## 3 Results
### Illustration and Evaluation of Algorithm Performance
However, QL considering user cooperation can integrate similar tasks, thereby making the best use of network resources and improving transmission rate. Observing the green dotted line, it can be seen that with the improvement in task similarity, considering user cooperation can achieve more obvious improvements in network performance. However, when the similarity exceeds a certain range, due to the logarithmic relationship between transmission rate and bandwidth, the improvement of network performance by user cooperation will fall into a bottleneck. It can be seen  
Figure 9: Optimization performance curve of variable G (M = 3, N = 3): (**a**) curve for value of objective function; (**b**) curve for time consumption; (**c**) curve for energy consumption. from Figure 10b,c that user cooperation can significantly reduce the transmission delay of the network without increasing the energy consumption burden of user devices. In conclusion, with the emergence of more similar tasks, higher performance can be achieved by user cooperation. Figure 11 shows the changes in \(\gamma_{t}\). The meaning of each axis is similar to that above. Obviously, the value of \(\gamma_{t}\) will not significantly affect the overall result of optimization; however, it will affect network delay and user energy consumption considerably. Specifically, when \(\gamma_{t}\) is small, \(\gamma_{e}\) is large. At this time, the objective function is very sensitive to changes in energy consumption; thus, all algorithms will focus more on the optimization of energy consumption. When \(\gamma_{t}\) is large, the result is opposite. In this simulation, the average delay of all actions in the model was less than the average user energy consumption. Therefore, the curve of user energy consumption changes more sharply than the delay. In addition, from Figure 11b,c, it can be seen that when the weight of delay or energy consumption is small, obvious benefits can be obtained by increasing its weight. This is because when the weight is large, although an increase in weight will still increase the corresponding term of the objective function, this increase is relatively small. For example, the delay weight increases from 0.1 to 0.3 and from 0.5 to 0.7. Both increase by 0.2, but the former increases 200% and the latter only 40%. Thus, the change is diluted. At this time, for all algorithms, it is better to distinguish the energy consumption items with great difference than to focus on distinguishing the suboptimal solution and the optimal solution with little difference in delay. The objective of optimization in this paper was the overall optimization of the network rather than the optimization of single performance. Therefore, this is reasonable. To summarize, according to the above figures and discussion, there is no doubt that user cooperation can help make better use of network resources and compress the exploration space. Both functions will result in a better network than other heuristic algorithms and conventional QL. Figure 10: Performance curve of QLs with different task similarities (M = 4, N = 4, G = 1): (**a**) curve for value of objective function; (**b**) curve for time consumption; (**c**) curve for energy consumption.