CHANNEL-ADAPTIVE WIRELESS FAIR SCHEDULING in .NET

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CHANNEL-ADAPTIVE WIRELESS FAIR SCHEDULING
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session 0 (CIFQ) session 1 (CIFQ) session 0 (CAFQ) session 1 (CAFQ)
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E x p e c t e d s e r v i c e a c t ua l s e r v i c e ( M b )
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Time (s)
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Figure 8.1. Variations of D in CIFQ and CAFQ.
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For CIFQ, b of session 0 increases at the rate of 2 Mbps while b of session 1 decreases at the same rate during the period when session 0 does not have an excellent channel state. This is because session 1 occupies the bandwidth to transmit when session 0 cannot transmit because of its channel state. After session 0 has a perfect channel state, it will be compensated and b will decreases at the rate of 1.8 Mbps. At the same time, b of session 1 will increase at the rate of 1.8 Mbps. Using the simple periodic error pattern, it suf ces to show that when the session has a comparatively high probability of not having a perfect channel state, it is possible that its b grows very fast. So session 0 is seriously lagging when session 1 is seriously leading. It is because CIFQ tries to make use of the bandwidth greedily, and will not save session 0 at the expense of wasting bandwidth. The CAFQ algorithm works better because it always allows session 0 to transmit provided the session is not under the worst channel state (i.e., state D). Meanwhile, session 0 is also punished (as governed by h) for transmitting data under an error-prone channel (hence inducing losses and wasting bandwidth). So session 1 will not lead as much as it will do in CIFQ; nor will session 0 lag that much. The only drawback is that session 1 sometimes is affected as its b exceeds 0 occasionally. But the amount of b that exceeds is not large and is soon recovered. This is because we always try to help the nonleading one rst and the leading one will be ignored until it gets lagged. In summary, we nd that
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The long-term fairness of CIFQ may not be maintained when there is a comparatively high probability that a session will have a nonperfect channel state. CAFQ helps the nonleading session more ef ciently.
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NUMERICAL EXAMPLES
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1600 Average delay of all sessions (ms) 1400 1200 1000 800 600 400 200 0 1 2 3 Error mode 4 5
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CS-WFQ CAFQ Max delay of all sessions (s)
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CS-WFQ CAFQ
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Throughput of the system (Mbps)
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(c) Figure 8.2. Comparison between CS-WFQ and CAFQ: (a) average delay; (b) maximum delay; (c) throughput.
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Scenario 2
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In this scenerio, the simulation time is 500 s and we computed the average result over 10 simulation runs. We simulate CS-WFQ and CAFQ under ve kinds of error modes. There are three sessions in the system. Their preallocated service rates are 0.25, 0.25, and 0.5 in CS-WFQ. The virtual compensation session has the rate of 0.2, and the other sessions rates are 0.2, 0.2, and 0.4 in CAFQ. The data source of the sessions are Poisson sources with arrival rates of 0.8, 0.8, and 1.6 Mbps. All the sessions in the system have the same kind of error mode in each simulation run. We calculate the average delay, maximum delay of all the sessions, and the system throughput in CAFQ and CS-WFQ when the error mode changes. The results are shown in Figure 8.2. As can be seen, the average delay and maximum delay increase, and the throughput decreases as the overall channel state worsens in both CS-WFQ and CAFQ. However, the rate of increase (decrease) is slower in CAFQ, and the average delays and the maximum delays are always smaller in that with CAFQ than in that with CS-WFQ. In addition, the throughputs in CAFQ are