# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 7 Performance Evaluation with Realistic Models
### Simulation Results and Discussions
The time constraint represents the maximum allowable delay for a user to pull a content file from a BS. It can be observed in Figure 6 that a decreased \(T_{s}\) leads to a reduced user interest. This indicates the conflicting objectives of maximizing the user interest and minimizing the content access delay, where both objectives are desirable for enhancing user experience. In practice, a proper balance should be sought by setting a proper value for \(T_{s}\). We note that in conventional pull-type protocols, the access delay is generally random and out of control. Therefore, the ability to manage access delay by adjusting \(T_{s}\) is also a major advantage of our protocol compared with conventional pull-type protocols. Figure 5: Performance comparison of different algorithms (file size distribution is subject to \(Lognorm(9.357,1.318)\), \(P_{T}=0.5\) W). Figure 6: The effect of time constraint \(T_{s}\) on the performance (the file size distribution is subject to \(Lognorm(9.357,1.318)\), \(P_{T}=0.5\) W). In Figure 7, we demonstrate the impact of the total power constraint on the interest-outage tradeoff performance. The two optimal algorithms and the two heuristic algorithms adapting minimum rate maximization are simulated. It is observed that the overall user interest increases with increasing power and outage until it researches the theoretical upper bound, which is 4586 in this particular case. Moreover, when the power increases, the performance gaps between optimal and heuristic algorithms reduce. It is interesting to see that at relatively high power values, a fairly good performance can be achieved even when the outage is very small. For example, at \(P_{T}=2\) W, nearly 90 percent of the optimal performance can be achieved at an outage of 0.05. This implies that sufficient power supply (relative to the file size characteristics) can ensure that the system operates in a desirable state with high performance and low outage. Figure 8 illustrates the impact of file size distribution on the system performance. It can be seen that the impact of reducing the average file size is very similar to the impact of increasing the power constraint. When the file size is small, the performance quickly approaches the optimal even at a low outage probability. This observation reinforces our previous remarks that power constraint and file size are two sides of the same coin and should be jointly considered when designing a system. Figure 8: Performance comparison of different algorithms with varying file size distributions (\(P_{T}=2\) W). Figure 7: Performance comparison of different algorithms with varying power constraint \(P_{T}\) (file size distribution is subject to \(Lognorm(9.357,1.318)\)). Finally, Figure 9 compares the system performance when the distribution of individual user's interest in a piece of content across multiple users has different forms. We simulate all four types of distributions reported in the literature. We can see that the normal, U-shaped and Beta distributions yield similar performance. However, the Levy distribution yields a much better performance in terms of the total user interest. This is because the former three distributions are balanced and the interest in a content file tends to spread across many users. On the contrary, the Levy distribution is a heavy-tailed distribution, so that the interest in a content file tends to concentrate with a few users. In this case, higher total user interest can be obtained by satisfying a few users that have very high interest.