## 5 Performance Evaluation
### Results Analysis  
We first analyze the results achieved under Scenario A. Figure 3 shows the NEE achieved by the analysed schemes under all frame configurations foreseen by the NR standard. We notice that the traditional CMS approach exhibits really poor performance with respect to the other schemes. Furthermore, CMS reaches the highest NEE value for the frame configuration with the lowest number of DL subframes (i.e., frame configuration No. 0). On the opposite, the most performing solution in terms of NEE, as desired, is our proposed EED2D-SF that presents the highest NEE value for the frame with the highest number of DL subframes (i.e., frame configuration No. 5). We also notice that when the number of UL subframes in the TDD frame is low (frame configurations No. 5 and 2), both EED2D-SF and D2D-SF algorithms converge to the same NEE values.  
The motivation behind these results can be better understood when analyzing the performance in terms of mean throughput reported in Figure 4. Since CMS does not exploit UL subframes, there is an inverse proportionality between mean throughput and NEE. In fact, the higher the throughput, the lower the NEE. The same reasoning does not hold for the other algorithms (D2D-SF and EED2D-SF), since UL subframes are used to convey data towards D2D receivers. By looking at Figure 4 with respect to Figure 3, we appreciate that better results in terms of NEE are at the expense of a slight mean throughput reduction.  
As expected, the higher the throughput, the lower the delivery time. Indeed, Figure 5 shows that frame configuration No. 5 offers the lowest delivery time, and it is classified as the frame configuration which achieves both the highest NEE and mean throughput in the shortest time interval.  
From now on, we focus attention only on the performance of the proposed EED2D-SF scheme. Hence, we analyze the results obtained in scenario B, where we assess how EED2D-SF works under an increasing number of devices (which varies from 200 to 1000). We evaluate the performance with all TDD frame configurations by analyzing NEE (Figure 6) and ADR (Figure 7) metrics.  
Both NEE and ADR exhibit a performance enhancement with the increase in the number of devices interested in the transmitted content. This is due to the cumulative nature of both metrics. More interesting is the substantially different result that can be achieved by considering the various frame configurations. Since the relay devices forward to their D2D receivers the data directly received from the gNodeB, the presence of a large number of DL subframes allows to buffer a high volume of data to be forwarded through D2D links during the UL subframes. Hence, the capacity of D2D links is  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multicolumn{8}{c}{**Subframe Number**} \\ \cline{2-10}  & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline
0 & D & S & U & U & U & D & S & U & U \\
1 & D & S & U & U & D & S & U & U & D \\
2 & D & S & U & D & D & S & U & D & D \\
3 & D & S & U & U & U & D & D & D & D \\
4 & D & S & U & U & D & D & D & D & D \\
5 & D & S & U & D & D & D & D & D & D \\
6 & D & S & U & U & U & D & S & U & U & D \\ \hline \hline \end{tabular}
\end{table}
Table 3: TDD Frame Configurations (U: Uplink; D: Downlink) [29].