Available Bandwidth-Based Association in IEEE Wireless LANs

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1 Available Bandwidth-Baed Aociation in IEEE 82. Wirele LAN Heeyoung Lee, Seongkwan Kim, Okhwan Lee, Sunghyun Choi, Sung-Ju Lee 2 School of Electrical Engineering & INMC, Seoul National Univerity, Seoul, 5-744, Korea 2 Multimedia Communication & Networking Lab, Hewlett-Packard Laboratorie, Palo Alto, CA 9434 {hylee,kim,ohlee}@mwnl.nu.ac.kr, choi@nu.ac.kr, jlee@hp.com ABSTRACT The performance of an IEEE 82. tation heavily depend on the election of an AP (Acce Point) that the tation i aociated with to acce the Internet. The conventional approach to the AP election i baed on the received ignal trength called RSSI (Received Signal Strength Indication) from AP within the tranmiion range. Thi approach however, might yield unbalanced traffic load among AP a the tation chooe an AP only baed on the ignal trength, intead of conidering the AP load and the level of contention on medium acce. Accordingly, the tation that i aociated with the highet-rssi AP might uffer from poor network performance. In thi paper, we propoe a new aociation metric, EVA (Etimated available bandwidth) with which a tation can find the AP that provide the maximum achievable throughput among canned AP. EVA i deigned to etimate the available bandwidth on a channel with repect to a tation that i to join a WLAN (Wirele Local Area Network). A tation equipped with EVA oberve a channel tate in a per-lot bai, and yet doe not requet any external information from nearby AP or neighbor tation. Our etimation mechanim i nonintruive, fully ditributed, and independent of the infratructure. Through imulation tudy, we evaluate the accuracy of the etimation and how that EVA-baed aociation yield enhanced throughput performance compared with the legacy cheme. Categorie and Subject Decriptor C.2 [Computer Sytem Organization]: Computer-Communication Network ; C.2. [Computer-Communication Network]: Network Architecture and Deign Wirele communication Thi work wa upported in part by Seoul R&BD Program (544). Permiion to make digital or hard copie of all or part of thi work for peronal or claroom ue i granted without fee provided that copie are not made or ditributed for profit or commercial advantage and that copie bear thi notice and the full citation on the firt page. To copy otherwie, to republih, to pot on erver or to reditribute to lit, require prior pecific permiion and/or a fee. MSWiM 8, October 27 3, 28, Vancouver, BC, Canada. Copyright 28 ACM /8/...$5.. General Term Algorithm, Meaurement Keyword IEEE 82. WLAN, Aociation, Available Bandwidth. INTRODUCTION Today, increaing number of uer acce the Internet via IEEE 82. WLAN (Wirele Local Area Network). We can eaily find AP (Acce Point) that are in the vicinity at public/municipal place. The election of the AP that a WLAN tation connect with mut be done prudently a it determine the performance of the tation. In the nomenclature of IEEE 82. [], uch AP election procedure i referred to a aociation. The mot widely ued metric for the aociation of WLAN tation i the received ignal power from an AP, known a RSSI (Received Signal Strength Indication). After canning the channel, a tation chooe the AP from which it receive frame with the highet RSSI. A revealed in the literature, however, uch an RSSI-baed aociation doe not necearily provide the bet throughput performance [2 4]. In addition, the RSSI-baed aociation might reult in unbalanced throughput among BSS (Baic Service Set). Therefore, a tation aociated with the highet-rssi AP might uffer from low throughput that reult from the overloaded bandwidth utilization in that BSS. We propoe a new aociation metric called EVA (Etimated available bandwidth) that i deigned to reflect the available bandwidth in a BSS, i.e., the maximum achievable throughput when aociated with the target AP. In order to accurately etimate the available bandwidth, EVA etimator conider the contention level on a BSS by calculating colliion probability and channel idle ratio baed on channel tate aement. After earching all acceible channel and, in turn, available AP on canned channel, a tation with the EVA etimator chooe the AP that provide the larget EVA. In order to make EVA a practical olution, we et the following deign goal. Firt, available bandwidth etimation hould be performed in a non-intruive manner a reource in WLAN i carce. Second, to avoid the modification of IEEE 82. protocol, EVA doe not require any extra frame exchange between tation and AP. Moreover, we do not For clarity, the term EVA will be ued to repreent the value of available bandwidth calculated through the propoed EVA etimator.

2 employ a centralized olution to control a WLAN or etimate available bandwidth. Third, our propoed approach hould provide highly accurate etimation in a timely fahion. Through imulation tudy, we evaluate the accuracy of the propoed EVA etimator on colliion probability and available bandwidth in a BSS. We alo evaluate the effectivene of EVA-baed aociation in term of individual and aggregate throughput performance. The ret of the paper i organized a follow. Section 2 review the related work, and the legacy 82. aociation proce i introduced in Section 3. The formulation of EVA i preented in Section 4. Section 5 decribe the algorithmic detail, and Section 6 how the accuracy of EVA etimator and the correponding throughput performance. Finally, the paper conclude with Section 7. IEEE 82. aociation procea Aociation Authentication Scanning tation probe requet (broadcat) probe repone probe requet (broadcat) authentication authentication aociation requet aociation repone AP elected AP 2. RELATED WORK RSSI i commonly ued a an aociation metric in WLAN. Thi approach i however, well known to have poor network performance when load ditribution i highly unbalanced [2 6]. There have been propoal to olve thi inefficient network reource uage. One of thee attempt i to receive information from AP. In [2, 4 6], by collecting information uch a the number of aociated tation or it tranmiion rate, tation can elect an AP with better link quality that i meaured by different method. Such an approach hould be accompanied with a protocol modification in the AP ide, and i not likely to work with already-deployed WLAN device. There are a few centralized olution [3,7]. A repreentative, uually the AP, control the whole aociation proce. Particularly, the AP decide whether to grant the aociation of tation. Thi approach alo need to modify AP behavior, and bring additional overhead to let the AP be aware of the detail of the entire network. In EVA, on the other hand, the channel tate i meaured paively. The EVA etimator operate in a ditributed manner (working only at the tation ide) and doe not require any modification of a WLAN infratructure. Furthermore, available bandwidth i predicted when the tation earche AP in the 82. canning proce and hence, doe not incur additional probing overhead. A imilar approach to our i preented in [8] and it algorithm work a follow. A tation oberve a kewed time period of beacon frame reception to etimate how much bandwidth i available. Although it i a purely non-intruive operation, it incur large delay for channel obervation and quality etimation a multiple beacon frame mut be received typically, beacon are tranmitted every milliecond. The etimation of available bandwidth on an 82. link i affected by many factor uch a retranmiion due to colliion or weak ignal, correponding increaed backoff interval, buy channel detection due to different ytem device, etc. VMAC [9] virtually run MAC proce at each tation to etimate colliion probability and available bandwidth. A it empirically oberve the wirele channel, VMAC can etimate the bandwidth that i fluctuating in a time varying manner. The convergence of VMAC etimation however, take relatively a long time a one etimate ample i collected after finihing every backoff procedure, i.e., taking at leat CW min 2 ttimelot duration to get a ample on average, where CW min i the minimum contention window ize and Figure : Frame exchange equence in the 82. infratructure mode. ttimelot i the interval of a ingle backoff timelot. Meanwhile, the EVA etimator oberve the wirele channel in a per-lot bai, thu enuring to have more number of ample and yielding fater convergence time than VMAC. There could be a tradeoff between convergence time and accuracy. We how that our approach get more ample and achieve fater etimation convergence without accuracy compromie in Appendix A. 3. ASSOCIATION IN 82. WLANS IEEE 82. aociation proce i divided into three tep: canning, authentication, and aociation, a illutrated in Fig.. The objective of canning i to elect an appropriate AP to be aociated with, in all available channel. There are two type of canning: active and paive. A the name imply, the tation find AP by litening to periodic beacon frame in the paive mode. The tation uing active canning broadcat a probe requet frame on each channel and may receive multiple probe repone frame from different AP working on the ame channel. Figure how the frame exchange equence in the active canning. Typically for mot exiting WLAN device, active canning i ued by default for aociation. The criterion that pecifie which channel hould be canned i addreed in the tandard []. MAC layer management entity called MLME (MAC ub-layer Management Entity), initiate canning channel upon receiving the correponding requet with a lit of channel to be canned, ChannelLit, from SME (Station Management Entity). Practically, the functional detail of SME can be implemented a a form of application oftware that control the aociation of WLAN client, including the ChannelLit. Baed on the belief that the highet-ignal-trength AP would provide the bet performance, mot commercial device rely on the conventional aociation metric, RSSI. Although the 82. DCF (Ditributed Coordination Function) i deigned to offer long-term equal medium acce opportunitie to all contending tation, RSSI-baed AP election might not provide the deired performance of the tation. The AP election hould conider load balancing and fairne among tation a well a throughput. After canning, the tation attempt to get authenticated

3 Buy medium DIFS Backoff O c PHY RTS SIFS SIFS DATA SIFS O a PHY CTS PHY U PHY ACK Figure 2: IEEE 82. DCF channel acce. and aociated with the elected AP by exchanging authentication and aociation requet/repone frame a depicted in Fig.. 4. METRIC FORMULATION We firt formulate the aociation metric EVA, tarting with it definition. We then decribe how to decompoe the metric formulation into multiple component that can be eparately calculated and etimated. 4. EVA Definition The deign of EVA aim to etimate available bandwidth on an operating channel, being aware of the contention intenity around the tation. We define available bandwidth a the maximum achievable bandwidth on the target 82. link. We define the concept of EVA along with the frame exchange equence of the 82. DCF. A illutrated in Fig. 2, after backing off, a tation accee the wirele medium by exchanging RTS (Requet-To-Send) and CTS (Clear-To- Send) frame. 2 A data frame tranmiion then follow and the frame exchange complete by the ACK (Acknowledgement) frame tranmiion. Keeping the frame exchange in mind, we define EVA a follow. Definition. We conider two equence of random variable, T i and n i: the former i the time duration pent by the tation in conideration to tranmit a data frame (including both contention delay and frame tranmiion time a illutrated in Fig. 2) at the tation i th tranmiion attempt; and the latter denote the tranmiion reult at the i th attempt, i.e., for ucce and for failure. The ubcript i indexe a et of M(t) tranmiion attempt of the tation by the time t. We define EVA a the frame ize (F) divided by the fraction of thoe random equence: F EVA(t) P M(t) i= Ti/ P, () M(t) i= ni where the denominator repreent the time average of the time pent by the tation in order to uccefully tranmit a data frame over the total oberved time interval, t. EVA(t) formulated by a function of random equence i not ueful ince it accuracy varie over the ize of M(t). Moreover, EVA(t) doe not quickly react to wirele channel variation and contention intenity. The following propoition tate that EVA(t) converge to the non-random (probabilitic) mean. 2 In thi paper, we conider RTS/CTS-enabled DCF MAC in EVA formulation. However, the uage of EVA a an aociation metric i not limited to the RTS/CTS uage, thu being able to change the formulation to be uitable for the acce without RTS/CTS exchange. Notation O phy CW min CW max tframe tdifs tsifs tbo tbuy ttimelot F τ Table : Notation. Definition tranmiion duration for PHY header and preamble the minimum contention window the maximum contention window tranmiion duration for that Frame type time interval of DIFS (DCF Interframe Space) time interval of SIFS (Short Interframe Space) backoff interval time interval holding medium acce due to buy channel unit time interval of a ingle timelot frame ize propagation delay Propoition. Eq. () and (2) are aymptotically equivalent a t : F EVA = E[T ]/E[n], (2) where E[T ] i the expected frame tranmiion time and E[n] i the expected number of uccefully tranmitted data frame at a unit tranmiion attempt. Proof. The random ample (tatitical) mean of the M(t) random variable with repect to T i and n i are defined by: T i = P M(t) i= Ti, n i = M(t) P M(t) i= ni. M(t) By the Law of Large Number, T i E[T ] and n i E[n] a t. In conequence, it follow that EVA = F E[T ]/E[n]. 4.2 Frame Exchange Sequence Decompoition From Propoition, EVA can be obtained by calculating E[T ] and E[n]. In order to obtain E[T ], we decompoe the unit frame exchange equence, a hown in Fig. 2, into three temporal component: O c : channel contention overhead, O a : channel acce overhead, and U : unit tranmiion time required for a data frame tranmiion. Therefore, we can revie Eq. (2) a follow: EVA = F E[n] O c + O a + U, (3) and the problem change to deriving each decompoed component: E[n], O c, O a, and U. Table lit notation of related parameter ued in the following derivation E[n], the expected number of uccefully tranmitted data frame Note that E[n] i equal to P, the probability that a data frame i uccefully tranmitted in a unit frame exchange. P can be preented by: E[n] = P = p rt p data, (4)

4 where p rt and p data denote the ucce probabilitie of RTS/CTS and data/ack exchange, repectively. Note that if we aume no hidden tation that i not carrier-ened by the tranmitter, but interfere frame reception at the deignated receiver, a frame colliion only happen with the imultaneou tranmiion of RTS frame. Accordingly, the colliion probability with repect to the tranmitting tation that etimate EVA can be denoted by p rt c and p data in Eq. (4) are preented a follow: j p rt p data = ` p rt e = ` p data e ` p ct e ` p rt c, ` p ack, e. Therefore, p rt where p Frame e i FER (Frame Error Rate) of the pecific Frame type, uch a RTS, CTS, data, and ACK frame. A the calculation of Eq. (4) and (5) i baed on the knowledge of FER that depend on F and the employed modulation and coding cheme, i.e., tranmiion rate (r), an FER etimation method hould precede. If we have a predetermined FER v. SNR (Signal-to-Noie Ratio) table in advance, the problem become imple. Such a table can be obtained either from meaurement, e.g., [], or from the vendor dataheet, e.g., []. Upon the reception of frame from an AP, the tation meaure the average SNR by meauring both RSSI and the oberved background noie level, and then FER i obtained from the table O c, the channel contention overhead From Fig. 2, O c can be divided into two time interval, tbo and tbuy, where tdifs i included into tbuy, i.e., (5) O c = E[tBO] + E[tBuy]. (6) Let σ denote the channel idle ratio during an O c period. σ i then preented by: σ = E[tBO] E[tBO] + E[tBuy]. (7) By inerting Eq. (6) into Eq. (7), we have O c = E[tBO]/σ and hence, the problem of O c calculation become finding σ and E[tBO]. We will preent a way to etimate σ in the following ection. In the cae of a tranmiion failure, the backoff procedure update CW (Contention Window) to [2 (CW + ) ]. Once CW reache CW max, it remain at thi value until finihing the tranmiion uccefully or dropping the frame due to the retry limit, reetting to CW min. The backoff interval of the i th tranmiion attempt can be denoted by tbo i = rand[,cw i], where CW i i the ize of contention window at the i th tranmiion and i written by: i CW i = min h2 i (CW min + ),CW max. (8) rand[x, y] i the operator that randomly draw an integer number from a uniform ditribution over the interval [x, y]. Accordingly, we can ay that tbo i CW i/2 on average. The average backoff interval per unit tranmiion, E[tBO] i derived a follow: E[tBO] = γx i= (i) CWi 2 ttimelot, (9) where γ i the retry limit and (i) i the probability that a data frame i uccefully tranmitted after the i th tranmiion attempt, being preented by: (i) = ( P ) i P. () for p c rt for buy idle 2. 3 DIFS Oberved timelot Figure 3: An illutrative example of channel obervation for p rt c, σ etimation. Accordingly, having the etimate of p rt c play a key role in knowing E[tBO], which i imilar to the cae of E[n] O a and U A illutrated in Fig. 2, O a and U can be written a: j Oa = 2O phy + trts + tsifs + tcts + 2τ, U = 2O phy + tdata + 2tSIFS + tack + 2τ. () DIFS 5. ESTIMATION ALGORITHM We preent how to etimate colliion probability, p rt c and channel idle ratio, σ with which we can calculate EVA a decribed in Section 4. We firt preent an illutrative example and then propoe the EVA etimator. 5. An Example Figure 3 depict that a tation i oberving the wirele channel to etimate p rt c and σ. The bold horizontal line repreent the channel tate: upper and lower level mean buy and idle tate of the channel, repectively. Small rectangle are timelot oberved by the tation. Upper timelot enumerated up to 9 i ued for p rt c etimation. Suppoe that the channel tate change up and down due to the frame tranmiion from other tation, and the oberving tation, ay A, aee that the channel become buy three time (at the 2 nd, 5 th, and 8 th timelot). Note that a colliion would happen, if A tried to tranmit a frame at thee timelot from which the channel become buy. Likewie, A can etimate it colliion probability by counting uch colliion-induced timelot out of all elaped timelot. In thi example, the etimated p rt c when the 9 th timelot expire become. 3 Similarly, we can alo etimate σ uing lower indexed timelot a follow. A count all idle timelot out of the total oberved lot. In thi example, the total number of idle timelot i 5 out of the total elaped lot, i.e., 5. A a reult, σ become 6 when the latet timelot expire. Note 5 that the etimation of p rt c and σ can be done concurrently. 5.2 EVA Etimator In order to etimate p rt c and σ, we employ the ARMA (Autoregreive Moving Average) etimator [2]. The typical ARMA etimator ha the following form: ŷ i = αŷ i + α K K X j= DIFS x i j, (2) where ŷ i i the target etimate and x i j, with j =,, K are the lat K timelot ample. K and α are deign parameter that determine the accuracy of the etimator.

5 K play a role to mooth meaurement that are fed to the weighted average; however, a revealed in [3], the election of K value ha little impact on the performance of ARMA etimator, and i ued for K in our etting. On the other hand, the filter memory (or autoregreive weighting factor) α work a a tuner controlling the tradeoff between the etimation accuracy and the repone time. In Section 6, we tudy the impact of α on the performance tradeoff and chooe a uitable α baed on which tation can elect the bet AP. At each timelot, a tation aee the channel tate whether it become buy or idle. For p rt c etimation, x i, which i a ample of colliion event, i et to if the channel tate change from idle to buy in the i th timelot, while x i i for every idle timelot. When etimating σ, x i mean a ample of an idle timelot. Therefore, for every idle timelot expiration, x i become ; otherwie, x i i. Note that for both p rt c and σ etimation, timelot during which the target tation tranmit it frame are not conidered, ince the tation only oberve per-timelot channel occupancy taken by other tation, not itelf. A tation, ay A, which trie to find the bet AP to achieve the highet throughput uing the EVA metric, run the following algorithm: Algorithm. EVA etimator. For all ttimelot, A aee the channel tate and determine an etimation ample, x i. 2. A run Eq. (2) with every valid ample, x i, and update the new etimate, ŷ i, i.e., ˆp rt c or ˆσ. 3. Going through Eq. (5), (4), (), and (9) with the etimated ˆp rt c, A obtain E[tBO]. 4. A inert E[tBO] and the etimated ˆσ into Eq. (7) and (6) to get E[tBuy] and O c. 5. EVA i etimated uing Eq. (3). 5.3 Implementation Iue Finding a practical etimation interval for accurate available bandwidth etimation i important. Too hort of an etimation interval would reult in an inaccurate EVA etimation. A longer etimation interval would yield an enhanced accuracy on the other hand, but might make the etimation uele for the aociation proce becaue of the long, unaffordable etimation interval. According to [4, 5], the typical time pent on canning the 82.b channel varie from 3 to 5 milliecond depending on WLAN client device. Since three to four different channel are ued in the 82.b band, i.e., 2.4 GHz, we can argue that approximately milliecond i ued to can a ingle channel. A addreed previouly, the only phyically required operation for a tation to etimate an EVA value i to oberve the change of wirele medium from buy to idle, or vice vera. Moreover, the channel obervation might not incur much overhead and hould be done along with the normal 82. operation. We will tudy how accurately the EVA etimator can calculate the available bandwidth within a pragmatic time interval, i.e., milliecond per each 82.b channel in Section 6. We will alo invetigate the appropriate α value in order to achieve an accurate EVA etimation within that time interval. 6. PERFORMANCE EVALUATION We preent our imulation tudy to evaluate the effectivene of the propoed EVA aociation metric. We firt decribe the imulation environment and identify the performance tradeoff between accuracy and reponivene of the EVA etimator. Finally, we evaluate the throughput performance baed on the propoed aociation metric, EVA. 6. Simulation Setup We enhance the 82. DCF module in the n-2 imulator [6] to upport the propoed EVA-baed aociation. The 82.b i conidered a the PHY module [7], and the highet tranmiion rate, i.e., Mbp, i employed by all tation. Each tation tranmit 28-byte frame with 2 dbm power and all tation are tatic. We ue the empirical BER (Bit Error Rate) v. SNR curve provided by Interil 3 to etimate the FER (Frame Error Rate) []. The background noie level i et to 96 dbm. We ue a log-ditance path-lo model with the path-lo exponent of four to imulate the indoor office environment [8]. A addreed in Section 5, we et the number K of ample for moving average proce to, and vary α, the weighting factor for autoregreive proce from.99 to.9999 to tudy it impact on the etimation accuracy and reponivene tradeoff in the ARMA etimator. 6.2 Accuracy and Reponivene In order to evaluate the accuracy of the EVA etimator, we compare the etimation reult with thoe of imulation and analytic model. We run the etimator by varying the filter memory of α (.99 α.9999), while oberving the performance tradeoff. The compared imulation reult i obtained baed on the etting that a tation in conideration i aociated and communicate with an AP in an infinite backlogged condition. For the analytical reult, we employ the Markov chain-baed analyi model propoed in [9]. We conider two different cenario: () when a tation that etimate EVA trie to aociate with an AP; (2) and when a tation trie to change it aociation to another AP to achieve a better throughput performance, i.e., the tation i already aociated with an AP. The correponding reult are hown in Fig. 4 and 5, repectively. In both cenario, the number of tation i et to including the tation of our interet, and each tation i aociated with a ingle AP. Note that only the firt cenario repreent the cae for the initial aociation attempt of a tation. A hown in both Fig. 4 and 5, the target tation oberve the channel for ten econd and etimate p rt c and EVA. Both cae how the imilar trend in the convergence and fluctuation tradeoff when α varie. Larger α how le etimation fluctuation, but take longer to converge. 3 The BER curve in [] were meaured in an AWGN (Additive White Gauian Noie) environment. Table 2: RMS error of the etimated value. Not aociated Aociated α p rt c EVA p rt c EVA

6 Colliion probability Etimation: α =.99 Etimation: α =.999 Etimation: α =.9999 Simulation Analyi Available bandwidth (Mbp) Etimation: α =.99 Etimation: α =.999 Etimation: α =.9999 Simulation Analyi Simulation time (ec) (a) Colliion probability (p rt c ) Simulation time (ec) (b) Etimated available bandwidth (EVA). Figure 4: Comparion of etimated reult with the imulated and analyzed when not aociated. Colliion probability Etimation: α =.99 Etimation: α =.999 Etimation: α =.9999 Simulation Analyi Available bandwidth (Mbp) Etimation: α =.99 Etimation: α =.999 Etimation: α =.9999 Simulation Analyi Simulation time (ec) (a) Colliion probability (p rt c ) Simulation time (ec) (b) Etimated available bandwidth (EVA). Figure 5: Comparion of etimated reult with the imulated and analyzed when aociated. Per-tation throughput (Mbp) EVA RSSI Number of tation Standard deviation (Mbp) Number of tation EVA RSSI CDF EVA RSSI Throughput (Mbp) (a) Average per-tation throughput. (b) Standard deviation of achieved throughput among contending tation. (c) Cumulative fraction of achieved throughput of each contending tation when the number of tation i. Figure 6: Performance comparion of EVA and RSSI-baed aociation procee.

7 AP AP 2 3m 2m tation 2m Figure 7: The topology of imulation. m To further invetigate the effect of α, we calculate RMS (Root-Mean-Square) error of all etimate. We collect random ample of p rt c and EVA at milliecond, and derive enemble average to find which α value yield the mallet etimation error for both etimate. Table 2 how the RMS error calculated for all α value. For all α value and whether aociated or not, we oberve that α =.999 how the minimum RMS error. Therefore, we fix the value of α and the etimation interval to.999 and milliecond, repectively, for the following imulation environment. 6.3 AP Selection by EVA We now evaluate the effectivene of the propoed EVA aociation metric by comparing it with the legacy cheme, RSSI-baed aociation. The conidered topology i hown in Fig. 7, where two AP and multiple tation are deployed. Two AP (denoted a AP and AP 2) are eparated by 3 m from each other, and tation are randomly located in the area of 2 2 m 2, which i hifted toward AP 2 a hown in Fig. 7. In thi topology, we expect that more tation aociate with AP 2 than AP if the RSSI metric i employed. Meanwhile, tation working with EVA might aociate with AP in pite of lower RSSI value, ince each tation etimate it maximum achievable throughput and elect the highet-throughput AP. Different frequency channel are aigned to each AP thu having no inter-channel interference. The carrier ene range i et to 8 m, o that all tation and AP can ee frame tranmiion from other in thi topology. The offered load of each tation i et to.4 Mbp during imulation run. Figure 6(a) how the per-tation throughput of both EVA and RSSI-baed aociation. All reult are averaged over run. We oberve that the propoed EVA-baed aociation how about 6.2 % enhanced throughput gain over the legacy aociation. We alo how the tandard deviation of the achieved throughput of all contending tation in Fig. 6(b). While EVA-baed aociation preent maller tandard deviation (i.e., at mot.7 Mbp when the number of tation i 9), the legacy cheme how larger tandard deviation for all the cae, which mean that the achieved throughput among tation i often highly unbalanced. In the cae of contending tation, we preent the cumulative fraction of the achieved throughput of each contending tation in Fig. 6(c). EVA-baed aociation (the olid line) how teeper lope of curve, meaning that mot tation achieve throughput evenly. We alo count the number of aociated tation with different AP. With RSSI, 3.25 tation (out of ) aociate with AP on average, while the number of tation that are aociated with AP 2 i On the other hand, tation with the EVA etimator are more evenly ditributed between two AP: 4.25 tation with AP and 5.75 with AP 2. A the reult indicate, we can achieve better balanced throughput hare a well a higher individual/aggregate throughput performance with the propoed EVA-baed aociation. 7. CONCLUSION We preented a new aociation metric called EVA (Etimated available bandwidth) for IEEE 82. tation. EVA i deigned to enhance the throughput performance of an individual tation by etimating the available bandwidth provided by multiple AP and electing the bet one to aociate with. We firt howed the accuracy of the propoed etimation method by comparing our etimation of colliion probability and available bandwidth with the imulation and analytic model. We compared EVA-baed aociation with the legacy cheme in term of individual and aggregate throughput performance. We howed that EVA-baed aociation increae the per-tation throughput, balance the load of the AP, and conequently, enhance the aggregate network throughput. Moreover, the EVA etimator doe not require any extra probing overhead. A future work, we plan to extend the formulation of EVA to incorporate the impact of hidden tation into the available bandwidth etimation. Moreover, we plan to apply the uage of EVA to the handoff deciion criterion o a to enhance the throughput performance of mobile tation. 8. REFERENCES [] IEEE , Part : Wirele LAN Medium Acce Control (MAC) and Phyical Layer (PHY) pecification, IEEE Std., Aug [2] A. Balachandran, P. Bahl, and G. M. Voelker, Hot-Spot Congetion Relief in Public-area Wirele Network, in Proc. IEEE MCSA 2, Callicoon, NY, USA, June 22, pp [3] Y. Bejerano, S.-J. Han, and L. Li, Fairne and Load Balancing in Wirele LAN Uing Aociation Control, in Proc. ACM MobiCom 4, Philadelphia, PA, USA, Sept. 24, pp [4] T. Koraki, O. Ercetin, S. Krihnamurthy, L. Taiula, and S. Tripathi, Link Quality baed Aociation Mechanim in IEEE 82.h Compliant Wirele LAN, in Proc. RAWNET 6, Boton, MA, Apr. 26, pp [5] O. Ekici and A. Yongacoglu, A Novel Aociation Algorithm for Congetion Relief in IEEE 82. WLAN, in Proc. ACM IWCMC 6, Vancouver, Britih Columbia, Canada, July 26, pp [6] G. Athanaiou, T. Koraki, O. Ercetin, and L. Taiula, Dynamic Cro-Layer Aociation in 82.-baed Meh Network, in Proc. IEEE INFOCOM 7, Anchorage, AK, USA, May 27, pp

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