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A Distributed Correlative Power Control Scheme for Mobile Ad hoc Networksusing Prediction Filters

Bassel Alawieh , Chadi AssiDepartment of Electrical and Computer Engineering

Concordia UniversityMontreal, Quebec

Email:b alawi,[email protected]

Wessam AjibComputer Science Department

Universite de Quebec a MontrealMontreal, Quebec

Email:[email protected]

Abstract

Transmission power control (TPC) in a Mobile Adhoc network (MANET) environment reduces the total en-ergy consumed in packet delivery and/or enhances networkthroughput by increasing the channel’s spatial reuse. In thispaper, a distributed correlative power control scheme usingprediction filters (Kalman or extended Kalman) is proposed.The prediction filter is used to estimate the forthcoming in-terference. Both the transmitter and receiver in MANETenvironment make use of predicted interference to assigncorrelative power values to their associated ensued pack-ets to guarantee the success of the IEEE 802.11 four-wayhandshaking communication (RTS/CTS/DATA/ACK). Sim-ulation results for different topologies are used to demon-strate the significant throughput and energy gains that canbe obtained by the proposed power control scheme.

1 Introduction

Significant research has been done to improve the per-formance of IEEE 802.11 medium access control (MAC)protocol[10]. One area of research consists of partiallyincorporating power control protocols [5],[7] into IEEE802.11. Power control protocols can provide better spa-tial reuse and energy efficiency. Although the IEEE 802.11MAC protocol [1] reduces the hidden terminal problem, ithas drawbacks. One of the drawbacks is that all packetsare transmitted at the same power, generally the maximalpossible transmission power, which is unnecessary. In thecase where the network is dense, a low transmission poweris sufficient to maintain network connectivity whereas hightransmission power brings about more interference to othertransmissions, resulting in more retransmissions and higherenergy consumption. The throughput of the network is alsopoor in such a case because high transmission power re-

duces the spatial reuse of the network bandwidth. By re-ducing the transmit power of wireless nodes, it is possible toreduce their power consumption. Since a wireless node of-ten operates on a battery, it is important to preserve energyand potentially extend the lifespan of an ad hoc network.Moreover, lower transmit power leads to shorter range of in-terference. As a result, multiple flows of transmission mayoccur in the vicinity of each other. This increase in spatialreuse could lead to increased capacity of the network.

In this paper, we propose a distributed correlative powercontrol algorithm using prediction filters (Kalman and ex-tended Kalman filter)[3]. A mobile node measures the in-terference around its transmission zone. Based on this mea-sured interference, this node makes use of any of the pre-diction filters to predict the future interference . This pre-dicted interference is encapsulated within the RTS (request-to-send) message initiated towards the destination. Uponreceiving the RTS message, the destination makes use ofthe included predicted interference to assign a power valueto the ensued CTS (clear-to-send) message. Before send-ing the CTS message, the destination node repeats the sameprocedure in measuring the surrounding interference andthen predicting the interference in the future. This informa-tion is sent within the CTS message towards the source nodeand used for assigning a power value to the DATA packet.The same criteria is carried again by the source node andused by the destination node for assigning power value tothe Acknowledgement (ACK) message. This class of powercontrol is referred to as correlative power control.

The organization of the rest of the paper is as follows. Insection II, we discuss the related work and the motivationfor the proposed research. The TPC using prediction filtersis derived in section III. We carry various simulations fordifferent topologies in section IV to demonstrate the signifi-cant throughput and energy gains that can be obtained underthe proposed protocol. Finally, we present our conclusionsand future work in section V.

21st International Conference on Advanced Networking and Applications(AINA'07)0-7695-2846-5/07 $20.00 © 2007

RRTS,A

∞

A

Figure 1. Interference model

2 Related work and motivation

Kalman filter has been recently proposed in the litera-ture [4],[6] in different mobile cellular systems applicationsrelated to power control such as in interference estimationand channel gain prediction. A Kalman filter method forpower control is proposed for broadband, packet-switchedTDMA (Time division multiple access) wireless networksin [6].In this work, a terminal starts sending data packetsvia an TDMA uplink channel to the base station and uponreceiving the first data packet in slot n, the base station mea-sures the channel interference around its area and predictsthe future interference using Kalman filter. Based on thepredicted interference, the base station calculates the re-quired optimum power for receiving the next data packetin slot n+1. This information is relayed to the terminal viaa downlink channel. Performance results revealed that theKalman-filter method for power control enhances the net-work performance.

One of the first modifications of incorporating powercontrol into the IEEE 802.11 distributed coordination func-tion (DCF) for MANET was presented in [2]. In [2], theauthors proposed to perform the RTS/ CTS handshake atthe highest initial power level to avoid packet collisionsfrom the interfering nodes. The proposed protocol allowsthe sender and the receiver to negotiate a lower transmitpower (the minimum required) level for sending the dataframes. In this paper, we refer to this scheme as the BASICscheme. BASIC consumes less energy than 802.11, yet itsuffers from the interfering nodes problem [5].

Signal-to-interference-ratio (SIR) based power controlschemes for MANET either uses the current interferencemeasurement [2],[7] or estimates future interference usingpredefined models [13]. The accuracy of those models is notexact to a great extent. The authors in [13] proposed a classof correlative power control algorithms for single channelMANET. A model has been derived to estimate the inter-

ference power. The Interference model as shown in Figure1 was estimated on an assumption that all potential inter-fering nodes are using an average transmission power Pavg

and average radius of transmission zone Ravg. Active inter-fering nodes located outside the transmission range of nodeA (the circle with radius RRTS) are separated from eachother by a distance of at least Ravg . Moreover, the densityof the simultaneous interferes is upper bounded by a fac-tor of 1/R2

avg; here, the total interference was derived as∫ ∞RRT S,A

2×π×x×Pavg×dxRavg

2×x4 . Three scenarios for finding Pavg

were presented. In the worst case scenario, all active nodesgenerate interference at the maximal power, Pavg = Pmax.In the adaptive scenario,Pavg is estimated from the nodeperformance. That is Pavg increases when one frame islost and decreases when N consecutive frames are receivedsuccessfully. Another adaptive scenario, Pavg is estimatedas a moving average from the network performance, i.ePavg = 0.9×Pavg +0.1×Pt, where Pt is the transmissionpower of any captured packet. The authors reported bet-ter network performance and energy gain can be achievedusing the proposed algorithms.

The advantages of the Kalman filter are its simplicitydue to its recursive structure, robustness over a wide rangeof parameters and conditions, and the fact that it possiblyprovides an optimal estimate with minimum square error.These features and the successful reported application sto-ries in various research fields (such as target tracking anddetection, digital signal processing, digital image process-ing , etc) for Kalman filter have motivated us to apply it forpower control in MANET. To the best of our knowledge, nopower control scheme based on Kalman filter has been pro-posed to enhance the capabilities of the IEEE 802.11 pro-tocol. We report here that a Kalman filter is also useful incontrolling transmission power in MANET. This is what weare going to present in the following section

3 Power control Scheme using prediction fil-ters

Our proposed protocol scheme is based on the followingproperties:

• A transmitter cannot initiate any communication ifit receives a power level larger than a given carrier-sensing threshold denoted by η.

• The channel loss gain between a pair of nodes can bedetermined and is stationary for the duration of thecontrol and ensuing data and control packets.The chan-nel loss gain can be measured as follows:

Gain =Pr

Pt. (1)


Figure 2. The ongoing Kalman filter cycle.The time update projects the current state es-timate ahead in time. The measurement up-date adjusts the projected estimate by an ac-tual measurement at that time

where Pr is the received power from the transmittedpower Pt.

• A receiver is able to receive and decode correctly apacket if and only if the defined signal to interferencenoise ratio (SINR) at the receiver side is larger than apredetermined threshold denoted by ζ; thus we havethe following constraint:

Pr ≥ ζ × Pn (2)

where Pn is sum of the interference power plus thermalnoise power. Substituting (1) into (2), we get

Pt ≥ ζ × Pn

Gain(3)

• The received power is equal or greater than a minimumpower level denoted by κ. Thus, the minimum trans-mission power is:

Pmin =κ

Gain. (4)

• The received power of a frame from a transmitter nodein its transmission zone is higher than or equal to κ andthe received power level of a frame a from transmitternode in its carrier sensing zone is higher than or equalto η. When the received power at the receiver side ishigher than η, we say that receiver can sense the trans-mission from the transmitter.

• Interference power at each time instant can be mea-sured quickly, but probably with errors at each node.The interference power is equal to the difference be-tween the total received power and the power of thedesired signal.

• The IEEE 802.11 MAC is adopted i.e., nodes lying inthe transmission vicinity of the sender or the receiverare not allowed to initiate any transmission for the theduration of the ongoing communication between thesender and the receiver nodes. Only nodes lying out-side the sender or receiver transmission zone are po-tential interfering nodes.

3.1 Interference Power Prediction usingPrediction Filters

The Kalman filter estimates a process by implementingfeedback control form; the filter estimates the process atsome time, then obtains feedback in the form of noisy mea-surement. Thus, Kalman filter prediction equations con-sist of two types: time update equations and measurementupdate equations. The time update equations estimate theprocess a priori value for the next time step by projectingforward in time the current state and error covariance esti-mates. Moreover, the measurement update equations inte-grate the new feedback measurement into the a priori es-timate to obtain an improved posteriori estimate. Indeedthe final prediction algorithm resembles that of a predictor-corrector algorithm [12] for solving numerical problems asshown in Figure 2 where the time update equations are thepredictor equations, while the measurement equations arethe corrector equations.

Let In be the actual interference-plus-noise power indBm received at time event n. In is to be considered theprocess state to be predicted by the Kalman filter. The ther-mal noise power, which depends on the channel bandwidth,is given and fixed. The total interference is simply the ther-mal noise plus the measured interference. The system dy-namics of the interference plus noise power can be modeledin state-space form as:

In = In−1 + Nn (5)

where Nn is the variation of the interference-plus-noisepower as new interfering nodes may start to initiate trans-missions and/or adjust their transmission power in the timeevent n. According to the Kalman filter state-space mode ,Nn is the process noise. Let Xn be the measured interfer-ence plus noise power at time event n. Then,

Xn = In + En (6)

where En is the measurement noise. Equations (1) and(2) are commonly referred to as the state space generationmodel. The time equations of the Kalman filter in this caseare

In+1 = In (7)

Pn+1 = Pn + Qn (8)


where In+1 is the a priori predicted interference at next timeevent. In is the a posteriori estimate of In. Pn+1 and Pn

are a priori and a posteriori estimate of the interference plusnoise error variance at time event n+1 and n respectively.Qn

is the variance of the process noise Nn.The measurementupdate equations are:

Kn =Pn

Pn + Rn

(9)

In = In + Kn × (Xn − In) (10)

Pn = (1 − Kn) × Pn (11)

where In and In are a priori and a posteriori estimate of In,Pn is the a priori estimate of the error variance at time eventn, Kn is the Kalman gain, and Rn is the variance for themeasurement noise En.

In the actual tuning operation of the filter, the measure-ment noise covariance Rn and Qn can be determined asfollows :

Qn =1

M − 1×

M∑

n=1

(XM − Xn)2 (12)

Rn = C × Qn (13)

where Xn is the mean of the last M measured values at timeevent n. The event n is when a node successfully receivesa control or data frame. XM is the last obtained measuredvalue. C is a constant between 0 and 1. Qn is an estimateof the variance of the sum of the process and measurementnoise because measurements Xn include the fluctuation ofboth interference and measurement errors.

A necessary condition for Kalman filter to operate isthat the process noise Nn should have a normal distribu-tion [12].The radio channel model considered in this pa-per includes two ray path loss, antenna gain and shadowing[9]. Shadowing is a log-normal distributed random variablecaused by terrain features. The received signal power at anynode can be formulated as :

Pr = Pt × r−4 × G2 × h2 × 10�/10 (14)

r is the distance between the two nodes and h is the heightof the antenna. G is the omnidirectional antenna gain of thenodes and is considered identical for all nodes. Pr is thereceived power from the transmitting power Pt. Note that� is the shadowing component, which is characterized bya Gaussian random variable with zero mean and standarddeviation of σ dB. This makes Nn in dBm normally distrib-uted which verifies the use of Kalman filter in the predictionof interference in MANET environment.

The convergence properties of the Kalman filter is de-pendent on the values of the variance denoted by (P ) [12].We will show by simulation that P is within limits and has

A

maxPPRTS =

B

)*

,max( min Gain

IPPCTS

ς=

)*

,max( min Gain

IPPDATA

+

= ς

)*

,max( min Gain

IPPACK

++

= ς

Figure 3. Power Control using Kalman Filter

the convergence shape at the end of the performance evalu-ation section.

The Kalman filter algorithm functions as follows: forevent n , the interference measurements are input to (12)and (13) to estimate Qn and Rn. Using these values and thecurrent measurement Xn in (9) to (11), we get Kalman gainKn, and the posteriori estimates for In and Pn, respectively.The a priori estimates for the next time event are given by(8) and (6). Specifically, In+1 in (8) is used as the predictedinterference plus noise power for power control as we aregoing to discuss in the coming section.Note that, Pn+1 isused as an initial value to get the next predicted value.

The extended Kalman filter (EKF) [12] attempts to cor-rect the error induced between the process and measuredvalues. Mainly the extended Kalman filter is used for non-linear systems. The process in evaluation has been modeledas linear system and thus a small enhancement can be addedusing the EKF scheme. This enhancement is incorporatedwith in time equations of the EKF. Thus EKF time updateequation is given by:

In+1 = In + (In − In−1) (15)

3.2 Power assignments for successfulframe reception

Before initiating a transmission, node A measures theinterference at time instance t, the interference plus noise-power are used as input to the Kalman filter or extendedKalman filter to predict the estimated interference plusnoise power I at future time as discussed in previous sec-tion. Without loss of generality, I notation will be usedas the predicted interference plus noise power (in mw) inthe coming formulas and for all nodes. The RTS messagewhich is sent at maximum power carries the interference in-formation I to node B as shown in Figure 3. Upon receivingthe RTS message, node B uses this value I to calculate therequired power of CTS as follows and according to the ba-sic rule stated in (4). The transmission power of the CTS


Table 1. Simulation parameter settingsPn 1

σ 4 dB

ζ 10 dB

Pmax 15 dBm

κ -78 dBm

η -83 dBm

Mobility factor 2 m\secSimulation Time 300 sec

message is given by:

PCTS = max(Pmin, ζ × I

Gain) (16)

Before sending the CTS message, node B measures in-terference around its transmission zone and then predictsthe interference plus noise power I+ for the future in or-der for node A to be able to assign successfully a powervalue for its data packet.This predicted value is sent to Ain the CTS message. When A receives the CTS message, itrepeats the same procedure taken by node B to assign a suit-able power value for DATA frame. The transmission powerof the DATA message is given by:

PDATA = max(Pmin, ζ × I+

Gain) (17)

Node B receiving the DATA frame will assign a powervalue to the ACK frame as follows:

PACK = max(Pmin, ζ × I++

Gain) (18)

where I++ is the predicted interference plus noise power atnode A upon receiving the CTS message and is sent to nodeB in the DATA message.

4 Performance Evaluation

4.1 Simulation Setup

We use Qualnet [11] to evaluate by simulation the perfor-mance of our proposed power control scheme using Kalmanand extended Kalman filters. In those simulations, we con-sider the interference from all the interfering nodes. Wecompare the power control scheme using prediction filterswith IEEE 802.11 , BASIC , and Adaptive(case ii,B)[13].We denote the power control scheme using Kalman filter asAdaptive-K and that of extended Kalman filter as Adaptive-EK. In Adaptive(case ii,B) the RTS frame is sent at a powerlevel such that the RTS , CTS, DATA and ACK messages

1 2 3 4 5 6 70

50

100

150

200

250

300

350

Scenario

End-t

o-e

nd

thro

ughput

(Kb/se

c)

IEEE 802.11BASICAdaptiveAdaptive−KAdaptive−EK

Figure 4. Network End-to-End Throughput

are all transmitted at the same transmission power. More-over, the average transmitting power Pavg of all interfer-ing nodes is estimated from the node performance.That is,Pavg increases when one frame is lost and decreases whenN consecutive frames are received successfully. The chan-nel rate is 11 Mbps and CBR (Constant Bit Rate) and FTP(File Transfer Protocol) are used as traffic generators. Threenetwork topologies are adopted in our simulations. One is10×10 grid network with ten end-to-end CBR (constant bitrate) or TCP (transport control protocol) flows. The flowsstart from the left most node to the right most node along thesame row. The route to the destination node is determinedvia a routing algorithm and in our simulations we used Adhoc on Demand Vector Routing (AODV)[8].The distancebetween each node pair is 100 meters. The other scenario isa 50 nodes uniform random network with ten CBR or TCPflows and all nodes are inside a 1000 × 1000 square of me-ters.The third scenario is a clustered topology. For the clus-tered topology, we consider a 1000 × 1000 m2 area with50 nodes. These 50 nodes are divided into four clusters,and each cluster is placed in a 150 × 150 m2 square. Thenodes distribution inside each cluster is not uniformly dis-tributed. The main purpose behind the clustered topology isto show the weakness of using model-based interference asinterference prediction method. The packet sending rate is400 packets per second for the CBR flows. Other simulationparameters are shown in Table 1.

We are considering the following seven scenarios:

1. Grid network with CBR flows;

2. Grid network with TCP flows;

3. Static random network with CBR flows;

4. Static random network with TCP flows;


0 5 10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

Time [sec]

Err

or

pre

centa

ge

Kalman Filter (Node 28)EKF (Node 28)Model−based (Node 28)Kalman Filter (Node 32)EKF (Node 32)Model−based (Node 32)

Figure 5. Interference error percentage

5. Dynamic random network with CBR flows;

6. Dynamic random network with TCP flows;

7. Static clustered network with CBR flows;

In each scenario, one node may communicate with an-other node directly or by relaying, depending on the trans-mit power. We use three metrics to evaluate 802.11, BASIC,Adaptive(case ii,B) [13], Adaptive-K and Adaptive-EK: 1)Aggregate Throughput which is the sum of the data framescorrectly received by the receivers per time unit; 2) Effec-tive Data Delivered per Joule which is the received effectivedata frames divided by the entire energy consumption; 3)Data Frame Corruption Ratio which is the portion of MAClayer frames corrupted by interfering nodes.

4.2 Results and Analysis

Figure 4 shows the total network end-to-end throughputfor different MAC protocols namely, IEEE 802.11,BASIC,Adaptive and Kalman and EKF. Without loss of generality,prediction filters points to both Kalman and EKF in this sec-tion. Clearly, the throughput of tuned prediction filters arehigher than that of the others. As can be seen in scenar-ios 1 and 2, the IEEE 802.11 suffers from the channel ac-cess problems which make it inefficient in terms of through-put.This is because nodes within the transmission zone ofthe sender or the receiver are refrained from initiating anytransmission for the duration of the ongoing communicationbetween the sender and the receiver nodes. Thus as traf-fic increases, the duration to win the channel decreases;asa result, packets will be dropped because their transmis-sion retry limit threshold has been reached. The traffic loadof 400 packets/sec is considered high and thus the IEEE802.11 starts to show its limitations in sharing the channel

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2

4

6

8

10

12

14

16

18

Scenario

Ave

rage

Err

orPer

cent

age

Model−based

kalman

EKF

Figure 6. Average Interference error percent-age

in the time domain. On the other hand, BASIC suffers fromthe hidden node problem which has a high effect on its finalthroughput.

The Adaptive method uses an interference model to es-timate the interference in the future whereas tuned predic-tion filters predict this interference. Figure 5 shows an in-tuitive comparison for the first 40 sec between these twotechniques for node 28 in scenario 3 and node 32 in sce-nario 7. Node 28 is taken randomly for performance evalu-ation whereas node 32 in scenario 7 is considered a gate-way between two clustered groups. As can be viewed,tuned prediction filter can give better estimate for the in-terference with less error percentage. It is to be noted herethat the error is defined by the measured value minus esti-mated or predicted value. Figure 6 shows all the average er-ror considering all the scenarios for the interference estima-tion using Kalman , extended Kalman filters and the model-based. An interference estimation value that is higher thanthe measured actual interference with an error that is morethan 10% may highly affect the overall performance of thenetwork. This is because nodes with higher interferenceestimation value may increase their transmission power toovercome this high interference, thus the higher the trans-mission power is the more the interference effect on othernodes. As a result this may highly affect the overall networkperformance which is shown in Figure 4. Moreover, nodeswith lower interference estimate value with an error greaterthan or equal to 10% will decrease their packet transmis-sion power. In such a case, there is a probability that thesepackets may be corrupted along the path due to actual inter-ference that nodes were not aware of when they assignedpower values to their consecutive packets. To concludethis section, tuned prediction filters outperform the model


0 50 100 150 200 250 300−120

−115

−110

−105

−100

−95

−90

−85

−80

Time [sec]

Interfe

rence[d

Bm

]

Kalman FilterEKFMeasured

Figure 7. Interference Prediction under vari-able traffic load

based by an average error enhancement of 11.4% and thiswas the reason behind the prediction filters achieving higherthroughput gain.

To test the effectiveness of prediction filters for the pre-diction task, we consider scenario 5 and try to vary the in-terference fluctuation by adding additional flows betweentwo simulation instances. Initially 10 CBR flows were run-ning. We injected an additional 10 CBR flows between 5sec and 200 sec to increase the interference. A snapshotfor the same random node 28 is reported in Figure 7. Ascan be viewed from Figure 7, the measured interferenceincreases between these two time instances. The predic-tion filters were able to follow the measured the value withan excellent accurately estimate. The proposed predictionfilters scheme can predict the interference in future, andbased on this prediction, assign consecutive power values toframes which differentiate this method from others in termsof achieving better throughput gains as can be seen in Figure4. In case of no interference , packets are transmitted witha sufficient minimal power Pmin for correct reception. Thisin turn decreases the interference and accordingly enhancesthe spatial reuse which results in better throughput gain. Aslight improvement in terms of CBR end-to-end through-put for IEEE 802.11 is reported in Figure 4 for scenario 3. The randomness of nodes enhances the IEEE 802.11 per-formance due to the fact that there exists cases where fewernodes may lie in the vicinity of the transmission range ofthe sender. On the other hand, BASIC transmits data pack-ets at low transmission power; there is a big probability fordata reception failure due to hidden node problem and ac-cordingly this aspect decreases network throughput as canbe seen in scenarios 3 to 6.

Figure 8 depicts the ratio of throughput to average energy

1 2 3 4 5 6 70

2

4

6

8

10

12

14

Scenario

Thro

ughput/

Energ

yR

ati

o(K

Bps/

Joule

s)


Figure 8. Ratio of Throughput by average en-ergy consumption per node

consumption per node in Kbps/Joules. Energy consumptionincludes the energy of a successful transmission of packetsand the lost energy in retransmitting a packet in case of col-lisions and the energy of the node while receiving a packetand when it is in idle state. The power saving is attributedto the correct assignment of power values for the powercontrol schemes adopting the prediction filters techniques.Reduction in the mutual interference makes it feasible fornodes to deliver packets efficiently. Nodes with packets fordelivery have always to access the channel in order to sendtheir packets. To win the channel , a node has always tosense the channel to be idle before transmitting its packets.If a node was unable to send its packets for a certain du-ration, it follows a backoff procedure and after the backofftime ends , the node has again to check if the channel is idleor not in order to send its packets. This results in unnec-essary carrier sensing. Our proposed scheme decreases togreat extent this unnecessary carrier sensing.This decreasein the sensing activity has reduced the energy consumptionas well. This can be pointed out in all the scenarios.

Figure 9 shows data frame corruption ratio with all sce-narios. Power control using prediction filters causes lesscorruption than the others. BASIC causes less corruptionthan IEEE 802.11. The reason behind this aspect is that allpackets with IEEE 802.11 are transmitted with maximumpower , thus interference increases,and this results in morepacket corruption. For BASIC, packets are transmitted withminimal power, nevertheless in case of interfering nodes ,packets transmitted with minimal power may be corruptedbefore reaching their destination. The effectiveness of thepower assignment in the power control scheme adoptingprediction filters is intuitively shown to decrease the num-ber of packets dropped. Figure 10 shows the convergence


1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Scenario

Data

Pack

et

Corr

uption

Facto

r


Figure 9. Data Frame Corruption Ratio

0 50 100 150 200 250 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [sec]

Interfe

rence

plu

snois

eestim

ate

error

varia

nce

(P

)

Figure 10. Convergence of Kalman filter

of the error of interference plus noise power estimates fora random node 28 in scenario 5. P is within a value be-tween 0 and 1 and has a convergence shape at the end of thesimulation time. This proves that Kalman filter has oper-ated successfully and fulfills the necessary condition for itsconvergence.

Finally, we considered a mobile environment and mea-sured the network throughput; the figures are not showndue to the lack of space. Our results showed that adap-tive schemes start to show throughput limitations after 16m/sec. For BASIC, average throughput drastically de-creases after 14 m/sec whereas that of IEEE 802.11 after12 m/sec. Adaptive schemes, on the other hand, outper-form the other two because they can better estimate the in-terference from interfering nodes and accordingly transmitframes using the optimal transmit power.

5 Conclusion

In this paper, we have proposed a distributed correlativetransmission power control (TPC) scheme for mobile adhoc networks using prediction filters.Prediction filters areused to predict the interference in future. Both the transmit-ter and receiver in MANET environment make use of thepredicted interference to assign power values to their asso-ciated ensued packets to guarantee the success of the IEEE802.11 (RTS/CTS/DATA/ACK) communication. We havecompared our proposed power control scheme with the stan-dard IEEE 802.11 MAC protocol, BASIC , and an Adaptivepower control scheme proposed in [13]. Through simula-tions we proved that prediction filters are far better thaninterference-model-based in estimating the interference forSIR-based power control schemes. Moreover, significantthroughput and energy gains are achieved under the pro-posed power control scheme.

References

[1] IEEE 802.11 wireless LAN media access control (MAC)and physical layer (PHY) specifications. 1999.

[2] S. Agarwal, S. V. Krishnamurthy, R. H. Katz, and S. K. Dao.”Distributed Power Control in Ad-hoc Wireless Networks”.In Personal Indoor Mobile Radio Conference, October 2001.

[3] R. Brown and P. Hwang. Introduction to Random Signalsand Applied Kalman Filtering. 3rd Edition, John Wiley andSons, New York, 1997.

[4] M. S. Elmusrati. Radio Resource Scheduling and SmartAnetnnas in Cellular CDMA Communication Systems. PhDthesis, Helsinki University of Technology, Department ofAutomation and Systems Technology, Control EngineeringLaboratory, August 2004.

[5] E.-S. Jung and N. H. Vaidya. ”A Power Control Mac Proto-col for Ad Hoc Networks”. In MOBICOM, September 2002.

[6] K. K. Leung. ”Power Control by Interference Prediction forBroadband Wireless Packet Networks”. IEEE Transactionson Wireless Communications, March 2002.

[7] A. Muqattash and M. Krunz. ”A Single-Channel Solutionfor Transmit Power Control in Wireless Ad Hoc Networks”.In MobiHoc, May 2004.

[8] P. Perkins and C. Royer. ”Ad-hoc on Demand Distance Vec-tor Routing”. In MOBICOM, August 1999.

[9] T. Rappaport. Wireless communications: Principles andPractice, New york: IEEE Press and Prentice Hall. 1996.

[10] V. S. R. S. Kumar and J. Deng. ”Medium Access ControlProtocols for Ad-hoc Wireless Networks: A Survey”. Else-vier Ad-Hoc Networks Journal, May 2006.

[11] Q. Simulator. www.scalablenetworks.com.[12] G. Welch and G. Bishop. An Introduction to the Kalman

Filter. Technical report, Department of Computer ScienceUniversity of North Carolina at Chapel Hill, August 2006.

[13] J. Zhang and B. Bensaou. ”Core-Pc: A Class of Correla-tive Power Control Algorithms for Single Channel Mobilead hoc Networks”. IEEE Transactions on Wireless Commu-nications, May 2006.


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