## 4 Performance Evaluation
### Simulation Results  
Because the proposed CA-CWA algorithm is mainly intended for industrial soft real-time applications, the simulation environment was adjusted to make it similar to the wireless conditions in industrial scenarios. According to the authors of [11; 27], high interference exists in unstable and harsh industrial environments, which causes high bit error rates (BER = 10\({}^{-2}\)-10\({}^{-6}\)) in industrial wireless \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Basic Parameters**} \\ \hline Reception energy threshold & \(-\)116.0 dbm \\ CCA threshold & \(-\)119.0 dbm \\ Noise figure & 3 db \\ Channel bandwidth & 1 MHz \\ Path loss model & Log-distance \\ Path loss exponent & 3.67 \\ Data rate & 2.4 Mbps \\ Maximal distance between AP and stations & 250 m \\ \(CW_{min}\) & 15 \\ \(CW_{max}\) & 1023 \\ UDP traffic interval & 0.05 s \\ Packet payload size & 100 bytes \\ \hline \multicolumn{2}{c}{**RAW Parameters**} \\ \hline RAW slot format & 0 \\ \(C\) & 100 \\ \(D\) & 12.5 ms \\ Number of group & 1 \\ \hline \multicolumn{2}{c}{**Algorithm Parameters**} \\ \hline \(T_{\rho}\) & 5 ms \\ \(\lambda\) & 0.2 \\ \(\omega\) & 2 \\ \(\pi\) & 0.9 \\ \(\theta_{max}\) & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic parameters in simulation. Figure 5: Smoothing factor effect on average delay. communication. Thus, we adopted a loss model to introduce a packet loss ratio at approximately 2.5%. Besides, CA-CWA was compared with two other backoff algorithms, namely BEB and CCCW [14]. BEB is the default backoff algorithm in IEEE 802.11 protocol, and CCCW is another CW adaption algorithm based on the channel congestion status, which is described in Section 2.3. In the simulation, we mainly focused on the real-time performance improvement that CA-CWA algorithm can bring for IEEE 802.11 ah in industrial scenario. Thus, we chose average delay, packet loss ratio, and the delay distribution, which is the most important metric for the industrial soft real-time systems, to test the network performance. These metrics can reflect the real-time performance of the network very well. To eliminate random drift of the simulation results, each simulation was conducted five times, and the results are the average of the five. To validate the characteristics of the algorithms in IEEE 802.11 ah more precisely, the simulation was conducted in two simulation scenarios for the two backoff states.In the first simulation scenario, the number of non-RAW stations \(N_{NRAW}\) (the stations which not support RAW) was fixed to 10. The number of RAW stations \(N_{RAW}\) varied dynamically to obtain the real-time performance of the algorithms in the second backoff state (the backoff used in the RAW period). Besides, as one of the key parameters in the RAW mechanism, the number of RAW slots \(N_{s}\) was also set to distinct values (\(N_{s}=1\), 2 and 4) to show the influence of different RAW parameters on the network real-time performance. In fact, the influence of \(N_{s}\) on network real-time performance has been discussed in the literature [28; 29]. In simple terms, when \(N_{s}\) increases and the other RAW parameters remain unchanged, the network delay will increase slightly under a lower load, but decrease slightly under a heavier load. The reason is elaborated in in detail below along with the simulation results. Table 3 illustrates the change of the values of the core parameters in CA-CWA when varying the number of RAW stations, and \(N_{s}\) was set to 1. To show the channel state estimation process of the CA-CWA in the simulation, we recorded the smoothed modified channel busyness \(\rho_{avg}^{j}\). In each interval \(T_{\rho}\), \(\rho_{avg}^{j}\) was updated by Equation (9). Here, we use \(\rho_{avg}^{100}\) to show the channel state because \(\rho_{avg}^{j}\) tended to be stable after one hundred updates. Besides, the backoff decrement factor \(\theta_{1}\), which was calculated by \(\rho_{avg}^{100}\), is also listed in the table.