# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 3 Node Management Methodology  
The proposed technique selects the eNB and gNB that will serve each user with the highest possible MOS in MR-DC-enabled scenarios. To this end, a set of indicators is used as input of the system. They are detailed as follows:  
* **Service:** The service used by the UE. In this regard, the selection could vary depending on the requirements of the service, i.e., the importance of the throughput, communication reliability, delay, etc. to perceive a good QoE. Thus, the system will use a different configuration for each service. * **Location**: The location of the UE. In this sense, the configuration of the system could also change depending on the characteristics of the area that surrounds the UE. In this study, the network operator is assumed to be aware of the UE position by trilateration [30]. This process computes the intersection between circles created between the UE and different network nodes taking advantage of the received signal strength. * **Reference Signal Received Power (RSRP)**: The RSRP of each candidate eNB and gNB reported by the UE. Only the nodes that overcome a threshold will be proposed as candidates. * **Reference Signal Received Quality (RSRQ)**: The RSRQ of each candidate eNB and gNB reported by the UE. It may not correlate with the RSRP in some cases, such as when both the received power and interference level are high. * **Available Physical Resource Blocks (PRB)**: The number of free PRBs in each candidate eNB and gNB. It may rely on the density of users as well as the average bandwidth assigned to each user, which can also depend on the predominant service. As introduced above, it is important to know the characteristics of the area that surrounds the user before assigning the network nodes. This methodology proposes to divide the scenario into \(M*N\) squares, whose surface is assumed to have the same characteristics. The experiences of historical users of each square are used to forecast the MOS that can be provided by each node. With this objective, the RSRP and RSRQreported, as well as the resource availability of the eNB and the gNB that simultaneously served these past users, are proposed to estimate the MOS. Therefore, as each metric could have a different weight to obtain a good QoE for different squares or services, \(M*N*S\) estimation models are generated from the introduced metrics to estimate the expected MOS for each service in each square, where \(S\) is the number of different services. For instance, the weight of the resource availability could be higher in ultra-dense areas. On the other hand, RSRQ could be more relevant for video users demanding high throughput, while RSRP could be more important for lightweight communications with Internet-of-Things (IoT) sensors. Hence the importance of having different estimation models. Once they are generated and deployed to assign the serving nodes in real time, the network operator will select the corresponding model based on user location and service. It will then take the measurements reported by the UE and the resource availability of each candidate eNB and gNB to assign the nodes that maximize the MOS and use them as inputs for the following algorithm. To decrease the computing cost, only the nodes that provide a RSRP over a threshold will be considered as candidates. Moreover, a lightweight technique is used to generate the estimation models: multiple regression technique [31], which is detailed in Equation (1):  
\[ \begin{bmatrix}QoE_{1}\\ QoE_{2}\\ \vdots\\ QoE_{n}\end{bmatrix} = \begin{bmatrix}1&PRB_{1}&RSRP_{1}&RSRQ_{1}\\ 1&PRB_{2}&RSRP_{2}&RSRQ_{2}\\ \vdots&\vdots&\vdots&\vdots\\ 1&PRB_{n}&RSRP_{n}&RSRQ_{n}\end{bmatrix}\begin{bmatrix}C\\ W_{Load}\\ W_{Power}\\ W_{Quality}\end{bmatrix} + \begin{bmatrix}Error_{1}\\ Error_{2}\\ \vdots\\ Error_{n}\end{bmatrix}  \tag{1}\]  
where \(n\) represents the number of historical samples of the same service in a single square from a dataset collected by testing the 3 GPP solution in the same scenario.