# Multi-RAT Orchestration Method for Heterogeneous Wireless Networks
## 3 Materials and Methods
Based on premeasurements, the power classes were grouped as listed in Table 3.  
Current network throughput is reported as the maximum data rate based on active network measurement to the orchestrator from the mobile terminal. Power classes were hardcoded in the orchestrator. In the production environment, the estimation of instantaneous UE throughput would be done by RAN-related network element measurements. Equation (2) is proposed for the solution, which defines how the energy efficiency is estimated based on the two factors \(R\) and \(P\). Measurements are done in a constant time frame; loop and stamps are averaged (here t = 10 s, and \(N\) = 3). The goal of constant loop is to eliminate too frequent links toggling, which may bring additional delay for receiving/sending the user data packet. Such an approach allows calculation of the instantaneous _EEMT_ (Energy Efficient Maximum Throughput) values in the orchestrator, which is able to assess in the current radio network conditions. \[\overline{EEMT_{\text{inst.}}}\left[\frac{Mb}{J}\right]=\frac{\sum_{n=1}^{N} \frac{R_{n}}{P_{n}}}{N} \tag{2}\]  
There are many factors that impact UE throughput. At least two can be listed: base station-user terminal distance, interference levels, and current network load (number of users and user activity). We modeled these two scenarios and examined in our testbed to verify how E2E orchestration improved total energy consumption. The impact on total data volume was also evaluated. In scenario A, a good Wi-Fi connection condition exists. It represents a situation where a terminal is in an area with good Wi-Fi coverage (e.g., closer distance to the Wi-Fi access point) or very limited interference in the Wi-Fi band exists, which may correspond, for example, to the situation where the mobile user moves among different locations, from outdoor to indoor. In Scenario B, a good LTE connection is available and much worse conditions are through Wi-Fi networks in many instances. This scenario represents a very congested environment of Wi-Fi networks or Wi-Fi access points that are far away in distance. In this work, for our research, the srsRAN solution was used, which is well proven and tested in the LTE testbed network environment. The core network (EPC) in this case consists of the Mobility Management Entity (MME), Home Subscription Server (HSS), Service Gateway (SGW), and Packet Gateway (PGW) modules. MME here is mainly responsible for establishing a connection with the UE, implementation of procedures related to mobility, and the tunnel (bearer management) between the EU and PGW. HSS is responsible for authenticating users, SGW and PGW, carrying user and signaling plane data and communication with the IP network. The architecture presented was deployed in the laboratory and all measurements were conducted in the anechoic chamber, where eNB and Wi-Fi access points were seated (see Figure 5). Detail setup configuration follows: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Modem** & **Throughput [Mbit/s]** & **Power Consumption** \\ \hline  & \textless{}2 & 0.6 W \\ LTE & 2\textless{}10 & 0.9 W \\  & \(\geq\)10 & 1.2 W \\ Wi-Fi & \(\leq\)1 & 0.3 W \\  & \(>\)1 & 0.6 W \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Power consumption classes according to the measured network throughput. * 4G Core Network + eNB deployed with USRP B210 and srsLTE;
* Wi-Fi access point MikroTik mAP late;
* LTE USB modem Huawei E3372;
* Wi-Fi USB dongle TP-Link TL-WN725N;
* Log-period antenna Kent Electronic LP8565.