can be attributed to that RRAA sets the threshold of each data rate in advance, then calculate MTL and ORI according to the estimation window [9] of each data rate. The determined threshold does not show enough flexility to sudden changes in channel conditions during the multi-hop transmission.
SARA reports better performance than RRAA in both distances. How-ever, the rate-percentage results (Fig. 5.11 and Fig. 5.13) for ”Path loss” and
”Rayleigh” is almost identical. This analysis demonstrates SARA lack the re-sponsiveness to the channel fading. Therefore, such a characteristics results in the performance degradation as observed in Fig.5.10 and Fig. 5.12.
SampleRate and the proposed algorithm demonstrate the robust performance for all channel conditions in both topologies. The capability of chosen the correct rate rate can be easily shown from Fig. 5.11 and Fig. 5.13 in ”Path loss” chan-nel model. This explains why the proposed algorithm outperforms all existing well-known algorithms. From the results of fading channel models, they further demonstrate the proposed algorithm exhibits superior responsiveness compared to other algorithms and can better exploit the short-term channel variations. That is the reason why the proposed mechanism shows the best throughput performance as shown in Fig.5.10 and Fig. 5.12.
5.6 Topology of Mixed Distances
In this experiment, the distances between stations are mixed with 250 meters and 370 meters in order to create a heterogeneous environment. Fig. 5.14 shows the aggregate throughput with different fading models. The best fixed-rate is 11 Mbps and is plotted for a reference. Fig.5.15 shows the percentage
5. SIMULATION RESULTS
ARF AARF CARA RRAA SARA SampleRate Proposed 0
0.5 1 1.5 2
x 106
Rate Adaptation Algorithms
Aggregate Throughput (bps)
Path Loss Ricean Rayleigh
5.5 Mbps (Path Loss)
Figure 5.12: The aggregate throughput of each algorithm in the distance of 370 meters.
5.6 Topology of Mixed Distances
ARF AARF CARA RRAA SARA SampleRate Proposed 0
The Ratio of Data Rate
1 Mbps
Figure 5.13: The ratio of data rates in the distance of 370 meters.
5. SIMULATION RESULTS
ARF AARF CARA RRAA SARA SampleRate Proposed 0
0.5 1 1.5 2
x 106
Rate Adaptation Algorithms
Aggregate Throughput (bps)
Path Loss Ricean data3
11 Mbps (Path Loss)
Figure 5.14: The aggregate throughput in the mixed type scenario.
5.6 Topology of Mixed Distances
ARF AARF CARA RRAA SARA SampleRate Propsed 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Rate Adaptation Algorithms
The Ratio of Data Rate
1 Mbps2 Mbps5.5 Mbps 11 Mbps
Path Loss
Rayleigh
Figure 5.15: The ratio of data rates in in the mixed type scenario.
5. SIMULATION RESULTS
Chapter 6 Conclusions
In this paper, we study the rate adaption problem in IEEE 802.11 wireless net-works and evaluate the performance of rate adaptive algorithms in 802.11-based wireless mesh network environments. The crux of the problem is to determine the state of the communication channels correctly and make the decision promptly.
We propose a novel cross-layer approach to tackle via a machine learning ap-proach. Maximum likelihood estimator is utilized to robustly estimate the chan-nel state. Then the joint correlation between PHY and MAC is exploited in order to evaluate the performances of available MCSs. Our decision strategy is to achieve the maximum spectral efficiency. We evaluate the performances of the proposed approach as well as several existing algorithms through extensive sim-ulations. The scenarios we consider include different topologies, fading channels, mobility, and various contending nodes. Experimental results show the proposed algorithm outperforms exiting algorithms in all scenarios and various wireless mesh network topologies.
6. CONCLUSIONS
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