Proposed a simple decentralised algorithm for channel allocation that is provably correct and requires no message passing or common administrative control between interfering WLANs. In this paper we implement this algorithm using a standard 802.11 hardware testbed and demonstrate that it does indeed offer the potential for effective channel allocation in realistic environments. This includes environments with complex, spatially varying noise and channel dependent propagation behaviour and with time-varying load

Clifford, P.

Leith, Douglas J.

Malone, David

Reid, D.

English

MURAL - Maynooth University Research Archive Library

Experimental Implementation of Optimal WLANChannel Selection Without CommunicationD.Malone, P. Clifford, D.Reid, D.J.LeithHamilton Institute, National University of Ireland, Maynooth, IrelandAbstract—[1] proposed a simple decentralised algorithm forchannel allocation that is provably correct and requires nomessage passing or common administrative control betweeninterfering WLANs. In this paper we implement this algorithmusing a standard 802.11 hardware testbed and demonstratethat it does indeed offer the potential for effective channelallocation in realistic environments. This includes environmentswith complex, spatially varying noise and channel dependentpropagation behaviour and with time-varying load.I. INTRODUCTION AND TESTBED SETUPFor the Communication Free Learning (CFL) algorithmitself, a full list of references and a survey of related workplease see [1].The testbed consists of 10 PC-based embedded Linux boxesbased on the Soekris net4801, 5 boxes configured as APs ininfrastructure mode and 5 as client stations. We also use 5PCs acting as monitoring stations to collect measurements toensure that there is ample disk space, RAM and CPU resourcesavailable for collection of statistics. These machines are setupas five WLANs (denoted WLAN A - WLAN E) located ina university office space as shown in Figure 1. All systemsare equipped with an Atheros AR5004G 802.11a/b/g mini-PCI card with an external antenna. All nodes use a Linux2.6.16.20 kernel and the MADWiFi wireless driver. All of thesystems are also equipped with a wired Ethernet port, whichis used for control of the testbed. Specific vendor features onthe wireless card, such as turbo mode, are disabled. Channelscanning is also disabled as we use the CFL algorithm forchannel selection. Unless otherwise stated, all of the testsare performed using the 802.11a physical transmission rateof 18 Mbps with RTS/CTS enabled and the channel numberexplicitly set. With this PHY rate and using 1500 byte packets,the achieved throughput in an isolated WLAN is measured tobe approximately 13 Mbps.To generate wireless network traffic and to measure through-put we use mgen. While many different network monitoringprograms and wireless sniffers exist, no single tool provides allof the functionality required and so we have used a numberof common tools including tcpdump. Network managementand control of traffic sources is carried out using ssh over thewired network.II. IMPLEMENTATION OF CFL ALGORITHMThe CFL algorithm requires no special hardware supportand, in addition to avoiding message passing, does not requireThis work was supported by Science Foundation Ireland grant IN3/03/I346.Fig. 1. Plan showing wireless node locations.clock/slot synchronisation between interfering WLANs. Thealgorithm is implemented as a user-space perl script thatruns on each WLAN AP. WLAN-wide channel switchingis achieved by a broadcast instruction from the AP that isreceived by a user-space script running on each WLAN clientstation, which then uses the iwconfig command to changechannel.The CFL algorithm requires a measure of channel quality.We initially investigated using the RSSI value returned by theAP wireless NIC. However, we found this value to be unre-liable – when channel quality is degraded due to interferingWLANs it is quite possible for the background noise level tobe low yet for the frame error rate to be high due to collidingtransmissions. We therefore use a direct measure of frame errorrate as our channel quality metric. Channel quality is estimatedfrom the average frame error rate measured over a 10 secondinterval; this duration was chosen experimentally.To allow scripting entirely within user-space we took ad-vantage of RTS/CTS. Using tcpdump to monitor packetstransmitted, over 10 second intervals we collected statisticson (i) RTS transmissions for which no CTS handshake wasreceived, (ii) transmissions for which the RTS/CTS handshakewas successful but the data packet transmission was notACKed, and (iii) transmissions with successful RTS/CTS anddata/ACK handshakes. We label (i) as CSMA/CA collisions,(ii) as frames lost due to interference and (iii) as successfultransmissions. The first of these labels is approximate asRTS/CTS handshakes may be lost due to interfering trans-missions or noise. However, the CFL algorithm only requiresa coarse good/bad measure of channel quality and we find thatmeasuring channel quality by the percentage of type (ii) eventsand thresholding at 10% is effective. We are also investigatingother measures [2].1-4244-0663-3/07/$20.00 ©2007 IEEE 316Authorized licensed use limited to: The Library  NUI Maynooth. Downloaded on July 6, 2009 at 12:40 from IEEE Xplore.  Restrictions apply.III. NATURE OF INTERFERENCE ENVIRONMENTFirst we attempted to characterise the interference environ-ment in our testbed. 0 1 2 3 4 5 6 701 02 03 04 05 06 07 08 09 10 11Throughput (Mbps)Channel #WLAN E Throughput Throughput SDFig. 2. Baseline throughput for WLAN C versus channel number in 2.4GHzband (no other WLANs active).The testbed hardware supports operation both in the 802.11a5GHz band and in the 802.11b 2.4GHz band. Spectrumanalyzer measurements revealed little external interference inthe 5GHz band (a noise floor of around -80dB being typical),significant external interference was observed in the 2.4GHzband. Figure 2 shows measured throughput versus channelnumber in the 802.11b band for WLAN C – none of the otherWLANs active here, so there is no testbed related interference.It can be seen that there exists significant background noise onchannels 7-10. We note that the level of external interferenceis strongly location dependent and is essentially negligible forWLANs B and E which are located approximately 10m furtherthan WLAN C from the interference source.Figure 3 shows measurements of the mean rate of successfultransmissions versus channel number when a single WLAN isactive (WLAN E). Measurements are repeated about an hourapart. The time-varying nature of the channel quality is evident– e.g. compare channels 48 and 153.Also marked on Figure 3 are error bars that indicate thestandard deviation of the error time history measured over aperiod of 50s. It is evident that variations in channel qualityalso occur on shorter time-scales. This is shown in more detailin Figure 4 which shows an example time history of measuredchannel quality over a period of approximately 60 minutes. Itcan be seen, for example, that the error rate rises to around15% for a period of about 10 minutes early in this experiment,then falls to around 3% after approximately 30 minutes.Our measurements indicate that the level of interferencebetween WLANs can be strongly channel dependent. Figure 5shows the measured interference level between WLANs B andC as the channel number is varied. We found this effect to beparticularly pronounced in the 5GHz band, with a significantlylower level of channel dependence measured in the 802.11b2.4GHz band.A. Spatial ReuseTo investigate the level of spatial reuse feasible in ourtestbed, we measured the frame error rate between pairs of 0 2 4 6 8 10 12 1436 40 44 48 52 56 60 64 149 153 157 161 165Throughput (Mbps)Channel #WLAN E Throughput WLAN E Throughput SD(a) 0 2 4 6 8 10 12 1436 40 44 48 52 56 60 64 149 153 157 161 165Throughput (Mbps)Channel #WLAN E Throughput WLAN E Throughput SD(b)Fig. 3. Measured throughput with a single WLAN active (no interferingWLANs). Measurements are shown for WLAN E over the range of 802.11achannels. The upper and lower plots are about 1 hour apart. Observe thesubstantial variation in throughput both with channel number and time. 0 2 4 6 8 10 12 14 16 18 20 0  500  1000  1500  2000  2500  3000  3500  4000Error Rate (%)Time (s)Fig. 4. Example of time-varying channel quality.WLANs as the channel used by one WLAN was varied.Initially we consider the behaviour when the 802.11a 5GHzband is used. Figure 6(a) shows the measured throughputs ofWLANs A and E when WLAN E is held fixed on channel 36while the channel used by WLAN A is varied between channel36 and channel 64. Figure 6(b) shows the correspondingmeasurements for WLANs C and E. Note that unlike in802.11b/g, 802.11a channels are not numbered consecutivelyi.e. channels 36 and 40 are in fact adjacent. Observe fromFigure 1 that WLANs A and E are located adjacent to eachother whereas WLANs C and E are located approximately 10mapart. We therefore expect that a larger separation in channelsis needed between WLANs A and E than between WLANs Cand E and indeed our measurements support this prediction.317Authorized licensed use limited to: The Library  NUI Maynooth. Downloaded on July 6, 2009 at 12:40 from IEEE Xplore.  Restrictions apply.36 40 44 48 52 56 60 6400.050.10.150.20.250.30.35Channel #Packet error rateFig. 5. Measured interference induced error rate versus channel number in5GHz band. Here WLANs B and C both transmit CBR traffic on the samechannel. Plot shows measured packet error rate at WLAN C as the channelnumber used for transmission is varied (with WLANs B and C always sharingthe same channel)..It can be seen that when WLAN A is located on channel 56and above, the aggregate network throughput is 26Mbps whichis approximately the maximum combined capacity that can beachieved by two independent WLANs for the 802.11a settingsused here. Observe also that both WLANs achieve throughputsi.e. network capacity is allocated equally. However, whenWLAN A is on a channel that is closer to that of WLANE we have that (i) the aggregate network throughput fallssubstantially and (ii) the WLANs can experience dramaticallydifferent throughputs (e.g. when WLAN A uses channels 44or 48 it achieves a throughput close to zero, while WLAN Eachieves throughput close to 12Mbps). The latter unfairnessis associated with hidden node type effects that occur whenthe WLANs operate on channels that are sufficiently closefor their transmissions to interfere yet not so close that theycan successfully decode each others transmissions. When theWLANs operate on the same channel, they can decode eachothers transmissions since the WLANS are located near toeach other and thus the 802.11 CSMA/CA operation fairlyallocates the available bandwidth. However, the aggregatenetwork throughput is half that achieved when the WLANsoperate on orthogonal channels.This behaviour can be contrasted with that of WLANsC and E. It can be seen from Figure 6(b) that even whenWLANs C and E use adjacent channels the aggregate networkthroughput is nevertheless close to 26Mbps. Note that WLANsC and E are located only 10m apart, yet the attenuation dueto walls etc when combined with the attenuation betweenadjacent channels is sufficient to effectively yield orthogonalityof transmissions.IV. COMMUNICATION-FREE CHANNEL ALLOCATIONALGORITHM EXPERIMENTAL RESULTSA. Convergence to non-interfering channel allocationTo demonstrate the CFL algorithm for channel selection, wesimultaneously generated traffic between the nodes on each 0 5 10 15 20 25 3036 40 44 48 52 56 60 64Throughput (Mbps)Channel #WLAN A ThroughputWLAN E ThroughputWLAN A Throughput SDWLAN E Throughput SDTotal SD(a) WLANs A and E (x-axis marks channel used byWLAN A). 0 5 10 15 20 25 3036 40 44 48 52 56 60 64Throughput (Mbps)Channel #WLAN C ThroughputWLAN E ThroughputWLAN C Throughput SDWLAN E Throughput SDTotal SD(b) WLANs C and E (x-axis marks channel used byWLAN C).Fig. 6. Measuring potential for channel reuse. Using the 802.11a 5GHz band,WLAN E is held fixed on channel 36 while the channel used by second WLANis varied. Measurements are shown for WLANs A and E and for WLANs Cand E. Height of histogram indicates aggregate throughput of both activeWLANs. Light shaded area marks throughput of WLAN E and dark shadedarea marks throughput of second WLAN. Also marked on the histogram arethe standard deviations of the throughput, which give a measure of throughputvariability – it can be seen that the standard deviations are consistently low.WLANs A and E are located adjacent to each other whereas WLANs C andE are located approximately 10m apart.of the five WLANs. To create a relatively demanding channelallocation task, the channel allocation algorithm was restricted(via scripting) to the use of four 802.11a channels. Initially,all WLANs are started on the same channel.Figure 7 shows traces of the channel selection time his-tories for each of the five WLANs as we run the CFLalgorithm. Throughput significantly increases once a non-interfering channel allocation is selected, yielding a substantialincrease in network capacity: the aggregate throughput from50-60 seconds is approximately 51 Mbps compared with 11.31Mbps when the WLANs all use the same channel. That is,we obtain approximately a factor of four increase in networkcapacity through appropriate channel selection.B. Convergence RateFigure 7 shows that the network converges to a non-interfering channel allocation in approximately 20 iterations.The duration of an iteration is determined by the time requiredto sense channel quality and is set to 10s in our tests yieldingan overall convergence time of 200s. Of course, during thisconvergence period the network continues to achieve a signif-318Authorized licensed use limited to: The Library  NUI Maynooth. Downloaded on July 6, 2009 at 12:40 from IEEE Xplore.  Restrictions apply.364860157 0  20  40  60  80  100  120Channel #IterationWLAN A WLAN B WLAN C WLAN D WLAN EFig. 7. WLAN channel time histories. Five WLANs, four available channels.Note that in this example the network settles on only three channels.icant level of throughput. Hence, the cost of the convergenceperiod in terms of throughput is limited.Secondly, the simulation analysis in [1] indicates that theCFL algorithm converges rapidly under a wide range ofconditions and this is confirmed in our experimental tests. Forexample, the mean convergence time measured over 10 tests isfive iterations with five WLANs and four available channels.C. Controlling local channel reuseObserve in Figure 7 that WLAN B and WLAN D settleon the same channel. It can be seen from Figure 1 that theseWLANs are located near to each other and on closer inspectionof packet traces we find that the nodes in these WLANs arevisible to each other (no hidden nodes). That is, both nodesinvolved in a collision are able to detect that the collisionoccurred, thus the 802.11 CSMA/CA MAC is able to scheduletransmissions properly and the frame error rate (i.e. packetlosses not associated with CSMA/CA collisions) is low. Sinceour objective here in allocating channels is to avoid hiddennode and interference related problems, this behaviour is asexpected. Indeed, it seems desirable in dense deployments asit increases the level of channel reuse. That is, channel reuseis possible not only between WLANs located so far apart thattheir transmissions do not interfere, but also between WLANslocated close together so that CSMA/CA operates correctly.It is straightforward to force nearby WLANs to use dif-ferent channels by observing beacons; this has been verifiedexperimentally.D. Impact of external/channel dependent interferenceOur measurements of the testbed interference environmenthighlight the presence of external interference sources in the2.4GHz band, and the channel dependent nature of the levelof interference between WLANs.Returning to the channel dependent interference betweenWLANs B and C noted in Figure 5, we recorded statistics onthe channels selected by these WLANs over a series of 10tests. In line with Figure 5 we find that, as expected, the CFLalgorithm settles on either channel 36,40 or 64 and avoids thelower quality channels. Similarly, in the case of WLAN E itcan be seen from Figure 3 that the quality of certain channelscan be strongly time-varying. We can also observe in Figure3 that certain channels are consistently of good quality, e.g.channels 36-44 and 60-64. Our measurements confirm thatthe CFL algorithm automatically adapts to channel dependentinterference by avoiding the low quality channels and settlingon the good quality channels.E. Time-varying network conditions364452153 0  20  40  60  80  100  120Channel #IterationNetA NetB NetC NetD NetEFig. 8. Example of a new WLAN becoming active. Four WLANs activeinitially, with fifth WLAN beginning at time 60.The level of interference between WLANs is dependenton the traffic load on each WLAN. In particular, when aWLAN carries no traffic and therefore generates essentiallyno interference. Importantly, when a WLAN that has beeninactive becomes active, we require to allocate a channelto that WLAN and this may require reconfiguration of thechannel allocations used by other nodes. Since the CFLalgorithm is convergent (i.e. stays settled on a non-interferingchannel allocation once it has found one), it can be left runningat all times. Changes in the network, such as a previouslydormant WLAN becoming active, that create new interferencewill then automatically activate the CFL algorithm to adapt thechannel allocation to restore a non-interfering allocation. Thisis illustrated in Figure 8. Here, we start with four WLANswhich quickly settle on a non-interfering channel allocation.At iteration 60 of the CFL algorithm, a fifth WLAN isactivated (i.e. begins transmitting traffic). It can be seen thatthe network automatically reconfigures its channel allocationto accommodate this new WLAN and quickly settles on a newnon-interfering configuration. At time 50 the total throughputwas 50.1Mbps; at time 120 this had increased to 60.3Mbps.V. CONCLUSIONS[1] discusses valuable theoretical properties of the CFLalgorithm; in this paper we have demonstrated its ease ofimplementation and its practicality in a wide range of challeng-ing real world conditions. The algorithm is flexible enough toperform well even when not operating in the regime assumedby the theory.REFERENCES[1] Leith,D.J., Clifford, P., “A Self-Managed Distributed Channel SelectionAlgorithm for WLANs”. Proc. ACM/IEEE RAWNET, Boston, 2006.[2] Malone, D.W., Clifford,P., Leith,D.J., 2006, MAC Layer Channel QualityMeasurement in 802.11. IEEE Communcations Letters, February, 2007.319Authorized licensed use limited to: The Library  NUI Maynooth. Downloaded on July 6, 2009 at 12:40 from IEEE Xplore.  Restrictions apply.

Experimental implementation of optimal WLAN channel selection without communication

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Experimental implementation of optimal WLAN channel selection without communication

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