## 5 Performance Evaluation  
### Simulation Model  
Simulation campaigns have been conducted in MATLAB, by following the guidelines in [27; 28], in order to validate the capability of the proposed EED2D-SF scheme to perform a network resources allocation that is able to minimize the network energy consumption while also being efficient in terms of delivered data rate to devices. We concentrated our attention on a mMTC scenario (1000 sensor/machine nodes located inside a (1000 m \(\times\) 1000 m) area) where the gNodeB is in charge of delivering to all devices a software update (10 MBytes). The main simulation parameters, listed in Table 2, have been set according to [29]. We have considered that R = 100 RBs are available in the system on a 20 MHz channel bandwidth. Channel conditions for the MTC devices are evaluated in terms of signal-to-interference-plus-noise ratio (SINR) experienced on each sub-carrier when the path loss and fading phenomena affect the signal reception. The effective SINR is mapped onto the CQI level that ensures a block error rate (BLER)  
Figure 2: EED2D-SF flow chart. smaller than 1%. In particular, 15 CQI levels are considered. We set the scheduling frame duration to 10 ms. During this 1s-long session, 100 data frames are transmitted, with 10 TTIs per data frame, and each TTI lasting 1 ms as we set \(\mu=0\) (sub-carrier spacing equal to 15 KHz). According to [30], we set the gNodeB and D2D node transmission power to 46 dBm and 23 dBm, respectively. In addition, the noise spectral density is equal to \(-174\) dBm\(/\)Hz [30]. The propagation model is described by the pathloss formula \(L=128.1+37.6^{\circ}\)log\((d)\)[30], where \(d\) is the distance (expressed in Km) between the gNodeB and the sensor/machine node. Finally, the shadowing standard deviation is set to 10 and 12 dB in cellular and D2D mode, respectively [30]. The power consumption values are set as in [31]. Specifically, the power consumption of device \(i\) in the cellular network is:  
\[P_{c_{i}}=\beta_{cellular}+\alpha_{d}\cdot R_{c_{i}} \tag{5}\]  
where \(\beta_{cellular}\) is the baseline power, i.e., 1288.04 mW [31], \(\alpha_{d}\) is the downlink power consumption, i.e., 51.97 mw/Mbps [31], and \(R_{c_{i}}\) is the downlink data rate (in Mbps) for sensor/machine node \(i\) over the cellular interface, which depends on the allocated RBs and the channel quality experienced by the sensor/machine node. A variable number of MTC devices is uniformly distributed in the gNodeB coverage area. We assume that, every TTI, all MTC devices send a CQI feedback about the channel condition perceived on the link towards the gNodeB. The CQIs are mapped onto an MCS level that ensures a Block Error Rate (BLER) smaller than 1%. The simulations focus on evaluating the EED2D-SF performance when considering the case of file downloading (e.g., software update), one of the most interesting applications in the mMTC usage scenario. In particular, the study considers the delivery of a 10 MB file and evaluates the network performance by considering all the Time Division Duplexing (TDD) frame configurations [29] foreseen by the 5G NR standard (in Table 3). The performance analysis has been conducted in the following two scenarios:  
* **Scenario A**, simulating a dense network of devices, where we analyze the performance of the proposed EED2D-SF with respect to CMS and D2D-SF [13] under different TDD frame configurations. The number of MTC devices and the number of available radio resources (i.e., RBs) are set to 1000 and 100, respectively. * **Scenario B**, where we analyze the performance of the proposed EED2D-SF for all the TDD frame configurations when varying the number of MTC devices from 200 to 1000, while the number of RBs is fixed to 100.