# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 7 Numerical Results
This  
Figure 4: (a) Upload time and (b) goodput for the proposed greedy schedulers in comparison with optimal solution and legacy schedulers for small numbers of STAs. result corresponds to one reported in [28] and is explained by the fact that the network becomes saturated in the case of big numbers of STAs. In the second series of experiments, we investigate the impact of _legacy_ STAs on the system performance. The difference between legacy STAs and the previously considered STAs (_ax_ STAs) is that _legacy_ STAs do not use scheduled access via trigger frames. These STAs only use conventional random access with default parameters and transmit data instead of transmitting BSRs and waiting for TFs. The _legacy_ STAs use the whole channel and the maximum available power for the transmission. In Figure 6, we show the performance of the _ax_ STAs in the system where half of all STAs are _legacy_. The results  
Figure 5: (**a**) Upload time and (**b**) goodput for the proposed schedulers in comparison with the legacy schedulers for high numbers of STAs. show that the presence of _legacy_ STAs degrades the performance of _11 ax_ STAs, which can be solved by a proper configuration of the channel access parameters, as done in [34]. However, the relative gain of the proposed greedy schedulers against legacy schedulers remains of almost the same order as without _legacy_ STAs. In the third series of experiments, we check the efficiency of the CSI Adaptation Algorithm (see Section 6). The results are shown in Figure 7 for different variants of the greedy-PF scheduler. We consider the cases with \(T_{fd}=0\,\mathrm{s}\), i.e., when the APs always have actual information about the channel, and the \(T_{fd}=0.5\,\mathrm{s}\), i.e., when fading information is updated every \(0.5\,\mathrm{s}\). Lines \"tune\" and \"no tune\" indicate whether the CSI Adaptation Algorithm is used or not, respectively. From the results, we can see that the adaptation gives a significant performance advantage both in terms of the average upload time and of  
Figure 6: (**a**) Upload time and (**b**) goodput of the _ax_ STAs if the half of all devices are _legacy_. the goodput. Moreover, the average upload time in using the adaptation is pretty close to the case when the APs know the actual channel information. We also investigate the effect of the power supplement described in Section 6.2. In this series of experiments, we also place three APs with \(R_{AP}=$20\,\mathrm{m}$\) and fix the total number of STAs \(N=$50$\). The dependencies of the upload time and the goodput on the \(P_{sup}\) parameter value for the greedy-PF scheduler for \(R=$20\,\mathrm{m}$\) and \(R=$40\,\mathrm{m}$\) (MR and SRPT have similar results) are shown in Figures 8 and 9. For the \(R=$20\,\mathrm{m}$\) case, power supplement only worsens the performance because of the maximum power difference constraint and because the STAs are located not so far from each other. However, for the \(R=Figure 1.0\) case, increasing the transmit power has a beneficial effect on performance. Such an effect happens due to the interference at the AP from far devices that are not regulated  
Figure 1.1 (**a**) Upload time and (**b**) goodput for the CSI Adaptation Algorithm for FS-PF. by traditional random access. Furthermore, we see that performance improvement for the \"tune\" case is relatively small. It means that the number of transmission errors caused by interference also is reduced by the CSI Adaptation Algorithm. Figure 1.2 (**a**) Upload time and (**b**) goodput for power supplement for \(R=20\,\mathrm{m}\).