# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Results
#### 8.2.3 UAV @ 120 m  
In this section, we discuss the results when the UAV is flying at a height of 120 m. This is the highest altitude under consideration; therefore, the number of interfering elements is the maximum amongst the considered scenarios. Figure 16 shows SINR values for UAV @ 120 m. When no link adaptation is employed, i.e., when the FPA method is used, the values vary from very 1.34 dB to 5.36 dB. This represents an unstable link as the SINR values vary drastically. For example, at about the 31 s mark, there is a sharp drop to 1.45 dB from 5.05 dB for about 30 s. However, at the 73 s mark, it increases till 5.38 dB sharply in a 6 s duration before falling back to 41.38 ms by the end of 100 s, after which it increases again and does not stay constant. The periods of steady and stable values are lesser as compared to when the UAVs are at a lower height, for example, between 0 and 26 s, and between 67 s and 73 s. As mentioned above, this is primarily because the FPA algorithm cannot take into account the changing interference conditions and, at a higher altitude of 100 m, the increase in interfering elements leads to a change in the number of interfering elements. This also coincides with the latency values, which see delays of up to 67.447 ms, as shown in Figure 17.  
Figure 15: Latency at 100 m.  
Figure 16: SINR vs. time @ 120 m.  
Figure 17: Latency at 120 m.  
Q-learning starts with a lower value of 5.78 dB as compared to its 100 m performance. It maintains a steady performance value of close to 6.8 dB till about 88 s. This is indicative of good performance for the link; however, after about 89 s, it suffers a drastic dip in performance and undergoes a series of peaks and troughs till the end of simulation. When the simulation progresses, the Q-table becomes large and achieving the optimal solution becomes difficult. This is also reflected in the link latency, as shown in Figure 17.  
Finally DQL performs the best with an average, minimum and maximum values of 8.51 dB, 7.72 dB and 8.6354 dB respectively, which is indicative of steady performance with high SINR values. It also shows a really low latency value of 43.15 ms. Even though it shows a 15.58% decrease in SINR values and 26.87% increase in latency values as compared to 100 m, it is still the best best performing algorithm. It implements link adaptation absent from FPA and eliminates the need for Q-tables by the use of neural networks. It is to be noted that the overall performance of the UAV at 120 m is the worst as compared to 50 m and 100 m. The main cause for this is the fact that, due to the higher altitude, the number of interfering elements, i.e., snBs that UAV has LoS with, increases. Figure 18 shows the SINR values for three different speeds of the UAV. It is seen that, with increasing speeds of the UAV, it does not see any drastic changes in SINR values. The average \(\delta\) value falls from about 9.5 dB at 10 m/s to 8.5 dB at 30 m/s. This is a clear indication that the DQL algorithm deals well with high speeds of the UAV. Table 8 summarises the results with different speeds. Figure 19 shows the observed normalised runtime. Compared to FPA and tabular Q-learning, the proposed DQL algorithm has a shorter runtime and performs better in terms of convergence and capacity. As the no. of UAVs h increases, the runtime complexity of FPA and tabular Q-learning increase rapidly, but the runtime of DQL remains relatively low. At h = 4, the DQL algorithm only took 4% of the runtime of the other two algorithms. This behaviour is observed because the increase in h means that the number of interfering elements increases. FPA and tabular Q-learning are not able to accommodate these changing interfering conditions effectively, but the proposed DQL performs better than them. Another way to visualise and evaluate an agent's performance during its training process is the reward plot. Figure 20 shows the reward plot for the proposed DQL method.