# Multi-RAT Orchestration Method for Heterogeneous Wireless Networks
## 3 Materials and Methods  
The motivation of our work coincides with the discussion concerning the Inter-RAT inter-system energy saving solution, which is addressed also by the 3 GPP Release 16 [28; 29] (however, RAT here means a 3 GPP related RAT, not a generic one) and in [30]. Addressing the topic of a multi-RAT heterogeneous wireless network and managing them at the hypernetwork level (orchestration of various radio networks) together with the possibility of its practical implementation in the SDR environment (Software Defined Radio) is part of the current global research trend in the field of wireless networks and allows for obtaining results that are convergent or even beyond the current state of the art in this field. Such an approach is presented in Figure 3, where the mobile network coverage is overlaid by another RAT, which energy efficiency is higher or the instantaneous UE energy consumption is lower. A prototype proposed solution for testing is composed of two different radio networks (RATs): Wi-Fi and LTE, established using software defined radio technology with the orchestrator set on the top of both RATs and continuously monitoring network parameters and making a decision on switching transmission between the two RATs. Basic mobile terminal (MT) power consumption metrics for different RATs are stored in the E2E orchestrator, which allows for traffic steering with the goal to decrease terminal power consumption. Our approach can allow tracing of power-saving and throughput for terminal oriented indicators. This approach is sufficiently flexible for traffic steering among generic RATs, including those not defined directly by 3 GPP. Based on the E2E managing approach, it is still possible to keep flexible data reconnection toward 5G, 6G, and future Wi-Fi (Wi-Fi6 and next), or if other KPI-oriented services need to be maintain. The testbed configuration is depicted in Figure 4. Two different RATs were connected to the public network via eNB and the access point. The throughput KPIs for both were reported to the nonreal time E2E orchestrator, which is delay sensitive and treated as a new network element. Network context DB stores the latest reported data. UE took the role of DUT. Multi-RAT consists of LTE and Wi-Fi networks, which are accessible via an external modem (UE). The network traffic was artificially steered to simulate different data rate values affected by traffic load and congestion. The decreasing data rate was also treated as a simulation of transmission on a cell edge where lower order modulations are used to maintain stable connectivity. Energy efficiency is estimated in the orchestrator, as presented in Equation (1). The metric defined is widely known in the literature, e.g., [31]. \[EE=\frac{R}{P}\left[\frac{b}{J}\right] \tag{1}\]  
Throughput here is denoted as \(R\) (b/s), and current power consumption as \(P\) (W). It is not obvious how to measure power consumption as it is not agreed what exactly should be measured (total device energy consumption, RAN energy usage, or even if estimation should be done based on physical layer operation). In our scenario, power \(P\) is treated as the total power consumption of a device-under-test (i.e., LTE modem, Wi-Fi dongle). Current mobile networks do not support the reporting of current power consumption in a standardized way. Thus, for the testbed configuration, premeasurements were conducted and outcomes were grouped into classes dependent on throughput. According to Equation (1), two factors in our scenario are needed to make a proper decision by the orchestrator:  
(a) Current network throughput (\(R\)) from UE perspective;  
Figure 4: Testbed network architecture. Figure 3: Sample visualization of cellular network coverage (e.g., 4G/LTE, which coverage is overlaid by another RAT, e.g., Wi-Fi). 2. Total UE power consumption (\(P\)) for a specific data rate grouped into classes, called PC.