# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 4 Simulation Results
The major metrics used in the evaluation were the average congestion control ratio, PSNR, and average throughput. The metric parameters are the important factors for deciding performance on D2D communications. The proposed algorithm is compared with the other existing schemes: link scheduling algorithm [24], clustering-based algorithm [28], and rate-allocation algorithm [12]. The proposed algorithm was applied with another factor such as FV(\(\mu\)), cache capacity (CC), and flow object request. In the first simulation, we analyzed the performance of the average congestion control ratio, PSNR, and average throughput with changing the sizes of flow blocks when the occupied cache capacity CC was 0.5, 0.4, 0.3, and 0.2, respectively, and the response coefficient \(\delta\) and FV was larger than 0.7, respectively. Figure 5 shows the performance of the average congestion control ratio \(\delta\) and FV when CC was 0.5, 0.4, 0.3, and 0.2. Figure 5 shows the simulation result according to the flow block change; when CC was 0.5 the average congestion control ratio had a better performance. However, the results did not improve when CC was lower than 0.5. These results imply that CC and FV affect traffic congestion, and the simulation suggested that they control and manage flow objects effectively. Figure 6 shows the performance of PSNR when the utilization of CC with \(\delta\) and FV was 0.9, 0.7, 0.6, and 0.5. A larger CC correlated with a lower flow error ratio and performance improvement, when CC was 0.9. The simulation showed the performance metrics such as D2D streaming quality and flow control given in PSNR. Thus, our proposed algorithm gets better with a higher CC rate among the flow objects. Figure 5: Average congestion control ratio with CC and FV. Figure 7 shows the performance of the average throughput when the utilization of CC was 0.9, 0.7, 0.6, and 0.5, respectively. A larger CC resulted in a higher increase in the throughput, and the efficiency of the excellent performance achieved when the utilization of CC is 0.9. In the second simulation, we analyzed the performance of the average throughput with flow rate and PSNR with increasing FV to see the effectiveness of sensing control for flow object size and block size. Figure 8 shows the performance of the average throughput when FV was 0.3, 0.5, 0.7, and 0.9. The average throughput showed the best performance when FV was 0.9. This result was because the proposed mechanism applied the TFRDF structure and D2D streaming algorithm based on FV for flow objects. Figure 6: PSNR with flow blocks. Figure 7: Average throughput with \(\delta\) and \(\mu\). Figure 9 shows the performance of PSNR with distance when FV was 0.3, 0.5, 0.7, and 0.9. The simulation showed that higher FV effectively controls the flow objects with larger size in cell radius under gNB. As shown in Figure 9, the PSNR results were relatively low because the sizes of all flow objects were set to have a FV. It means that PSNR has little correlation with the distance of the cell radius, and as a result, it has been shown that the quality of D2D streaming is maintained stably. In the third simulation, we analyzed the performance of the average congestion control ratio, PSNR, and average throughput with increasing FV. Figure 10 shows the simulation result performance with the flow block size ranging from 200 MB to 1.5 GB when FV was 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. As shown in Figure 10, the proposed algorithm exhibited improvement in average performance compared with the link scheduling, clustering, and rate allocation algorithms. Figure 8: Average throughput with flow rate. Figure 9: PSNR with distance. Figure 11 shows the performance of PSNR when FV was 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. The proposed mechanism exhibited an excellent result when FV was 0.9. This meant that a larger FV correlated with a good performance result. This paper has not taken into account the interference constraints to improve the quality of channel bandwidth on gNB.