Voice Capacity Evaluation of IEEE 802.11a with ... - Semantic Scholar

Voice Capacity Evaluation of IEEE 802.11a with Automatic Rate Selection Nattavut Smavatkul, Ye Chen, Steve Emeott Motorola Labs, 1301 E. Algonquin Rd., Schaumburg, IL 60196 Email: (Natt, Ye.Chen, Steve.Emeott)@Motorola.com Abstract— This paper analyzes the transmission of voice and isochronous traffic in general over an 802.11a wireless local area network. In particular, we provide a simple analytic technique for estimating the capacity of an 802.11a 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. The 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 voice capacity from data traffic is studied.

I.

INTRODUCTION

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 802.11a [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 email and web browsing, there has been a growing interest in supporting isochronous services with automatic rate selection. Current WLAN technologies [2][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. The 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. The 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 802.11a WLAN access point (AP) under contention-based scheme in term of the maximum number of voice 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. This paper is organized as follows: first, we review 802.11a medium access control (MAC) protocol and physical layer, and describe how these protocols effect the WLAN capacity in Section II and III. The voice traffic model and its capacity criteria are described in Section IV. In Section V, the analytic models to estimate WLAN voice capacity with automatic rate selection are presented. The quasi-static concept that separates system simulator from link simulator is addressed in Sections VI. The system simulator is introduced in Section VII. The simulation results are presented in Section VIII. Finally, Section IX concludes the paper. II.

CONTENTION-BASED MEDIA ACCESS CONTROL

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 (ACK) 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 STA does not receive an ACK 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

B ackoff w indow

D ata to ST A i

AC K S IFS S IFS

AC K

ST A i

ST A j

D IFS D ata to AP

Figure 1. Basic frame exchange sequence of contention-based access.

The on-going 802.11e standard [3] additionally specifies a prioritized contention-based access method that provides traffic class differentiation. III.

802.11A PHYSICAL LAYER

802.11a physical layer is based on orthogonal frequency division multiplexing (OFDM) technology as specified in 802.11a standards [1]. It operates in a 5GHz unlicensed frequency band in both Europe and the US. To study the effect of noise and automatic rate selection in the system, a realistic link simulator to model 802.11a 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. This 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, 12, 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. The 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. 2. The 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 62 meters. L in kS p eed(M b p s)

4 0

3 0

2 0

1 0

2 0

3 0

4 0

R an g e(m eters)

5 0

6 0

7 0

Figure 2. 802.11a Link Speed vs. Range

IV.

V.

TELEPHONY VOICE TRAFFIC MODEL

The voice traffic model represents a speech signal using a periodic pulse train, where the periodicity of the pulse train

VOICE CAPACITY MODEL

In the absence of hidden terminal, noise, and interference, it is possible to analytically estimate the maximum number of telephony voice conversations that an 802.11a WLAN AP can support with contention-based access. A. Overview Telephony voice traffic is sensitive to both delay and packet loss. In order to maintain an acceptable voice quality, we assume that the packet loss rate must be less than or equal to 1% and over 99% of voice packets experience less than 20 ms delay (e.g. equal to the voice frame duration). The WLAN system voice capacity is then defined as the maximum number of full duplex calls that a given AP can support while still maintaining these criteria. The basic frame exchange of a telephony voice flow consists of a data frame carrying voice payload followed by an ACK frame, similar to Fig. 1. The analytic model will estimate a maximum number of voice flows that can be accommodate during a 20 ms period (a single voice frame) of an IEEE 802.11a AP with multi-rate support under the contention-based access method. The analytical model consists of the contention module and the automatic rate selection module. The 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 voice call is treated as a constant bit rate (CBR) connection with both uplink and downlink uni-directional flows. The time consumed by a voice flow sequence, T flow , is shown in (1). T

1 0

0 0

depends upon the voice coder frame size. We assume that all voice coders have a 20 milli-second frame size. The process of encapsulating voice frames into IP packets is commonly referred to as packetization. The size of each packet depends on the rate of the voice coder, which commonly ranges from 4 Kbps to 64 Kbps. Each conversation is bi-directional, resulting in two voice flows in opposite directions for a single conversation. Both uplink and downlink voice flows are assumed to be independent from one another with the start times randomly distributed over 20 ms period. The lifetime of a voice packet is also assumed to be 20 ms.

flow

= DIFS

+ T Voice

_ PDU

+ SIFS + T ACK

(1)

TVoice _ PDU and T ACK are the time to transmit data and ACK frames, respectively. In addition to

T flow , 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].

The backoff duration ( Tbackoff ) represents the average backoff interval for a successful packet transmission. It can be calculated as shown in (2), where n is the number of backlogged stations and τ is the probability that a station transmits in a randomly chosen slot time. T backoff

1−τ  = T slot    n ⋅τ 

(2)

Tslot equals to a duration of one backoff slot. In a stable system (less than 1% packet loss criteria) with CWmin value of 15 slots, the sustained maximum number of backlogged stations at any given time must be less than 15/e = 5.2, based on a slotted aloha throughput. In our analytical model, n is chosen as 3. The 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=

2 1 + CWmin + p ⋅ CWmin ∑i =0 (2 p) i m −1

p = 1 − (1 − τ ) n −1

(3) (4)

In addition to backoff overhead, a portion of the channel capacity is also unusable from collisions. The average unusable time due to collision per single successful packet transmission (Tcollision) is shown in (5) [10]. Tpropagation is the maximum propagation time for the WLAN cell under consideration.  (5)  1 − (1 − τ ) n Tcollision = (TVoice _ PDU + DIFS + T propagatio n ) ⋅  − 1 n −1   n ⋅ τ ⋅ (1 − τ )

By adding the overheads related to backoff and collision, the amount of time associated with a single voice flow (Vk) during a 20 ms period can be obtained by (6). K will be defined in the next subsection.

ςκ = T flow + T backoff + T collision ,

κ= 1, . . ., Κ

(6)

C. Statistical Models of Automatic Rate Selection The 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 Nflow active uni-directional voice flows at any given time in the system. Because a full-duplex voice call consists of two flows, the total number of voice calls Nuser that the system can support equals to (Nflow/2). Given that each active voice flow carries out one frame exchange sequence within a period equal to the voice frame duration of 20 ms, there are Nflow frame exchange sequences per 20 ms. Let a random variable Tvoice represents the amount of time consumed by the aggregated voice calls during the same duration, Tvoice equals to the summation of time consumed by each voice call (Ti).

Because each voice user is statistically identical, mean and variance of Ti are identical for all values of i (i=1,…,Nflow). Since each voice call is independent from one another, according to the central limit theorem, the mean ηvoice and variance σvoice2 of random variable Tvoice equal to the sum of means and variances of random variables Ti, which equals to and N flow ⋅ σ T2 , respectively. Under an N flow ⋅ η T assumption that Ti is not deterministic (σ ≠ 0), the cumulative distribution function (CDF) F(Tvoice) of the random variable Tvoice approaches a normal distribution with the mean ηvoice and variance σvoice2 as shown in (7). i

i

F ( T voice ) = Norm (

T voice − η voice

σ

(7)

)

voice

Assuming that there are K possible durations of a voice frame exchange sequence, denoted by Vk, where k ranges from 1 to K. If there are Nlink possible physical transmission rates, there are Nlink possible values of Vk (K = Nlink). The probability that a station will uses any given Vk duration for its voice frame exchange sequence is represented by Pk, which equals to the probability that a given physical transmission rate is chosen (Pk = Prob [k-th link is used]). It is straightforward to derive the mean

ηT

i

and variance

σ T2

i

of Ti, based on the discrete

random variable rules. The mean and variance of Tvoice can then be derived from

ηT

i

and

σ T2 . i

The resulting CDF of

Tvoice is shown in (8). F (Tvoice ) = Norm (

Tvoice − N flow ⋅ η Ti

σ T ⋅ N flow

)

(8)

i

D. Voice Capacity Given the voice capacity criteria, in which, with 0.99 probability, the time consumed by aggregated voice calls remains below the system capacity, the maximum voice capacity of a WLAN AP can be estimated using (9).

[

P T voice > T frame

]=

T frame − N flow ⋅ η T i 1 erfc ( ) ≤ Pclipping 2 2 ⋅ N flow ⋅ σ T i

(9)

Tframe represents the maximum channel capacity available for voice transmission during a given voice frame duration, which is 20 ms. The clipping probability or packet loss ratio, Pclipping, is limited to be under 0.01. The maximum number of voice flows can be determined by solving (9) for Nflow. The maximum number of full-duplex voice calls Nuser that the system can support for each vocoder rate is equal to Nflow/2. Using this analytical model, the voice capacity of an 802.11a WLAN can be estimated. A set of basic channel access parameters for the contention-based access method can be obtained from the 802.11a standard [1]. The amount of time required by each voice flow, Tflow, during each 20 ms period depends on both the vocoder rates and link speeds. The probability of each link speed is calculated based on the

802.11a range Vs. link speed in Fig. 2, assuming that mobiles are uniformly distributed in a circular region. VI.

QUASI-STATIC SIMULATION CONCEPT

WLAN capacity is additionally estimated using a system simulator that models contention-based access and a noise limited channel. The 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. This SNR value can be obtained from an appropriate pathloss and propagation models.

Figure 3. Summary of 802.11a WLAN voice capacity

B. Delay For a delay-sensitive traffic such as voice, maintaining low and predictable delay is crucial in supporting real time traffic. The CDF for channel access delay is shown in Fig. 4 for 64 Kbps and 16 Kbps vocoder rates.

VII. SYSTEM SIMULATOR The system simulator is implemented in OPNET® Modeler, based on the 802.11a 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. SIMULATION RESULTS 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 voice capacity numbers for an 802.11a AP with contention-based access method are depicted for different combinations of vocoder rates and physical transmission rates. The simulation results are compared with the theoretical estimations from the previous section. As shown in the figure, automatic rate selection increases the WLAN voice 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 voice users can also be supported by using a lower rate vocoder. The number of voice 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.

The effect of automatic rate selection is also presented. For comparison, all curves are depicted when the system reaches its maximum voice capacity. Even at capacity, virtually all packets experience less than 2 ms in channel access delay, which is much lower than the 20 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. The average delay of 16 Kbps is approximately 0.5 ms smaller than the average delay of 64 Kbps. Because a voice packet from high rate vocoder is larger than a voice packet from low rate vocoder, voice with a 64 Kbps vocoder requires more transmission time. Consequently, other voice users must wait for a longer period for the current voice 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. The 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. The slightly smaller delay results from the higher data rate used in the multi-rate case. However, the shallower

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 voice traffic over data traffic. Nevertheless, the voice capacity is expected to be slightly lower when data traffic is introduced. From our preliminary simulation results, the voice traffic is reasonably protected from low priority traffic, resulting in a graceful degradation of voice capacity. The contention parameter settings and data traffic model are depicted in TABLE 1. From Fig. 5, the voice capacity decreases by 2-3 users with 8 HTTP and 6 FTP users.

TABLE I.

SIMULATION PARAMETERS

Contention Parameters CWmin/CWmax for voice/HTTP/FTP IFS for voice/HTTP/FTP

Data model for HTTP/FTP

IX.

Value 15/1023; 31/1023; 63/1023 DIFS; DIFS+5; DIFS+9 320 Kbps; HTTP1.1, 200 Kbps with truncated Pareto object size

CONCLUSIONS

In this paper, both analytic and simulation techniques for estimating voice capacity using contention-based access are described for 802.11a WLAN. In particular, methods of modeling multi-rate links are described. Five different vocoder rates are considered in the voice capacity study. The 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 voice capacity. The 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 voice capacity can also be improved with a decrease in vocoder bit rate. REFERENCES

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 voice stations. The 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 25%. Additionally, the number of stations in backoff seems to be insensitive to vocoder rate and link speed.

Figure 6. Packet Loss Rate Vs. Number of station in backoff. (802.11a)

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