# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 5 Performance Evaluation  
In this section, we perform a comparative analysis between SCRE and SORE algorithms proposed in Section 4. First, these proposed algorithms are analyzed with respect to the most important factors, i.e., maximum association limit in case of SCRE and Wi-Fi leftover capacity in case of SORE. Then comparison is performed between SCRE and SORE by varying different network parameters. **Comparison:** To the best of our knowledge, no other work has performed this comparison under these conditions, therefore, it is difficult to compare our results with any prior state of the art. Instead, we compared the two proposed schemes with a baseline case of no offloading and no cooperation. We performed this comparison to observe the increase in gain (in terms of average data rate) that each of these schemes offer with reference to the baseline. We consider a small portion of macro cell with small cells with approximate radii of 70 m. A total of 15 small cells are uniformly distributed. All the presented results are computed with a 95% confidence interval by averaging over \(10^{3}\) different channel conditions. To simplify our computations, we calculate normalized data rates. Table 2 summarizes the values of the parameters used for evaluation. Different values of the network parameters have been used in simulations, as shown in Table 2, to represent variation in network conditions. Figure 3 demonstrates the effect of increase in cooperation (number of small cells a user can associate itself with) on the achievable data rate per user and the consumed power per associated SBS per user. We consider 15 SBSs, 7 RBs and 5 users. We observe that with the increase in the associated SBSs, the achievable data rate per user increases. This increase is significant in the start (from \(M_{j}=1\) to \(M_{j}=2\)) and gradually flattens. This indicates that after \(M_{j}=3\), there is not many resources (to increase achievable data rate) left in the system to be exploited by cooperation in this scenario. For practical scenario, its maximum value can be 3 or 4 as most of the SCs are surrounded by these many neighboring SCs. We also observe that the consumed power of each SBS per user decreases with the increase in association of the users. This decrease is as a result of being served by multiple SBS which distributes the power consumption between different SBSs. Please note that the algorithm is not violating the maximum power constraint of each SBS. Thus, cooperation increases the achievable data rate per user and decreases the power consumed per SBS per user. Figure 4 presents the performance analysis of SORE algorithm for Wi-Fi offloading. Here users are offloaded to Wi-Fi network if they are unable to fulfill their data rate demand by connecting with single cellular BS. We consider 5 SBSs, 5 RBs and 20 users. We analyze the effect of changing the total leftover capacity of each Wi-Fi AP on the achievable data rates and transmission power of cellular network. It is pertinent to note that the improvement in performance reflects the amount of leftover rate of each Wi-Fi AP. If this amount is high enough then the users obtain higher partial data rates when offloaded to Wi-Fi network. We analyze achievable data rates of network at two different  
rate threshold values by increasing leftover capacities of each Wi-Fi AP present in the Wi-Fi network. The results indicate that by increasing the leftover capacity, the cellular UEs obtain better data rates. When the UEs data rate requirement is less, i.e., \(R^{th}\) equals to 50, then they are satisfied by the cellular resources only, as shown by the red dashed line. On the other hand, the consumed cellular power per SBS per user is not affected much with the increase in the Wi-Fi leftover capacity. Hence, the performance of SORE is mainly driven by the Wi-Fi leftover capacity; however, the power consumption per SBS per user is hardly affected.