficient to meet the diverse needs of the Internet services if any handover mechanism only provides information of the signal strength and/or available bandwidth. Therefore, it is essential to provide the QoE-aware mechanism as the handover criteria for network services. In [8], the authors presented a trajectory prediction algorithm of UE to plan for BS handover.