On the Effect of Radio Channel Propagation Models - Oulu

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lows or exceeds the FSL curve. This kind of behavior can be also extracted from route breakage figure (Figure 3). This phenomenon can be explained quite easily, if we recall the power attenuation behavior. The attenuation under FPL model is so strong that it effectively behaves nearly like a CP model, when the network traffic load is very low, since the interference power far away from the transmitter is almost negligible. However, as the network traffic is increased, the total interference power in the network is also increased, and as a consequence, it makes the CP approximation to fail with increasing traffic. Important metric from the application QoS perspective is the average end-to-end delay, which is presented in Figure 5. Typical network behavior is seen: the delay stays constant at low traffic loads, but increases heavily when the network becomes near to the limits of it's capability to handle traffic. Pushing even more traffic causes buffers to fill up, which finally leads to buffer overflows (the delay rise settles). We note that the used propagation model does not seem to have great impact to average delay, at least not at the low traffic load. At heavy traffic, there are differences between the points, where the network starts to become unstable. CP-HP gives the lowest delay and FPL follows it at low traffic load. But, as in the case of packet loss (and route breakages), the delay of FPL starts to approach FSL as the traffic load is increased. >c O >. 0,0 10 I.. .., 0,01 Offered data traffic Figure 5. Average data packet delay (BC-MAC). B. IEEE 802.11 and Comparison to BC-MAC At the very high traffic loads, the performance differences between different cases can be clearly seen in throughput (Figure 6). In this figure, the FSL and CP-HP curves of the 802.11 and BC-MAC are shown. An interesting observation can be made when considering 802.11 curves. On the contrary to BC-MAC results, 802.11 FSL gives better performance than 802.11 CP-HP! This is quite unexpected, but can be explained: The very nature of this result originates most likely form the well-known hidden-terminal problem. CSMA is based on power detection and a single transmission can be just detected at a distance of 250 m. 10 6 of 7 However, when there are several transmissions simultaneously at the different portions of the network, the cumulative power of these transmissions can cause signals to be detected from farther than 250 m. Therefore, this basically alleviates hidden-terminal problem as compared to the CP-HP case, in which the CSMA range is always 250 m, and shows a better throughput is a result. The same phenomenon also exists with BC-MAC, but the effect is only minor, since node-specific spreading codes and processing gain handle the situation. 0.7 0.6 0.5 D Ideal random channel access --802.11 (FSL) --802.11 (CP-HP) 0.4 -R _sc' =3 . BC-MAC (FSL) s 0.3 BC-MAC (CP-HP) 0.2 0.1 O 1- 0.001 0.01 0.1 1 Offered data traffic Figure 6. Average throughput (BC-MAC vs. 802.11). Offered data traffic 1 .vrL nF+nn uu T 0o0 )Q1 ,.. 0001 00 . _.. 1 -1 1 .OE-01 t , 1.0E-02 -~eou 1.0E-03 1 .OE-04 ...- -.,.-802.11(FSL) -- - 80211(CPHP) . 1BCMAC 8021 (FSL) BC-MAC (CP-HP) 1.OE-05 Figure 7. Average packet loss ratio (BC-MAC vs. 802.11). This interesting phenomenon affects only at the high traffic load as seen from the packet loss behavior (Figure 7). 802.11 CP-HP clearly dominates the performance over the 802.11 FSL case at low traffic load. But, as the traffic load is increased, there will be more and more simultaneous transmissions at the different parts of the network, and hence, the phenomenon is enhanced. From about the traffic load of 0.08 forward, the performance under FSL is better than under CP-HP. As a consequence, commonly used CP-HP model clearly fails, since it gives too optimistic results at the low traffic load and too pessimistic at the high traffic load. 10
Another important observation is that BC-MAC outperforms 802.11 regardless of the propagation model. In the maximum throughput, there is about two to three-fold difference in favor to BC-MAC. Of course, if one makes a comparison, e.g., BC-MAC under FSL and 802.11 under CP-HP, the comparison could imply that 802.11 performs better at the low traffic load (Figure 7). As obvious, such a comparison is not valid and the comparison between methods should be always made in a way that all the other variables are kept unchanged (the propagation model in this case). SUMMARY & CONCLUSIONS In this paper we studied the effect of propagation modeling to the ad hoc network performance (BC-MAC and 802.11). As real propagation models, we had free space loss and forest propagation loss models, and as a point of comparison, we had high and low power cut propagation models. CP-HP is idealistic in a sense that due to high power, only a strong collision can cause a loss at the physical layer. CP-LP is much more vulnerable, since near to the radio range, the signal is barely receivable. Already a small interference power can cause a long link to fail in all but CP- HP model. However, both CP models fail to model MAI, and thus, give typically far too optimistic view of the performance. CP-HP, which is in a way very close to the models that are often used in ad hoc network studies, gives totally incorrect results. For example, the packet loss ratio (with BC-MAC) under real propagation models is of the order of 10-3, but under CP-HP it is practically zero at low traffic load. An interesting phenomenon was also discovered with 802.11: CP-HP at low traffic load gave too optimistic results, but at high load the effect was reversed. Also FSL and FPL give quite different view of the performance, moreover, these differences are not fixed, but rather the behavior is heavily dependent on network conditions (e.g., network traffic load). Hence, this study clearly addresses that careful attention should be put also to lower layer and especially in radio channel modeling even if the focus would be in the higher layer performance (e.g., application layer QoS). This is definitely the case with the absolute performance, but still it can be asked: can the relative performances between different higher layer methods be compared using simplified lower layer models? The answer is probably dependent on the method under interest. This study, nevertheless, confirmed that our BC-MAC outperforms 802.11 regardless of the propagation model. ACKNOWLEDGEMENTS This work was supported by ITEA Easy Wireless project (J. Prokkola) and by the Finnish Defence Forces through 7 of 7 the Finnish Software Radio Programme (T. Braysy, T. Vanninen) REFERENCES [1] I. Stojmenovic, A. Nayak, and J. Kuruvila, "Design Guidelines for Routing Protocols in Ad Hoc and Sensor Networks with a Realistic Physical Layer", IEEE Communications Magazine 2005, vol 43, No. 3, pp. 101 - 106,. [2] L. Qin and T. Kunz, "On-demand routing in MANETs: The Impact of Realistic Physical Layer Model", In Proc. of 2nd Int'l Conf. Ad Hoc Networks & Wireless, 2003, pp. 37 - 48. [3] M. Takai, J. Martin, and R. Bagrodia, "Effects of Wireless Physical Layer Modeling in Mobile Ad Hoc Networks", Proc. ofACM MobiHOC 2001, Long Beach, USA pp. 87 - 94. [4] J.-M. Dricot and P. De Doncker, "High-accuracy physical layer model for wireless network simulations in NS-2", Proc. of Int. Workshop in Wireless Ad-Hoc Networks 2004. [5] J.-M. Dricot, P. De Doncker, E. Zimanyi, and F. Grenez, "Impact of the physical layer on the performance of indoor wireless networks", In Proc. of the Int. Conf. on Software, Telecommunications and Computer Networks, Split,Croatia,2003. [6] J. Prokkola, T. 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