Wireless Ad Hoc and Sensor Networks

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Distributed Power Control of Wireless Cellular and Peer-to-Peer Networks 2257 × 10−13 k v = 0016Channel change—Gii5432100 50Iterations100 150FIGURE 5.31Smoothed channel variation.probability with time wherein the outage probability using the proposedDPC scheme is significantly less (near zero) when compared to all theother schemes (about 85%), i.e., our approach can accommodate morenumber of mobile users rendering high channel utilization or capacity inthe presence of channel variations. This, in turn, implies that the powerconsumed per active user is less compared to other schemes.CASE II: SLOWLY VARYING CHANNEL In this scenario, . and σ = 3.In this case, though the channel changes every ten time-units, the channelvariation illustrated in Figure 5.31 follows the Rayleigh fading and shadowingbehavior. The channel variation is smoothed out using a linear functionbetween the changes. In this case also, the proposed DPC schemerenders a low outage probability (about 30% as observed in Figure 5.32)while consuming less power per active mobile because the proposed DPCscheme maintains the SIR of each link closer to its target compared to others.Other schemes result in about 85% outage probability. The low outageprobability for the proposed DPC scheme is the result of faster convergence,and low SIR error while it consumes satisfactory power per active user.Example 5.4.2: Performance Evaluation with Number of UsersWhen the total number of mobile users in the cellular network varies,we compare how the total power and outage probability vary. In this
226 Wireless Ad Hoc and Sensor Networks90Outage probability (%)8070605040302010OptimalBambosSSCDCSOPCNew05 10 15 20 25Time30 35 40 45 50FIGURE 5.32Outage probability.simulation scenario, the number of mobile users trying to gain admissionincreases by 25 from the previous value, and the correspondingoutage probability is calculated. Figure 5.33 and Figure 5.34 presentthe performance of the DPC in terms of total power consumed andoutage probability, respectively, when the channel changes slowly. Asexpected, the proposed scheme renders significantly low outage probabilitywhile ensuring low power per active mobile user compared toother schemes.Example 5.4.3: Mobile User ScenarioBecause the users are mobile and in the presence of channel uncertainties,Figure 5.35 illustrates the initial location of mobile users, whereasFigure 5.36 presents the final location. Users in the cellular network tryto move in any one of the eight predefined directions chosen at randomat the beginning of the simulation. A user can move a maximum of 0.01km per time unit. Because the time unit is small, the 0.01 km is a considerableamount of distance for any mobile user. Figure 5.37 and Figure 5.38display the total power consumed and the corresponding outage probability.As evident from the result, the proposed DPC renders a loweroutage probability (average of about 30%) compared to others (about 90%)while consuming low power per active mobile.
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Wireless Ad Hocand SensorNetworksPr
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18. Chaos in Automatic Control, edi
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CRC PressTaylor & Francis Group6000
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Table of Contents1 Background on Ne
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3 Congestion Control in ATM Network
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5.4.2 Distributed Power Controller
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7.3 Adaptive and Distributed Fair S
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9.2.2 Overview.....................
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the number of connections in each l
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y Maheswaran Thiagarajan, and tirel
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2 Wireless Ad Hoc and Sensor Networ
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10 Wireless Ad Hoc and Sensor Netwo
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2BackgroundIn this chapter, we prov
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Background 51u(k)x n (k + 1)x n (k)
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Background 53with x( 0)being the in
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Background 55with tr(.) the matrix
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Background 57nGiven a function ft (
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Background 592.3.2 Lyapunov Stabili
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Background 61as well as Jagannathan
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Background 63The subsequent example
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Background 65Evaluating this along
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Background 67Now, select the feedba
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Background 69is SISL if and only if
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Background 71L 1 (x)L(x, t)L 0 (x)0
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Background 73for some R > 0 such th
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Background 75Evaluating the first d
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Background 77Section 2.4Problem 2.4
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4Admission Controller Design for Hi
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Admission Controller Design for Hig
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7Distributed Fair Scheduling in Wir
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Distributed Fair Scheduling in Wire
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8Optimized Energy and Delay-Based R
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Optimized Energy and Delay-Based Ro
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9Predictive Congestion Control for
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Predictive Congestion Control for W
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10Adaptive and Probabilistic Power
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Adaptive and Probabilistic Power Co
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