In this section, we evaluate the performance of the proposed schemes by MATLAB. The simulation setup and performance analysis are presented as follows.

#### 5.2. Simulation Result And Analysis

In this section, we analyze the influence of the proportion of active CHs $\mu $ or the SINR threshold $\Gamma $ on the caching system performance.

Figure 5 shows the effect of the SINR threshold

$\Gamma $ on the successful transmission rate

$\mathbb{P}(SIN{R}_{HCH}\ge \Gamma )$ under different proportions of active CHs

$\mu $ by Host-CH-provide. The larger the value

$\mu $, the more interference accumulates at typical MU, and

$\mathbb{P}(SIN{R}_{HCH}\ge \Gamma )$ decreases. A larger value of

$\Gamma $ also results in a smaller

$\mathbb{P}(SIN{R}_{HCH}\ge \Gamma )$. The impact of

$\Gamma $ on

$\mathbb{P}(SIN{R}_{HCH}\ge \Gamma )$ is nearly linear. Because there is no inference at the typical MU when

$\mu =0$, the received signal of the typical MU can always be successfully demodulated and decoded, and there is no effect of

$\Gamma $ variation on

$\mathbb{P}(SIN{R}_{HCH}\ge \Gamma )$, which remains a high value.

In addition,

$\epsilon $ is the key indicator that decides the value of the successful transmission rate

$\mathbb{P}(SIN{R}_{NCH}\ge \Gamma |f{b}_{i})$ by Neighbor-CH-provide in formula (23) and represents which file blocks can be received successfully. As shown in

Figure 6, the larger the proportion of active CHs

$\mu $, the less signal superposition and the more interference accumulates such that

$\epsilon $ decreases and fewer blocks can be obtained successfully. A larger threshold

$\Gamma $ also produces a smaller

$\epsilon $. Because there is no inference-maker among the neighbor-CHs when

$\mu =0$, there is also no effect of

$\Gamma $ variation on

$\epsilon $, and

$\epsilon $ has a high value, which means that most blocks can be obtained successfully. Because none of the neighbor-CHs can act as the potential transmitter when

$\mu =1$, there is no probability of successful demodulation and decoding of file blocks

$f{b}_{i},{N}_{U}\le i\le {L}_{m}$ and

$\epsilon ={N}_{U}=2$, i.e., the number of file blocks locally cached in the MU.

As shown in

Figure 7, RMPro decreases as the SINR threshold

$\Gamma $ or proportion of active CHs

$\mu $ increases. Regardless of how the parameter

$\Gamma $ or

$\mu $ changes, RMPro always retains a value greater than 0.83. Except for Local-Cache-provide, a larger value of

$\Gamma $ means that successful reception of the requested file blocks is a more challenging process for the last three methods of obtaining contents. A larger value of

$\mu $ means that there is more interference from neighbor-CHs at the typical MU, which results in the hardship of demodulation and decoding.

According to

Figure 7, we should reasonably increase the distribution density of CHs in deployment to ensure that some CHs are idle and

$\mu $ remains at a relatively low number. At the same time, the process of demodulation and decoding must be optimized to ensure a high successful transmission rate.

Figure 8 indicates that the effects of

$\Gamma $ or

$\mu $ on RMPro of PBCP or UNCP are the same as in the proposed scheme, and RMPro of the proposed scheme is always larger than that of the other two, even by a little. When

$\Gamma $ is a smaller number, it is obvious that RMPro of our proposed scheme is far better than that of the others and is especially better than that of UNCP. When

$\Gamma $ is greater than a certain number, the performance of the three schemes more or less converge.

Regardless of the popularity, all contents are always stored evenly in the UNCP strategy and, thus, the retrieval delay of UNCP must be a high value. In the PBCP strategy, the less popular contents are always transmitted from CP via BS, where the delay caused is also immeasurable, especially when the cache capacity of MU or CH are small. The retrieval delay caused by these two strategies is not comparable to the proposed scheme. Therefore, we only consider the effects of $\mu $ or $\Gamma $ on the delay of our proposed scheme.

Figure 9 and

Figure 10 show the impact of the proportion of active CHs

$\mu $ on the content retrieval delays under different SINR thresholds

$\Gamma $ when requesting a file block or a whole file, respectively, indicating that the content retrieval delay increases with parameter

$\mu $. In more detail, when

$\mu $ remains within a small range of values, the delay time does not change substantially with

$\mu $ variation, but the delay time increases exponentially when

$\mu $ varies beyond a certain value. It is noted that parameter

$\Gamma $ has little influence on the content retrieval delay because

$\Gamma $ affects the delay time only by its effect on the successful transmission rate. According to the two figures, we should set

$\mu $ at a relatively low number to control the content retrieval delay, which is the same as our optimization objective of the caching system.