## 3 Results
### Illustration and Evaluation of Algorithm Performance
Therefore, although user cooperation only reduces the transmission delay of the network, this component of delay is no longer illustrated separately in the simulation but is represented in the image of network delay. Figure 7 shows the impact of changing the number of users in the optimization algorithms. From left to right, the three curves are the objective function curve corresponding to the optimization result, the delay curve and the energy consumption curve. The abscissa of the three figures is the number of users. The ordinates are the value of objective function (O.F. ), delay and energy consumption, respectively. As shown in the optimization problem (14), the objective function consists of terms of both delay and energy. The units of delay and energy are, successively, seconds (s) and Joules (J). Another prerequisite is that all new users will request similar downlink tasks. It is known that with an increase in the number of users, networks gradually become congested. At this time, the optimization performance of the QL algorithm is better than others. The exponential growth curves can be seen in Figure 7a. With an increase in users, the value of the objective function increases more quickly. At the left portion of the curves, resources are still relatively abundant after new users access the network. From Equations (1) and (2), the transmission rate and the number of network resources form a logarithmic relationship; thus, the excess resources cannot significantly improve the transmission rate. At this time, allocating network resources to new users has little impact on network performance. At the right portion of the curves, the network becomes congested gradually; thus, allocating network resources to new users now has a greater impact. This can be understood by analogy with the slope change of a logarithmic function under horizontal flipping. Next, with respect to Figure 7b,c, since the weight of delay optimization in this simulation was 0.6 (very close to 0.5), the curve changes in both figures are close to that in Figure 7a. Since the weight of delay optimization is slightly larger, the trend of the curves in Figure 7b is closer to the overall trend shown in Figure 7a. In Figure 7b, after considering user cooperation, the server can integrate the downlink tasks and reduce the impact of new access users on network delay without significantly raising energy consumption. The delay brought by new users can be mitigated by user cooperation; however, the energy consumption of new users cannot be ignored. Therefore, the curves in Figure 7c will still increase. Figure 7: Optimization performance curve with variable M (N = 5, G = 1): (**a**) curve for value of objective function; (**b**) curve for time consumption; (**c**) curve for energy consumption. Figure 8 shows the impact of changing the number of independent communication resources on optimization. The content of each axis is similar to that in Figure 7. Obviously, with an increase in the number of resources, network performance improved. Meanwhile, the performance of QL was better than others. However, with a further increase in the number of resources, the improvement in network performance gradually slowed down. This is because the relation between transmission rate and the number of communication resources is not linear but logarithmic, which leads to the marginal revenue of delay optimization. In addition, the increase in energy consumption is still accelerating with the increase in resources. Therefore, reasonable planning with respect to the number of communication resources according to the number of users in the network can not only avoid the waste of resources, but also reduce the computation burden of the algorithm. Increasing the number of resources and reducing the number of access users are similar to some extent. Therefore, the trend of the curve in Figure 8a is opposite to that in Figure 7a. In Figure 8b, since the delay optimization weight is still 0.6, the trend of the curves is also similar to the overall trend shown in 8a.