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ICWMMN2006 ProceedingsA Link Adaptation Scheme in Wireless LAN

Based on Channel EstimationLiquan Chen, Aiqun Hu

Department of Radio Engineering, Southeast University Nanjing 210096, P. R. ChinaE-mail: chenliguan@nju.org.cn

AbstractA novel link adaptation scheme using linear Auto Regressive(AR) channel estimation algorithm to enhance theperformance of auto rate selection mechanism in IEEE802.11 g is proposed. Low efficiency caused by time intervalbetween the Received Signal Strength (RSS) measured t imeand the frame transmitted t ime is overcome. The best rate isselected based on data payload length, frame retry count andthe est imated RSS value. Simulation esult s show that theproposed scheme enhances mean throughput performance upto 7% in saturation state, and up to 24% in finite load statecompared with those non-estimation schemes. Performanceenhancement s in average drop rate and a-erage number oft ransmission attempts per data frame del ivery also val idatethe effectiveness of the proposed scheme.Keywords: link adaptat ion, channel estimation, receivedsignal strength, wireless LAN.1 IntroductionThe rate selected for a given data frame transrrussion in802.11g ranges from 1Mb/s to 54Mb/s [ll. However, how toselect the best rate based on the speci fically factors at a giventransmitted time is an important issue to be resolved. Thereare several auto rate selection schemes which select ratebased on keeping track of a timing function and othermanners such as ARF scheme, RBAR algorithm and OARscheme [2,3 ,41. Request ing many changes to the IEEE 802.11standard make them no t to be the practical schemes forWLAN products in market.Basically , the mechanism to select one out of multipleavailable rates at a given time is referred to as link adaptation.The auto rate selection schemes based on link adaptat ion area lways make up of two parts. One is the measurement ofsystem status elements such as data payload length, wirelesschannel condi tion, and the frame retry count; the other is theintroduction of an appropriate rate selection scheme based onthe above measurement. The major challenge of linkadaptation scheme is to measure the up-to-date wirelesschannel condition. A scheme by measuring the receivedframes' RSS in sender-side to determine the receiver-sidechannel condi tions in a relat ive manner has been proposed in[5]. It is a pract ical scheme. However, because the wirelesschannel conditions are a lways in fluctuation and the MACprotocol of IEEE 802.11 s tandard under DCF uses Carrier

Sense Multiple Access/Collision Avoidance (CSMA/CA)mechanism to access wireless medium, the transmission timeof a given data frame has been proven to be in a relativerandom state [6 , 7]. According to the link adaptat ion schemeproposed in [5], there is t ime interval between the t ime whenthe RSS is measured from the previous rece ived frame andthe time when rate is selected for the next frame transmissionattempt. Bpecially , when WLAN sta tion runs in the finiteload state, this time interval becomes large. This time intervalproblem is not considered in [7], neither has [5] proposed onepractical scheme to determine the real t ime fluctuation of thewireless channel condition.In this paper, the effect of this time interval is firstly analyzed.A linear AR model channel estimation algorithm to estimatethe exact channel condition for a given data frametransmission is proposed . Together with the data payloadlength and the frame retry count, the best rate for the nextdata frame transmission attempt is selected based on the tablelooking-up scheme proposed in [7], and the system effectivethroughput is enhanced.2 Link adaptationwith channel estimationAccording to the relations between Bit Error Rate (BER) andSNR for different transmission rates in WLAN , bymaintaining enough BER for successful receival of thet ransmit ted frame, the select ion of a rate for a giventransmission attempt is based on the relat ive receiver-sideSNR level, also RSS at this given t ime. In wireless channel ,the RSS at the receiver side (in dB) is actually equal to thet ransmi t power level (in dBm) minus the path loss value (indB). When source station gets the RSS of the previousreceiving frame transmitted from the destination station, thepath loss value is calculated out. This path loss value alsoreflects the path loss from source station to destination station.Based on the above assumptions, this measured RSS reflectsthe receiver side RSS ofthe destination station.However, when the time interval between measurement andtransmission is large, the path loss value between the timewhen RSS is measured and the t ime when the next data frameis transmitted is not constant in real time. With large timeinterval, the RSS measured from the previous received framecan not actually reflect the RSS when the next frame isreceived in the destination station. As shown in Fig. 1, there istime interval '!;ntcrval between the previous received frame andthe next data frame transmission attempt (Here, t is the air

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propagation delay). When the RSS from the previous receivedframe is used to determine the best rate for the next dataframe transmission, the precision of this rate selection schemebecomes bad, the mean throughput of this system isinfluenced by this lower precision.

rate selection of node B is the same as those proposed in nodeA.Regarding to the estimation algorithm, for easy realization,we select the Minimum Mean Squared Error (MMSE) l inearestimation scheme based on l inear AR model to est imate theRSS from the recorded ones. In linear AR model, we specify/ , - l l- t

Destin.ation I Framel I Istation \ I fr ame2 I pi n= -La k.), -k .h I (1)

Fig. 2 Architectureof the proposed link adaptation scheme

(4)

(3)

(2)

mIkm= -[Lam-I(k )r,.(m- k) + rx(m)]/ Pm -Ik=1

and the reflecting coefficient is defined as

3 Analysis of the estimation step-size

where [ 13 ] is the average backoff window size , 7;101 is theslot interval defined for the different wireless physical layers,Tphysical is the transmission duration of physical layer frame, 1"is propagation delay, TDIFS and 'TsIFS are t ime duration ofDistributed Inter- Frame Space (OIFS) and Short Inter-FrameSpace (SIFS), TACK is the transmission time of ACK frame,

T stcp = [13]7;101 + y;'hysical + 21"+ TSIFS + TolFs + 'I;.CK + EfIDLE]", (5)

According to IEEE 802.11 standard under contention-basedDCF, all WLAN stations must contend for the opportunisticto access the wireless medium [6]. The time for every frametransmission attempt is in random state according to differentsystem load and the fluctuation ofwireless channel. Therefore,the step-size of the proposed estimation algorithm must adaptto this random access state to balance the precision ofestimation and computation complexity. Referring to IEEE802.11 standard, he estimation algorithm step-size 7;,cp isdefined as

Here i n is the n-th estimated RSS, i n_k is the recorded RSSof the (n-k)-th, and a , is the major parameter of this l inearAR model. The recorded RSSs range from Xo to XN_1 whilen range from zero to N -I + p . p is the rank of this l inearAR model, N is the historical record length. We useLevinson-Durbin iterative algorithm to calculate theparameter a, as

where ",(m) is the self-correlation of the m-th RSS and Pm;nis the mean minimum value of {x(n) -x(n)} . When a , iscalculated, the n-th est imated RSS can be calculated from Eq.(1). Here, the est imat ion precision is determined by N and p .The larger the N and p are, the more precision theestimation is, but the more memory and time the es timationneeded.

The MMSE is specified asPm = Pm -I[I - k;;,], Po = rx(O) = I ,

f rame2 IRaft stleetlun

rame l IKSS measurement

ourcestation -+----+---- '-------------11------ '....

Then, we propose a novel scheme to solve this time intervalproblem. First, we built up a queue to save the RSSs of theprevious received frames, which are received from the taggedstation. Second, when a newly data frame is ready to betransmitted to the tagged station, a l inear AR model channelest imation algor ithm is used to est imate the est imated RSSbased on the recorded RSSs. Thi s estimated RSS, togetherwith the data payload length and frame retry count, is used toselect the best rate for this data frame transmission attempt.The architecture of the proposed channel estimation linkadapta tion scheme is shown in Fig. 2. In this figure , node Ameasures the RSSs of the frames received from differentt agged des tinations and save them in different his toricalqueues, one queue corresponds to one tagged destina tion.When node A is ready to send a data frame to one of thesedestinations, it f irst estimates the exact RSS for the time whenth is frame reach the tagged des tina tion from the recordedRSSs. The estimated RSS, together with the frame length andframe retry count parameters, is used to determine the bes trate for th is g iven t ransmiss ion attempt. The rate may beselected through looking-up a table which is built up byapplying the dynamic programming technique as specified in[7] and indexed by the system status t riplet which consist ofthe estimated RSS, frame payload length and frame etrycount . The step-size of the proposed estimation algorithmmay be adapt ively adjusted according to the system load andthe fluctuation of the wireless channel condi tion to balancethe estimation precision and computation complexity. The

Fig.1 Time interval betweenRSSmeasurementand rate selection

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and E[IDIE] is the average idle time when backoffprocedureof this s ta tion has f in ished. There are two circumstances wemust consider.

number n can be calculated out from E[ K ] .4 Simulations and discussion

When the up-to-date contending station number n iscalculated out, ?;'yc1c can be used to replace 7;tp as the step-size for this linear AR model estimation.

(1) First, \\hen there are only two stations in th e WLANsystem, and these stations are running in saturate status (i.e. ,there is always a transmission attempt in every T,lot interval),Eq. (5) is rewri tten as

1.0.2

__ eas ic access, zrrvs speea-- Basic access, 5m/s speed---.t.- Basic access, 8m/s speed

o + - - - - - . - _ _ r _ _ _ _ _ _ , _ _ _ r _ - . - - - - - - . - _ _ o _ - - - - - . - _ _ r _ _ _ _ _ _ , r _ 0.0 0.4 0.6 0.8

Saturation status

Fig. 3 Mean throughput diversificationvs. different saturate level atdifferent moving speeds

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To prove the e ffec tiveness of the proposed link adaptat ionscheme specified above, we use the NS -2 network simulatorfrom U. C. Berkeley to setup the s imulat ion model in Linux[ 101 The effectiveness of the proposed scheme (specified asLA-new scheme) is simulated when the wireless LAN systemis run in multiple nodes mode with different system loads anddifferent nodes' moving speeds. The enhanced performanceof the proposed scheme compared with those non -estimationschemes (specified as LA -old scheme) in average drop rateand average number of transmission attempts per data framedelivery are also simulated.The system loads and moving speeds simulation results areshown in Fig. 3. We can draw conclusions from this figure asfollows. First, with the decrease of saturate status of thesystem, it becomes obvious that the mean throughout of LAnew scheme with channel est imat ion is superior to LA -oldscheme. For example, when the moving speed is 10mls, andthe saturate status level is 1.0, the mean throughpu timprovement of LA -new scheme against to LA -old scheme isabout 7% ; when the saturate status level is 0.2 , theimprovement reaches 24%. Second, in different speeds, theimprovements are different. The improvement of meanthroughput performance in h igher moving speeds, such asIOmls, is greater than those in lower moving speeds.

Then, we evaluate the changes of total mean throughputperformance according to the changes of the number ofcommunication nodes. Assume that all the communicationnodes are work in saturate s ta tus, and the wireless channelmodel is Rayleigh fading model. As shown in Fig. 4 , whenthe node numbers are increased , the total mean throughpu tperformances of LA -new scheme and LA -old scheme changeto low state, bu t the performances of LA -new are alwayshigher than those of LA -old. The reasons for these areexplained as follows. When the node numbers a re increased,the collisions caused by content ions among those nodesbecome serious, thus the time interval between thetransmissions of frames in each of these nodes is enlarged.

(9)

(7)E[Length]nTphysical = Tph .hd +Tmac.hcd + Rate

7' T 2 T. T. T. CWmin 7'c de = ph-sical+ ,+ SIFS + ACK + D1FS +-- sialY j n+]There are two exist ing methods used to measure the numberof active contending station number n , one is based onprobability measurement and the other is based oninterruption measurement [9] The method based oninterruption measurement is used to approximate thecontending stations number. By measur ing the number ofinterruption K in one backoffperiod, the contending stations

where the average backoff duration E[BJ is definedas E[B]=(CWmin - I ) /2 , and CWmn is the minimum contendwindow specified in the IEEE 802 .11 standard. Tphyical iscalculated as

(2) Second, vhen there are multiple stations transrmttingframe in the WLAN sys tem, and all the stat ions are runn ingin saturate s ta tus, we first def ined the t ime between the s ta rtof two payload transmissions as ?;'ylc' Cit ing from [8], we getthe definition

where Tphy.h cd and 7 a - h c d are the transmission time for thephys ical layer header and MAC layer header, E[Length]n isthe n-th average length of the transmitted frame, and Rate isthe transmission rate for the (n+ l)-th frame. In practice, whenthere is a frame transmitted , E[Length]n is approximated byexploiting a moving averaging window as

E[Lengthl.+1= aE [Lengthl. + (I-a)Lengthn+1 (8)In Eq, (8), E[Length]n+! is the approximation of E[Lengthl. atthe end of the (n+1)-th transmission attempt, Lengthr; is thelength of the (n+1)-th transmitted frame, respectively. a is asmoothing factor.However, when the transmissions of these s ta tions are not insaturation, E[IDLE]n must be added to Eq. (6) to consider theidle time In non-backoff period. E[IDLE]n+l is alsoapproximated by exploit ing a moving averaging window. f3is a the smoothing factor of E[IDLEl,,+I'

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LA-new scheme, which adopts channel estimation algorithmto overcome the low efficiency caused by this time interval,can improve the precision of the transmission rate selection ,thus enhance the total mean throughput performance whennode numbers are increased. In the other hand, LA -oldscheme can not select the best rate for the exact nexttransmission attempt when the time interval is large, thus theperformances of it is lower than those of LA-new scheme.

References

It is proven that auto rate select mechanisms can improve theprecision of rate selection, and enhance the mean throughputperformance of wireless LAN system. In this paper, a novellink adaptation schemewith channel estimation is proposed toovercome the low efficiency caused by the time intervalbetween the RSS measured time and the frame transmissiontime. From the simulation results , we can conclude that theproposed scheme is especially fit for high interfere wirelessLAN environment and the wireless LAN environment wherethe communicating nodes' moving speeds are high.

[I] IEEE 802.llg standard, PartII: Wireless LAN mediumaccess control (MAC) and physical layer (PHY)specifications: further higher data rate extension in the2.4GHz band, IEEE WG, June, (2003).[2] Van der Vegt A J, "Auto rate fallback algorithm for theIEEE 802.11a standard", Master thesis , UtrechtUniversity, (2002).[3] Holland G, Vaidya Nand Bahl P, "A rate-adaptive MACprotocol for multi-hop wireless networks",MOBfCOM'Of, pp. 236251,(2001).[4] Sadeghi B, Kanodia V, Sabharwal A, et. a\.,"Opportunistic media access for multi-rate ad hocnetworks",MOBfCOM '02, pp. 486-497, (2002).

[5] Pavon J P, Sunghyun Choi, "Link adaptation strategy forIEEE 802.11 WLAN via received signal strengthmeasurement", fCC '03,2, pp. 1108-1113, (2003).

[6] IEEE 802.11 standard, Part I I : Wireless LAN mediumaccess control (MAC) and physical layer (PHY)specifications, IEEE WG, August, (1999).[7] Qiao D J, Sunghyun Choi, Shin KG , "Goodput analysisand link adaptation for IEEE 802.lla wireless LANs",fEEE Trans. Mobile Computing , 1(4), pp. 278-292,(2002).[8] Tay Y C, Chua K C, "A capacity analysis for the IEEE802.11 MAC protocol", Wireless Network, 7(2), pp. 159171,(2001).[9] Lin Z H, Huang A P, Qiu P L, "Measurement of numberof contending stations in IEEE 802.11 WLAN', JournalofCircuits andSystems, 8(5), pp. 37-42, (2003).

[10] UC Berkeley, Ns notes and documentation,http://www.isi.edu/nsnam/ns/ns-documentation.html.December, (2003).

5 Conclusions

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10.510.0 -- LA-new scheme-- LA-old schemeUl 9.50-.c 9.0

:; 8.50-J:: 8.0l"e 7.5;c: 7.0IIQ)E 6.5]i0 6.0r- 5.55.0

Node numberFig.4 Mean throughput vs, node numbers

Finally, we evaluate the enhanced performance of theproposed scheme compared with LA-old scheme in averageframe drop rate and average number of transmission attemptsper data frame delivery aspects. The transmission load ischanged among 800Kbps, 8Mbps, 30Mbps, and 60Mbps. Weget the reduced average frame drop rate ratio and reducedaverage number of t ransmission attempts per data framedelivery ratio of the proposed scheme compared with LA-oldscheme within these simulations. From Fig. 5, we canconclude as follows. First, the performances of the proposedscheme in average frame drop rate and average number oftransmission attempts per data frame delivery are better thanthose of LA-old. Second, those performance enhancementsare decreased as the transmission load increases because theproposed scheme has higher effectiveness In lighttransmission load.

10 20 30 40 50 60Tansmission load (Mb/s)

Fig. 5 Average attempt per packet and average drop packetsvs.transmission load