Voice Capacity Evaluation of IEEE a with Automatic Rate Selection

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1 Voice Capacity Evaluation of IEEE 80.11a with Automatic Rate Selection Nattavut Smavatkul, Ye Chen, Steve Emeott Motorola Labs, 1301 E. Algonquin Rd., Schaumburg, IL (Natt, Ye.Chen, Abstract his paper analyzes the transmission of and isochronous traffic in general over an 80.11a wireless local area network. In particular, we provide a simple analytic technique for estimating the capacity of an 80.11a access point under the contention-based access method when all stations can select an optimal transmission rate using automatic rate selection. Capacity is additionally estimated using a wireless LAN (WLAN) system simulator that models contention-based access in a noise limited channel, in which packet erasures are modeled using quasi-static link simulation techniques. he impact of automatic rate selection on capacity is evaluated. Results from the analytic and simulation based methods of estimating capacity are compared. In addition, impact on capacity from data traffic is studied. I. INRODUCION Wireless LAN (WLAN) has experienced spectacular growth in recent years due to the popularity of nomadic computing and the inherent advantages of wireless connectivity, such as rapid deployment and mobility. In many WLAN technologies, information can be transmitted at various rates. Stations compliant with IEEE 80.11a [1], for example, can transmit information at eight different rates, ranging from 6 to 54 Mbps. While the principal application of WLAN systems has been providing connectivity to application such as and web browsing, there has been a growing interest in supporting isochronous services with automatic rate selection. Current WLAN technologies [][3] provide two main approaches for delivering isochronous traffic, either through a prioritized contention-based technique or a polling-driven technique. Under the polling-driven scheme, channel access is regulated via an explicit polling message issued by a central controller. he real-time traffic capacity and performance using polling has been studied in several papers [4][5]. Another approach, the prioritized contention-based scheme, employs a free-for-all approach called carrier sensing multiple access with collision avoidance (CSMA/CA), which lets station contend for channel access. he capacity of this scheme has also been studied in several papers [6][7]. However, neither set of papers considers the impact of multi-rate operation. In this paper, we analyze the performance of 80.11a WLAN access point (AP) under contention-based scheme in term of the maximum number of conversations that can be supported in a noise-limited channel. In particular, we provide a simple analytic technique that allows us to estimate the impact of automatic rate selection on capacity. Although we focus on a typical office environment, this technique can also be applied to other types of environments. his paper is organized as follows: first, we review 80.11a medium access control (MAC) protocol and physical layer, and describe how these protocols effect the WLAN capacity in Section II and III. he traffic model and its capacity criteria are described in Section IV. In Section V, the analytic models to estimate WLAN capacity with automatic rate selection are presented. he quasi-static concept that separates system simulator from link simulator is addressed in Sections VI. he system simulator is introduced in Section VII. he simulation results are presented in Section VIII. Finally, Section IX concludes the paper. II. CONENION-BASED MEDIA ACCESS CONROL A typical frame exchange sequence using contention-based access is shown in Fig. 1. First, a station with traffic senses channel for a certain period of time. If the channel is idle, it acquires the channel and sends data frame. Otherwise, it starts to backoff. If the receiving station correctly receives this data frame, it is required to respond with an acknowledgement () frame within a period defined as short interframe space (SIFS). Following a successful frame exchange sequence, a station is required to wait for duration, defined as distributed interframe space (DIFS), prior to beginning its backoff procedure. During the backoff period, each station waits for a random amount of time, which is uniformly distributed between zero and a contention window (CW) value. A portion of the channel capacity is unusable from collision when more than one WLAN user tries to transmit at the same time. When the sending SA does not receive an frame within SIFS interval, it assumes that the transmission has been lost and invokes a random backoff procedure prior to retransmitting the packet. Access point SA i SA j Data to SA i DIFS SIFS Backoff window Data to AP SIFS Figure 1. Basic frame exchange sequence of contention-based access.

2 he on-going 80.11e standard [3] additionally specifies a prioritized contention-based access method that provides traffic class differentiation. III A PHYSICAL LAYER 80.11a physical layer is based on orthogonal frequency division multiplexing (OFDM) technology as specified in 80.11a standards [1]. It operates in a 5GHz unlicensed frequency band in both Europe and the US. o study the effect of noise and automatic rate selection in the system, a realistic link simulator to model 80.11a physical layer is needed. By adopting a quasi-static simulation technique presented in Section VI, the simulator is separated into a link simulator and a system simulator. A multi-purpose OFDM link simulator [8] is used to generate a statistical model that describes the relationship among received signal power, packet error rate, packet size, and modulation & coding. his information will then be used by the system simulator to look up the packet error rate using the remaining three factors, viz., received signal strength, packet size, and modulation & coding. During this phase of study, only four different data rates, i.e., 6, 1, 18, and 36 Mbps, are considered. A. Automatic Rate Selection An idealized procedure for automatically selecting transmit rate is used, based on link curves from the link simulator. It is assumed that the path loss information between transmitter and receiver is known to the transmitter prior to selecting a transmit rate. he transmitter can then select the appropriate rate based on path loss information and link curves. A packet error rate before retransmission (PER) of more than 5% is established as a criterion to reduce the transmit rate. Given a current propagation path between transmitter and receiver, the chosen physical transmission rate is the highest rate that can still satisfy the maximum 5% PER criterion. A sample of the operating ranges of each physical transmission rates are depicted in Fig.. he maximum cell radius is chosen such that the PER will equal to 10% at the cell boundary for the lowest physical transmission rate, 6 Mbps. For an indoor office environment, this cell radius equals to 6 meters. LinkSpeed(Mbps) Range (meters) Figure a Link Speed vs. Range IV. ELEPHONY VOICE RAFFIC MODEL he traffic model represents a speech signal using a periodic pulse train, where the periodicity of the pulse train depends upon the coder frame size. We assume that all coders have a 0 milli-second frame size. he process of encapsulating frames into IP packets is commonly referred to as packetization. he size of each packet depends on the rate of the coder, which commonly ranges from 4 Kbps to 64 Kbps. Each conversation is bi-directional, resulting in two s in opposite directions for a single conversation. Both uplink and downlink s are assumed to be independent from one another with the start times randomly distributed over 0 ms period. he lifetime of a packet is also assumed to be 0 ms. V. VOICE CAPACIY MODEL In the absence of hidden terminal, noise, and interference, it is possible to analytically estimate the maximum number of telephony conversations that an 80.11a WLAN AP can support with contention-based access. A. Overview elephony traffic is sensitive to both delay and packet loss. In order to maintain an acceptable quality, we assume that the packet loss rate must be less than or equal to 1% and over 99% of packets experience less than 0 ms delay (e.g. equal to the frame duration). he WLAN system capacity is then defined as the maximum number of full duplex calls that a given AP can support while still maintaining these criteria. he basic frame exchange of a telephony consists of a data frame carrying payload followed by an frame, similar to Fig. 1. he analytic model will estimate a maximum number of s that can be accommodate during a 0 ms period (a single frame) of an IEEE 80.11a AP with multi-rate support under the contention-based access method. he analytical model consists of the contention module and the automatic rate selection module. he contention module considers the effect of contention and collision among users in accessing the medium, whereas the automatic rate selection module considers the effect of switching between multiple transmit rates. It is also important to note that the analytical model is based on two simplifying assumptions: ideal availability of channel state information and an errorfree channel. B. Contention-based Access Under the contention-based access method, each call is treated as a constant bit rate (CBR) connection with both uplink and downlink uni-directional s. he time consumed by a sequence,, is shown in (1). = DIFS + _ + SIFS + (1) and Voice _ PDU Voice PDU are the time to transmit data and frames, respectively. In addition to, the impact from the contention scheme is considered in two phases, backoff phase and collision phase. Both phases are modeled analytically based on a DCF model derived by Bianchi [10].

3 he backoff duration ( backoff ) represents the average backoff interval for a successful packet transmission. It can be calculated as shown in (), where n is the number of backlogged stations and τ is the probability that a station transmits in a randomly chosen slot time. backoff = slot 1 τ () n τ slot equals to a duration of one backoff slot. In a stable system (less than 1% packet loss criteria) with CW min value of 15 slots, the sustained maximum number of backlogged stations at any given time must be less than 15/e = 5., based on a slotted aloha throughput. In our analytical model, n is chosen as 3. he number of backlogged station will be studied further in the simulation results section. τ can be determined by solving (3) and (4) [10], where m is the maximum number of backoff stages. τ (3) = m i 1 CWmin p CWmin ( p) i= 0 n 1 p = 1 (1 τ ) (4) In addition to backoff overhead, a portion of the channel capacity is also unusable from collisions. he average unusable time due to collision per single successful packet transmission ( collision ) is shown in (5) [10]. propagation is the maximum propagation time for the WLAN cell under consideration. collision n 1 (1 τ ) ( ) (5) = Voice _ PDU + DIFS + propagation 1 n 1 n τ (1 τ ) By adding the overheads related to backoff and collision, the amount of time associated with a single (V k ) during a 0 ms period can be obtained by (6). K will be defined in the next subsection. ς κ = + backoff + collision, κ= 1,..., Κ (6) C. Statistical Models of Automatic Rate Selection he central limit theorem is used to determine the effect of automatically selecting a physical transmission rate based on the channel condition. Let s assume that there are N active uni-directional s at any given time in the system. Because a full-duplex call consists of two s, the total number of calls N user that the system can support equals to (N /). Given that each active carries out one frame exchange sequence within a period equal to the frame duration of 0 ms, there are N frame exchange sequences per 0 ms. Let a random variable represents the amount of time consumed by the aggregated calls during the same duration, equals to the summation of time consumed by each call ( i ). Because each user is statistically identical, mean and variance of i are identical for all values of i (i=1,,n ). Since each call is independent from one another, according to the central limit theorem, the mean η and variance σ of random variable equal to the sum of means and variances of random variables i, which equals to N η and i N σ, respectively. Under an i assumption that i is not deterministic (σ 0), the cumulative distribution function (CDF) F( ) of the random variable approaches a normal distribution with the mean η and variance σ as shown in (7). F ( ) = Norm ( η σ Assuming that there are K possible durations of a frame exchange sequence, denoted by V k, where k ranges from 1 to K. If there are N link possible physical transmission rates, there are N link possible values of V k (K = N link ). he probability that a station will uses any given V k duration for its frame exchange sequence is represented by P k, which equals to the probability that a given physical transmission rate is chosen (P k = Prob [k-th link is used]). It is straightforward to derive the mean η i and variance σ i of i, based on the discrete random variable rules. he mean and variance of can then be derived from η i and σ i. he resulting CDF of is shown in (8). F ( ) = Norm ( N σ N i ) η i ) (8) D. Voice Capacity Given the capacity criteria, in which, with 0.99 probability, the time consumed by aggregated calls remains below the system capacity, the maximum capacity of a WLAN AP can be estimated using (9). P 1 frame i [ > frame ] = erfc ( ) Pclipping N N σ i (7) η (9) frame represents the maximum channel capacity available for transmission during a given frame duration, which is 0 ms. he clipping probability or packet loss ratio, P clipping, is limited to be under he maximum number of s can be determined by solving (9) for N. he maximum number of full-duplex calls N user that the system can support for each vocoder rate is equal to N /. Using this analytical model, the capacity of an 80.11a WLAN can be estimated. A set of basic channel access parameters for the contention-based access method can be obtained from the 80.11a standard [1]. he amount of time required by each,, during each 0 ms period depends on both the vocoder rates and link speeds. he probability of each link speed is calculated based on the

4 80.11a range Vs. link speed in Fig., assuming that mobiles are uniformly distributed in a circular region. VI. QUASI-SAIC SIMULAION CONCEP WLAN capacity is additionally estimated using a system simulator that models contention-based access and a noise limited channel. he simulation results are used to validate the theoretical estimates. We use a quasi-static simulation technique, which enables a separation between link simulator and system simulator. At the system level, it is assumed that the link quality remains the same over the duration of each packet transmission. It is also assumed that errors are uncorrelated, i.e., error in prior packet does not influence the probability of error in subsequent packet. Furthermore, it is assumed that the location or distribution of error bits does not have any impact to the packet error rate, which is generally the case with a good interleaver design. Finally, it is also assumed that the CRC never fails. Based on these assumptions, the entire channel state information can be represented by a single SNR value, which remains constant over the packet duration. his SNR value can be obtained from an appropriate pathloss and propagation models. Figure 3. Summary of 80.11a WLAN capacity B. Delay For a delay-sensitive traffic such as, maintaining low and predictable delay is crucial in supporting real time traffic. he CDF for channel access delay is shown in Fig. 4 for 64 Kbps and 16 Kbps vocoder rates. VII. SYSEM SIMULAOR he system simulator is implemented in OPNE Modeler, based on the 80.11a MAC and physical layers. It also employs a statistical model that predicts PER from SNR, packet size, and modulation & coding, which is generated using the link simulator. VIII. SIMULAION RESULS In this section, the simulation results are presented, both to validate the analytic model and to provide insight into other performance metrics. Several vocoder rates, from 4 Kbps to 64 Kbps, and the capacity gain from automatic rate selection are investigated. A. Voice Capacity In Fig. 3, the capacity numbers for an 80.11a AP with contention-based access method are depicted for different combinations of vocoder rates and physical transmission rates. he simulation results are compared with the theoretical estimations from the previous section. As shown in the figure, automatic rate selection increases the WLAN capacity from the fixed 6 Mbps transmission rate. For example, with a 16 Kbps vocoder, 44 users can be supported with automatic rate selection while only 31 users can be supported without automatic rate selection. Additional users can also be supported by using a lower rate vocoder. he number of users increases from 30 to 50 when the vocoder rate decreases from 64 Kbps to 4 Kbps. In all cases, there is only a small difference between the analytical estimate and the simulation results, which can be attributed, in part, to noise in the system. Figure 4. CDF of medium access delay for selected vocoder rates. he effect of automatic rate selection is also presented. For comparison, all curves are depicted when the system reaches its maximum capacity. Even at capacity, virtually all packets experience less than ms in channel access delay, which is much lower than the 0 ms maximum delay criteria. Other simulation results show that delay increases rapidly when the system exceeds its capacity. A comparison between different vocoder rates reveals that delay is lower with lower vocoder rate. he average delay of 16 Kbps is approximately 0.5 ms smaller than the average delay of 64 Kbps. Because a packet from high rate vocoder is larger than a packet from low rate vocoder, with a 64 Kbps vocoder requires more transmission time. Consequently, other users must wait for a longer period for the current transmission to complete, resulting in a longer channel access delay. Additionally, a larger packet is more susceptible to erasure, which results in additional retransmission delay. he impact from multi-rate transmission can also be seen from the figure. Under the same vocoder rate, smaller channel access delay can be achieved via automatic rate selection. he slightly smaller delay results from the higher data rate used in the multi-rate case. However, the shallower

5 CDF curve in multi-rate simulation indicates that there is more delay jitter when automatic rate selection is used. C. Voice Capacity in the Presence of Data Users By appropriately setting up the contention parameters, higher priority can be granted to traffic over data traffic. Nevertheless, the capacity is expected to be slightly lower when data traffic is introduced. From our preliminary simulation results, the traffic is reasonably protected from low priority traffic, resulting in a graceful degradation of capacity. he contention parameter settings and data traffic model are depicted in ABLE 1. From Fig. 5, the capacity decreases by -3 users with 8 HP and 6 FP users. Figure 5. Voice Capacity in the presence of data traffic. D. Number of Stations in Backoff As previously discussed, the number of backlogged station is one parameter of the analytical model. Fig. 6 shows a relationship between packet loss rate (indication of system stability) and stations in backoff. Higher packet loss rate is achieved by increasing the number of stations. he AP is almost always in backoff, having to transmit downlink traffic to all stations. Consequently, backlogged station starts from one and goes up sharply when the packet loss rate is greater than 5%. Additionally, the number of stations in backoff seems to be insensitive to vocoder rate and link speed. ABLE I. SIMULAION PARAMEERS Contention Parameters Value CWmin/CWmax for /HP/FP 15/103; 31/103; 63/103 IFS for /HP/FP DIFS; DIFS+5; DIFS+9 30 Kbps; HP1.1, 00 Data model for HP/FP Kbps with truncated Pareto object size IX. CONCLUSIONS In this paper, both analytic and simulation techniques for estimating capacity using contention-based access are described for 80.11a WLAN. In particular, methods of modeling multi-rate links are described. Five different vocoder rates are considered in the capacity study. he analytic models assume a finite number of mobile stations, multi-rate links, and ideal channel conditions. Results for multi-rate noise limited channels are obtained from system simulation models. Packet erasures are modeled using path loss equations and quasi-static link modeling techniques. Comparisons between the analytic models and simulation results show that the analytic models provide an accurate estimate of capacity. he capacity improvement due to using ideal automatic rate selection instead of a fixed 6 Mbps link ranges from 35% to 55%, depending on vocoder rate. Additionally, the capacity can also be improved with a decrease in vocoder bit rate. REFERENCES [1] ANSI/IEEE Std 80.11a: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications; High-speed Physical Layer in the 5 GHz Band, 1999 [] ANSI/IEEE Std 80.11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, 1999 [3] IEEE Standard 80.11e, Media Access Control (MAC) Enhancements for Quality of Service (QoS), Draft 4.0, November 00 [4] M. Veeraraghavan, N. Cocker, and. Moors, Support of services in IEEE wireless LANs, Proceedings of the IEEE INFOCOM 001, pp , 001 [5] E. Ziouva and. Antonakopoulos, CBR packetized transmission in IEEE networks, Proceedings of the Sixth IEEE Symposium on Computers and Communications, pp , 001 [6] D-J. Deng and R-S. Chang, A priority scheme for IEEE DCF access method, IEICE ransactions on Communications, E8-B(1), January [7] I. Aad and C. Castelluccia, Differentiation mechanisms for IEEE 80.11, Proceedings of the IEEE INFOCOM 001, Vol. 1, pp , 001 [8] S. Simoens and D. Bartolome, Optimum Performance of Link Adaptation in HIPERLAN/ Networks, IEEE 53rd Vehicular echnology Conference, Vol., pp , Spring 001 [9] A. Doufexi, S. Armour, A. Nix, and D. Bull, A comparison of HIPERLAN/ and IEEE 80.11a, Symposium on Communications and Vehicular echnology, SCV-000, pp. 14 0, 000 [10] G. Bianchi, Performance analysis of the IEEE distributed coordination function, IEEE Journal on Selected Areas in Communications, pp , Vol. 18, no. 3, March 000 Figure 6. Packet Loss Rate Vs. Number of station in backoff. (80.11a)