# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 7 Performance Evaluation with Realistic Models
### Simulation Results and Discussions  
According to our discussions in Sections 3 and 4, the performance of the following seven different algorithms will be evaluated: (1) the optimal algorithm with maximum file size constraint (Opt-Max); (2) the optimal algorithm with average file size constraint (Opt-Ave); (3) the heuristic algorithm with maximum file size constraint and sum rate maximization (Heu-Max-Sum); (4) the heuristic algorithm with maximum file size constraint and minimum rate maximization (Heu-Max-Min); (5) the heuristic algorithm with average file size constraint and sum rate maximization (Heu-Ave-Sum); (6) the heuristic algorithm with average file size constraint and minimum rate maximization (Heu-Ave-Min), and (7) the traditional \"pull-type\" content retrieval algorithm without outage management (traditional). The tradeoff between total user interest and outage probability will be used as the performance evaluation framework. Figure 5 compares the performance of all seven algorithms. The performance of the traditional \"pull-type\" content retrieval algorithm is characterized by a single point (marked by the star sign). This single point represents an extreme case where the system yields the highest user interest at the cost of the largest outage probability. We note that the traditional algorithm does not offer the flexibility to adjust the user interest or outage probability. In contrast, the performance of our algorithm is characterized by a smooth curve, in which user interest can be flexibly traded for outage probability. It is interesting to see that a small reduction in the user interest (e.g., a 25 percent reduction) can greatly reduce the outage probability by almost 90 percent. Such a capability to flexibly manage the outage probability is a major advantage of our algorithm compared with the traditional pull-type algorithm. The flexibility offered by our protocol essentially comes from exploiting the content diversity as a new degree of freedom. Figure 5 also shows the performance comparison of different algorithms. Let us first compare the two optimal algorithms. It is observed that the Opt-Ave algorithm outperforms the Opt-Max algorithm. Moreover, the Opt-Ave algorithm tends to have a lower computational complexity than the Opt-Max algorithm because it solves a strictly linear programming problem. As a result, we conclude that the Opt-Ave algorithm has a better performance. As for the heuristic algorithms, it can be seen that the two algorithms adapting minimum rate maximization yield better performance than the two \begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameter** & **Parameter Value** \\ \hline Number of users \(U\) & 10 \\ \hline Number of contents \(F\) & 500 \\ \hline Number of channels \(K\) & 256 \\ \hline Recommended form length \(N\) & 50 \\ \hline Time slot \(T_{s}\) & 1 s \\ \hline System bandwidth \(B\) & 10 MHz \\ \hline Noise power spectral density \(N_{0}\) & \(-174\) dBm\(/\)Hz [58] \\ \hline Bit error rate BER & \(1\times 10^{-4}\)[37] \\ \hline Macrocell path loss model & \(128.1+37.6\text{log}_{10}d\) (\(d\) in km) [58] \\ \hline Inter-site distance \(d\) & 330 m \\ \hline Channel gain \(\alpha_{uk}^{2}\) & Exponential distribution of parameter 1 \\ \hline File distribution \(L\) & Logarithmic normal distribution with location parameter of 9.357 and scale parameter of 1.318 [47] \\ \hline Interest matrix \(\mathbf{R}\) & Zipf distribution with parameter 1 [52]; Truncated Gaussian distribution between 1 and 5 with a mean of 3 and a variance of 2 [53]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. algorithms adapting sum rate maximization. This suggests that it is beneficial to allocate capacity evenly among users. It is shown that the best heuristic algorithms can achieve 80 percent of the optimal performance in the worst case. The performance loss is acceptable and is traded for much lower computational complexity. Figure 6 investigates the impact of time constraint \(T_{s}\) on the system performance.