## 4 Performance Evaluation
### Simulation Results
A similar conclusion about average delay and packet loss ratio can also be drawn from the results in Figures 6b,c and 7b,c. Figure 6: Performance of average delay of each algorithm. To indicate the real-time performance of the three algorithms more intuitively, we also plot histograms (Figure 8) to show the discrete distribution of the transmission delay in 10 RAW stations (Figure 8a), 22 RAW stations (Figure 8b) and 40 RAW stations (Figure 8c) scenarios, respectively. In Figure 8a, the end-to-end delay of 95% packets was below 10 ms in BEB, CCCW and CA-CWA, and only 5% packet delay was distributed between 10 and 100 ms. This is because the channel was not crowded in this scenario, and all the three backoff algorithms performed well. In the scenario shown in Figure 8b, the network was slightly congested. The end-to-end delay in BEB was mainly distributed between 100 and 1000 ms, which had the poorest performance among the three algorithms. By contrast, the end-to-end delay of CCCW and CA-CWA was mostly less than 10 ms due to their feature of CW adaption. Compared with BEB and CCCW, CA-CWA had 578% and 23.4% performance boost, respectively, when considering the percentage of delay distributed below 10 ms. In the scenario shown in Figure 8c, the end-to-end delay of the three algorithms was mainly distributed in the interval of more than 1000 ms, due to the heavily congested channel condition. However, compared with the other two algorithms, CA-CWA still had the largest proportion (17.8% for \(<\) 10 ms, and 21.3% for 10-100 ms) when considering the end-to-end delay distribution between 0 and 100 ms. In summary, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11 ah. The second simulation scenario was to verify the real-time performance of the first backoff state (the backoff used in the free contention period). Table 4 shows change of the values of the core parameters in CA-CWA when varying the number of non-RAW stations. In this scenario, the number of RAW stations \(N_{RAW}\) was fixed to 20, and \(N_{NRAW}\) varied from 0 to 40. As shown in Figure 9a,b, we observed that the average delay and packet loss ratio rose rapidly when \(N_{NRAW}\) only equaled 5 and 20, respectively. This is because all stations could compete for channels in the first backoff state, which led to more congestion of the channel. The performance comparison of the three algorithms was similar to  
Figure 7: Performance of packet loss ratio of each algorithm. busyness ratio. Moreover, to eliminate the influence of the interference in real wireless environment on the algorithm, the channel busyness ratio is then modified with an interference discrimination method. To validate the performance of the proposed algorithm, we compared the real-time performance of CA-CWA with the other two algorithms, namely BEB and CCCW, in NS-3 simulator with IEEE 802.11 ah modules. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Moreover, CA-CWA has a lower delay distribution in the congested wireless condition. Thus, compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11 ah. As for the future research work, we plan to introduce a model considering both backoff schemes and RAW mechanism to provide a theoretical analysis of the network real-time performance, as well as to develop a new channel state estimation method based on several novel mechanisms, such as machine/deep learning techniques, to provide more accurate channel information. Conceptualization, Y.C., H.Z. ; validation, Y.C. ; writing-original draft preparation, Y.C. ; writing-review and editing, Y.C., D.Y., H.Z. This paper was partly supported by NSAF under Grant No. U1530118, partly by National High Technology of China (\"863 program\") under Grant No.