VTBM - a Novel Distributed MAC Approach forWireless Networks

Tama's RadVanszkiMobile Communications and Computing LaboratoryBudapest University of Technology and EconomicsMagyar Tudosok krt. 2. H-1117 Budapest, Hungary

Email: radvanszki@hit.bme.hu

Abstract-The recently emerging possibility of ubiquitouswireless connectivity has increased the importance of the effectiveradio bandwidth usage. In the case of distributed multipleaccess MAC algorithms are needed to lower the probability ofcollisions [1]. In our paper we investigated a dynamic p-persistentalgorithm [5]-[7] based CSMA/CA procedure for distributedwireless networks. A novel distributed medium access procedurethat aims to increase the efficiency of the radio bandwidth usageis presented. The proposed VTBM (virtual transmission basedMAC) procedure is based on our analytical results which arepresented in this paper. VTBM uses a virtual transmission mech-anism [8] to get information about the network configuration andestimate the optimum transmission pattern for the active mobilenodes. The performance of our MAC solution is investigatedby simulations. Specifically, the effects of different populationof active terminals on performance are investigated. Simulationresults demonstrate that the proposed algorithm outperforms theexponential backoff [6] algorithm used in 802.11 DCF MAC ([1],[3], [6]) regarding throughput.

I. INTRODUCTION

Studies of distributed wireless networks (e.g. WLANs,MANETs) [1] are a relatively new field gaining more pop-ularity for various new applications. In these networks, theMedium Access Control (MAC) protocols are responsible forcoordinating the access from active nodes. The common goalof the distinct MAC protocols is to let users transmit theirpackets successfully over the channel at the highest possiblerate.

Recently the IEEE 802.11 standard [6] [3] is taken mostlyas a basis of designing wireless networks. The standardimplements the CSMA/CA (Carrier Sense Multiple Access/ Collision Avoidance [1], [3]) as medium access procedurefor the DCF (Distributed Coordination Function) operatingmode of wireless devices'. In this protocol, the nodes keeplistening the wireless channel, and try to transmit if and onlyif it is found to be idle for a predefined time called DIFS(Distributed Inter Frame Space). The communication betweenthe nodes is based on positive acknowledgement. That is,the receiver node must acknowledge (ACK) each successfullyreceived packet to the sender. This receipt indicates to thesender that there was no collision or data-loss in the radio

'The DCF method also uses RTS-CTS mechanism for medium accesscontrol, however this behaviour is not addressed in our paper.

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Saindor ImreMobile Communications and Computing LaboratoryBudapest University of Technology and EconomicsMagyar Tudosok krt. 2. H- 1117 Budapest, Hungary

Email: imre@hit.bme.hu

channel and the packet was successfully transmitted. If theacknowledgement is not sent or it is lost the sender nodetries to resend its packet according to its scheduling algorithm.The 802.11 standard uses the wide-spread exponential backoffalgorithm to schedule the resending process [1], [2].

Another approach is to apply a centralized solution where amaster node controls all channel activity and allows the othernodes the capability to access to the communication medium[9]. This scheme, however, is not addressed our paper.

In our researches we have focused on a novel approachbased on the 802.11 DCF MAC protocol but it uses thedynamic p-persistent method [6] [7] instead of the exponentialbackoff algorithm to resolve access contentions.

The rest of the paper is organized as follows. In Section IIwe examined some important issues: (1) how can the optimaltransmission pattern regarded to the dynamic p-persistent algo-rithm be determined, (2) how can the nodes collect informationof the entire network (3) how can it be detected that the nodesare operating optimal. Section III introduces the novel VTBMalgorithm and presents a detailed description of the protocol.The performance of the protocol is evaluated by simulations.The simulation model, the test scenarios and the results can befound in Section IV. Finally, we conclude our paper in SectionV.

II. ANALYSIS OF DYNAMIC PERSISTENT CSMAA. Optimal transmission probability

First we apply the dynamic p-persistent model to investigatethe slotted ALOHA access scheme [1] to point out theconnection between transmission probability and the activepopulation of the network and it is also shown how importantto have a proper estimation for these parameters.

Let n be the number of users wishing to access the sharedchannel. Each user attempts to access the channel with pprobability . We remark that in this case all nodes use thesame p-value. The channel utilization U is the probability thatexactly one user transmits:

U = np(1 _p)fnl1 (1)

Differentiating U with respect to p and set the result to zero,we get that the maximal value of U is achieved for p = nSubstitute p in (1) we get that:

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0.9 / |-n-=100.8

05 - 1 -, 0 0. 1 1. 2

Reltiv esiainerr-lgrtmc

Fig. 1. Throughput as function of the relative estimation error

Umax (1 - -

ny

Fig. 2. Channel utilization characteristic related to an individual node

(2)

We investigated the impact of approximation error on theutilization of the channel and the results for ten and onehundred nodes are shown in Figure 1.The horizontal axis depicts the logarithm of the relative

error (n-t) and the plotted throughput is normalized bythe theoretical optimum value that could be achieved if theerror was zero. Note, that the two curves are close to eachother leading us to the conclusion that the system reacts toestimation errors of the order of o(l) less sensitively. It can

also be seen that under estimating the population has more

serious effect on the throughput than over estimating it.

B. Channel feedback

In order to get information about the distributed wirelessnetwork, a node can mostly draw conclusions from the stateof the shared channel. We applied 0/1/C channel feedback, inwhich the node can distinguish between an empty slot ('O'), a

successful (single) transmission ('l'), and a collision ('C') -

when at least two transmissions occur at the same time slot.

C. Channel utilization characteristics

The effects on channel utilization of an individual node us-

ing different transmission probabilities were also investigated.The channel utilization characteristic means the achieved

utilization in the channel as function as the transmissionprobability of the specified node when the other nodes use

static transmission probabilities during the examination. Thisfactor is computed the following way:

U(pi) = piI0i + (1 - pj)S_i, (3)

where pi is the p-value of the examined ith node, I-i is theprobability that the other active nodes keep waiting, and S-iis the utilized channel capacity achieved by the nodes expectthe ith one.

It can be seen, that the rise of the utilization curve isindependent of pi:

U'(pi) = 1_i-S-i. (4)

Figure 2 shows that the line of utilization characteristiccurve is opposed to the difference between optimal and appliedprobability. If p-values were lower than optimal, we got thatthe channel was better utilized when the investigated nodeused greater values, and vice versa. At the same time if theother nodes used optimal p-values, the individual node had noinfluence on channel utilization, whereas I-i equals S-j:

n( )(5)

Note, that the overall utilization of the channel is optimalat this time unrelated to the p-value of the individual node:

U(pi) = = Umax. (6)

However, this statement does not perform in the oppositedirection. If we consider the set of nodes using same proba-bilities for transmission then the line of the throughput curvebeing approximately constant indicates that probabilities usingby nodes are closed to the optimal value. This establishmentconstituted the basis of designing the following VTBM pro-cedure.

III. VTBM PROCEDURE

The concept of our VTBM (Virtual Transmission BasedMAC) algorithm builds upon a method called virtual trans-mission [8] that aims to provide more information for thedynamic p-persistent algorithm to determine the channel uti-lization regarding different p-values. A virtual transmissiondiffers from a "real" transmission of the dynamic p-persistentalgorithm that the packet is not actually transmitted physically.The only purpose to perform such virtual transmissions is togain additional information on the state of the medium: thethroughput of these transmissions can be measured and basedon this information the utilization characteristic of the channelcan be estimated.

In order to calculate the utilization regarded virtual or phys-ical transmission probability the node should know whetherthe last packet transfer was successful or not. In case of a realtransmission the transfer is considered successful when the

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node gets the ACK receipt from the communicating party. Inthe virtual case, however, the situation is slightly complicated.The node wouldn't get any physical acknowledge as the packetis not actually transmitted therefore it can only draw conclu-sions from the state of the channel. After a virtual transmissionis performed the node starts listening the medium for a SIFS(Short Inter Frame Space) time. If the channel became busyduring this time it means that the virtual transmission wouldcause collision if it accessed the channel. Otherwise when themedium remains idle for SIFS long the terminal considers thevirtual transmission successful as if it were a real one the othernodes could sense it and would not interfere.

At the same time a method implemented in VTBM proce-dure is proposed to estimate the optimal transmission proba-bility based on the utilization characteristic. The estimation iscarried out in each terminal by executing the following task:

1) virtual transmissions using different probabilities areperformed simultaneously

2) the performance of these transmissions is evaluatedperiodically

3) the real transmission probability for the next period iscomputed

4) a new set of probabilities is determined for the virtualtransmissions.

The transmission probability used for the real transmissionis computed the following way:

pt+1 f p Ui) (7)

where pi+1 is the real transmission probability applied in thenext period, pi is the vector of probabilities from the previousiteration and ui is the vector of the utilization measured inthe ith period. The function f() establishes the connectionbetween the probability applied in future transmissions andthe experienced utilizations during the last measurement.

(8) shows the form we used to gain the transmissionprobability

p+l Pr + p* (8)Pr 2(8where p' is that probability which has the largest channelutilization measured in the ith iteration.

Choosing this mapping can ensure that there will be norapid changes in the values of used transmission probabilities,because the physical p-value for the next iteration is selectedfrom a given proximity of the previous one.

At the same time this transmission probability is chosenfrom the side of the virtual probability value which providesthe highest throughput in order that the channel would bebetter utilized during the next measurement.

Finally we have to provide a new set of virtual probabilitiesfor the virtual transmissions carried out in the next cycle of theoperation. It can be assumed that there are no rapid changes inthe state of the network so the optimal transmission probabilityin the next iteration will be close to the previous one.New virtual probabilities are picked according to an uniform

distributed random variable with mean value of p'+l. Choosing

this distribution ensures that the virtual probabilities will bein close proximity of p'+1 with a good chance. The devianceof the uniform distribution can be expressed as

cf = max{lu' - u'l, lu'- (9)

where u4, ul are the two highest utilities and u' is theutilization factor related to the transmission probability appliedin the previous iteration.

However, the algorithm can use many virtual transmissions,in order to eliminate transient changes in the network state theprocedure replaces only the two virtual probabilities, whichyielded the lowest throughput and the others remain the samefor the next measurement period.The primary goal of the candidate probability selection

algorithm is to provide a method that converges to the op-timum transmission probability as fast as possible. Thus, thecandidates are taken from an interval which gets smaller andsmaller after each iteration of the algorithm. This behaviouris achieved by successively decreasing the deviation of thedistribution which ensures that the candidates are picked fromthe close proximity of the estimated optimum -transmissionprobability, whereas it maintains a small uncertainty in thesystem that enables it to re-adapt in case of bigger changes inthe network configuration.The procedure described above produces the new set of

virtual probabilities at the end of each measurement period.When a terminal is tumed on, however, it has no informationfor the computation of the virtual probabilities and what ismore important the transmission probability for real transmis-sions must be initialized, as well. As pointed out in Section IIoverestimating the network population has less impact on theperformance than underestimating it therefore the chosen realtransmission probability is close to zero. Theoretically, virtualprobabilities can be any values between 0 and 1 but selectingvalues for typical network sizes makes the convergence to theoptimum faster.

IV. PERFORMANCE EVALUATION

The performance of the protocol is evaluated by runningsimulations in the OMNeT++, event-driven simulation frame-work. In this paper the VTBM procedure capacity is estimatedby developing a model with finite number, n, of stationsoperating in asymptotic conditions. This means that all then nodes always have a packet ready for transmission. Thecommunication medium is slotted, thus the nodes can accessit at the beginning of the slots. The errors arising from signal-spreading are not taken into consideration, so there is nodelay, fading and damping. The terminals use carrier sensingto decrease the chance of collisions. Both the DIFS and SIFSconstants are one slot time long.

Table I summarizes the parameters used in the simulationsfor the exponential backoff and the VTBM algorithm, re-spectively (all units are measured in time-slots except for theprobabilities).

Figure 3 shows the obtained throughput values as the func-tion of the population size for the different MAC procedures.

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TABLE ISIMULATION PARAMETERS.

Exponential backoffCWmino 1024 ts

CWmax 16 ts

Virtual p-values (initial)po 0.050pi 0.70P2 0.20p3 0.1

P4 0.01T (measurement period)

1500 ts

L (packet size)100 ts

a0-

o.e.

I95 10 1S 20N-U, dS5

Fig. 3. Throughput of the MAC algorithms.

Note, that the VTBM protocol provides better efficiency thanthe exponential backoff in all cases that can be attributed tothe estimation algorithm. As the exponential backoff algorithmuses a monotonically increasing contention window to delaytransmissions when a collision occurs there can be numerous

unutilized time-slots in the system even if only a few terminalsaccess the channel. In contrast, the VTBM method does notchange the transmission probability during the measurementperiod that can enable the collided packets to be resent in theconsecutive time-slot.

A medium access protocol is expected to ensure that eachnode can access the channel and not only a set of the activeterminals have the opportunity to transmit via the medium.In this scenario we chose three of the nodes at random andinvestigated their throughput one by one at four differentnetwork populations.

The results are depicted in Figure 4. The average values inthe figure are computed as the aggregate throughput achievedby a given population divided by the number of nodes. Apartfrom the fact that our algorithm provides high throughput itcan be declared fair, as well.

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V. CONCLUSIONS

In our paper we have focused on a novel approach to theproblem of medium access control in wireless networks.

It is shown that the transmission probability has a seriousimpact on the throughput and the optimal probability valuedepends on the number of the active stations.We pointed out the importance of accurate estimation for the

number of active stations and found that the system toleratesthe estimation error as long as it does not exceed the order ofo(1).A novel algorithm is proposed that is based on the dynamic

p-persistent method and it aims to estimate the transmissionprobabilities for the communicating nodes by using virtualtransmission threads.We also presented our simulation results, which indicate

that the proposed algorithm can outperform the 802.11 DCFregarding throughput. We also demonstrated the fairness ofour algorithm.

In the near future we are planning to extend our analysisto the selection of virtual probabilities and we also want toevaluate scenarios based on more realistic mobility modelsand use traffic patterns that characterize mobile terminals. Wealso would like to focus on scalability issues.

ACKNOWLEDGMENT

This research work is supported by OTKA F042590 andTET GR-29/03

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