# IBP Based Caching Strategy in D2D
## 4 Performance Evaluation  
In this section, we investigate the performance of proposed strategy and compare it with other caching strategies for D2D:  
* Maximal closeness: BS locates content holder with highest closeness to user [10]. All the content received by device are cached.
* Zipf-based Caching: It is a proactive caching strategy, files are pre-cached into devices according to Zipf distribution [14].
* Most popular caching strategy: It is also a proactive caching strategy, the most popular files are pre-cached into devices according to the predication of content popularity.  
In our simulations, devices are randomly distributed on a surface covered by a BS, the radius of BS is 500 m. The max D2D communication distance ranges from 5 m to 50 m. BS is responsible for collecting users' encounter and interaction history, also locating content holder(s). In our simulation, cellular spectrum is used for both cellular and D2D communications, i.e., underlay inband D2D [30]. Content selections of devices are modeled by IBP. We use cache hit ratio and sum rate [10] to study the performance of four caching schemes. We define cache hit ratio as the rate of requests satisfied by other devices to all the requests sent by devices.  
In our strategy, BS determines whether the content can be cached by device according to contribution degree \(\overline{I_{ik}}\) and caching threshold \(I_{th}\). Firstly, we investigate the influence of caching threshold \(I_{th}\) on cache hit ratio under different Poisson parameter \(\alpha\) and cache size, as shown in Figures 1 and 2. Parameter \(\alpha\) reflects user's preference; a large \(\alpha\) indicates that devices request new content (has not been requested by precious devices) with high probability. Cache hit ratio increases with \(I_{th}\) increasing in the first stage, then decreases to zero. This is because, once threshold \(I_{th}\) exceeds nutrition degrees of all users, none of content can be cached in D2D network.  
In Figures 3 and 4, we investigate the performance of all the strategies with different parameter \(\alpha\). Parameter \(\alpha\) reflects users preference, a large \(\alpha\) means devices request new content with high probability, which is not cached by other devices. In this case, the traffic to BS will increase. As shown in Figure 3, with the increasing of parameter \(\alpha\), caching hit ratio of all strategies decreases. The proposed IBPSC strategies achieve highest cache hit ratio compared to other schemes, and the advantages of IBPSC are more obvious with larger \(\alpha\). With \(\alpha\) increasing, the total number of content requested by users increases, therefore the sum rate increases as shown in Figure 4. The proposed IBPSC strategy achieves best performance thanks to its contribution degree based caching strategy. The content cached by devices will be requested by other devices with high probability in future.  
Figure 1: \(I_{th}\) with different Poisson parameter \(\alpha\).  
With the increasing of cache size, more content can be cached by devices, and more requests can be satisfied through D2D communications. Therefore, the cache hit ratio of all the caching strategies increases with the increasing of cache size as shown in Figure 5. Meanwhile, as more requests are satisfied by devices, the traffic to BS can be reduced. Figure 6 shows that the sum rate of all schemes increases as the cache size is increased. Since the proposed strategy takes probability of establishing  
Figure 4: Sum rate vs. Poisson parameter \(\alpha\).  
Figure 3: Cache hit ratio vs. Poisson parameter \(\alpha\).  
Figure 2: \(I_{th}\) with different cache size.  
Figure 8: Sum rate vs. number of users.  
Figure 7: Cache hit ratio vs. number of users.  
Figure 9: Cache hit ratio vs. maximum D2D communication distance.