## 4 Simulations
For instance, when the number of sets of UE is set to 30, our proposed caching scheme achieves 37.4%, 35%, 26.3%, and 46.3% lower delay than MPC, RC, LFU, and no-D2D schemes, respectively. The performance gain underscores the efficiency of D2D communications in exerting a positive influence on the network performance in terms of minimizing data access delay. In Figure 4, the popularity skewness (\(\gamma\)) is varied, and its effect on the network performance is observed. A large \(\gamma\) leads to less content being responsible for generating most of the data traffic. Our findings indicate that when \(\gamma\) increases, the delay decreases. The results show that beyond \(\gamma\) = 2.5, all the schemes show stable performance. The obtained results demonstrate that the popularity skewness \(\gamma\) imposes a substantial impact on network performance, and it is imperative to accurately determine which content should be cached to reduce network delay. Our proposed scheme exhibits better performance than other baseline schemes, manifesting the efficiency of our proposed scheme in accurately capturing the data popularity profile to improve data caching. Figure 3: Impact of number of users on network delay, when \(\gamma\) =1, \(M\) = 1000, \(c_{n}\) = 20, and \(c_{i}\) = 50. Figure 2: Impact of varying content quantity on network delay, where \(\gamma\) is set to 1. In Figure 5, we analyze the impact of the data popularity profile on the performance of our proposed scheme. We varied the amount of content while varying \(\gamma\) values. The results illustrate that when \(\gamma\) is low, the delay is high because the data popularity is scattered. Consequently, more content is responsible for generating data traffic that cannot be cached. This leads to more data fetching from the core network, imposing a data traffic burden on the backhaul. Conversely, when the \(\gamma\) value rises, there is a profound reduction in the delay as the amount of popular content that can be located in the cache resources shrinks to meet demands. The influence of popularity skewness is evident because in the case of high popularity skewness, the delay is low. This finding underscores the importance of the data popularity profile during the design of a caching mechanism. When the cache capacity is large, a large amount of content can be cached. However, both the SBS and UE are equipped with limited cache spaces, imposing a constraint on the maximum amount of content that can be cached. Consequently, uncached content results in a data traffic burden on the backhaul link. In Figure 6, we observe the impact of increasing the cache capacity of the SBS on the network delay. There is a monotonic decay in the performance across all the approaches. Nevertheless, our proposed caching scheme demonstrates the best performance compared with the benchmark approaches. The results demonstrate that when the cache capacity can accommodate 150 pieces of content, our proposed caching approach shows 25.2%, 23.3%, 11.3%, and 29.5% lower delay compared with the MPC, RC, LFU, and no-D2D approaches, respectively. Therefore, in order to maximize the advantages of the caching mechanism, it is crucial to cache only that content that generates the majority of data requests. Figure 4: Network delay vs. \(\gamma\) under various caching schemes when \(M=400\), \(N=50\), \(c_{n}=20\), and \(c_{i}=50\). Figure 5: Influence of popularity skewness (\(\gamma\)) on network delay under varying amounts of content in the proposed scheme. In Figure 7, we observe the impact of increasing the amount of content on the percentage of backhaul consumption. When the amount of content increases, it is not possible to cache all the content in the limited cache resources. Furthermore, the UE's data demands exhibit diversity, leading to more requests for uncached content. For instance, when there are 300 pieces of content, our proposed caching scheme demonstrates a reduction of 37%, 33%, 22%, and 44.6% in data access via backhaul compared with MPC, RC, LFU, and no-D2D, respectively.