802.11 AMPDU and AMSDU Performance 2007

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Performance Analysis of A-MPDU and A-MSDU

Aggregation in IEEE 802.11n

Boris GinzburgIntel Corporation, Haifa, Israel

Email: [email protected]

Alex KesselmanIntel Corporation, Haifa, Israel

Email: [email protected]

Abstract— With recent improvements in physical layer (PHY)techniques, the achievable capacity for wireless LANs (WLANs)has grown significantly. However, the overhead of IEEE 802.11MAC layer has limited the actual throughput of a WLAN. A-MPDU aggregation suggested in IEEE 802.11n draft is a keyenhancement reducing the protocol timing overheads that enablesaggregation of several MAC-level protocol data units (MPDUs)into a single PHY protocol data unit (PPDU). Another aggrega-tion scheme proposed in IEEE 802.11n is A-MSDU aggregation,which allows several MAC-level service data units (MSDUs) to be

aggregated into a single MPDU. In this work we present a novelanalytic model for estimating the performance of a 802.11n highthroughput wireless link between a station and an Access Point(AP). We consider a 2 × 2 MIMO system and investigate howthe MAC goodput under TCP and UDP traffic is affected by theaggregation size, packet error rate and PHY settings. Our resultsdemonstrate that for UDP traffic, A-MPDU aggregation allowsto achieve a high channel utilization of 95% in the ideal casewhile without aggregation the channel utilization is limited by

just 33%. We also show that A-MPDU aggregation outperformsA-MSDU aggregation, whose performance considerably degradesfor high packet error rates and high PHY rates.

I. INTRODUCTION

With improvements in physical layer (PHY) techniques such

as the orthogonal frequency-division multiplexing (OFDM)modulation technique and multiple-input multiple-output

(MIMO) antenna technology, the achievable capacity for

WLANs has grown significantly. However, the overheads of

media access control (MAC) have limited the actual through-

put. In today’s 802.11 WLANs control frames are transmitted

at a basic rate while the transmission time of physical headers

is fixed. As a result, the 802.11 WLAN efficiency is severely

compromised as the data rate increases since the throughput is

increasingly dominated by these overheads for high data rates.

Therefore, both reducing MAC overheads and pursuing higher

data rates are necessary for high performance WLANs.

In IEEE 802.11e data aggregation is implemented through

controlled frame-bursting (CFB) and the block ACK scheme.Such aggregation schemes benefit from amortizing the control

overhead over multiple data packets. Performance of frame

aggregation schemes is studied in [7], [5]. The works of [2], [8]

derive analytical models of distributed coordination function.

The performance of block ACK schemes is analyzed in [4],

[6], [9]. TCP and UDP performance analysis over a 802.11

WLAN appears in [3], [10].

IEEE 802.11n [1] is a new WLAN standard that provides

both PHY and MAC enhancements to support high data

rates over 100Mbps and up to 600Mbps. The main PHY

technologies of IEEE 802.11n are MIMO and adaptive channel

coding. One key MAC-layer enhancement reducing the pro-

tocol timing overheads is the A-MPDU aggregation scheme,

which enables aggregation of several MAC-level protocol-

data units (MPDUs) into a single PHY-layer protocol data

unit (PPDU). An Aggregated MPDU (A-MPDU) consists of

a number of MPDU delimiters each followed by an MPDU.

Another aggregation scheme proposed in IEEE 802.11n is A-MSDU aggregation, which allows several MAC-level service

data units (MSDUs) to be aggregated into a single MPDU.

In A-MSDU aggregation, multiple payload frames share not

just the same PHY, but also the same MAC header. While A-

MPDU structure can be recovered when one or more MPDU

delimiters are received with errors, an A-MSDU aggregate

fails as a whole even if just one of the enclosed MSDUs

contains bit errors.

Our model. In this work, we focus on the MAC efficiency

improvements in IEEE 802.11n. We present an analytical

framework for estimating the maximum throughput of 802.11n

using A-MPDU and A-MSDU aggregation schemes. To the

best of our knowledge, we are the first to perform analysisthat (i) studies novel A-MPDU and A-MSDU aggregation

techniques, (ii) considers the goodput of MAC not counting

retransmissions and (iii) takes into account collisions of TCP

data packets with TCP ACKs. We consider a 2 × 2 MIMO

system. The maximum throughput is achieved under the best-

case scenario when there is an Access Point (AP) and only

one active station, which always has frames to send. While

aggregation reduces control overhead, the actual benefits de-

pend to a large extent on the channel conditions and MAC

settings. We study how the aggregation size, the packet error

rate and the PHY settings affect the MAC goodput.

Our results. We show that A-MPDU aggregation allows to

achieve a high channel utilization in IEEE 802.11n WLAN. Inparticular, the best-case channel utilization for the mandatory

PHY rate of 130Mbps is 84% under TCP and 95% under UDP

traffic. For the optional PHY rate of 300Mbps, the maximum

channel utilization is slightly worse, that is 78% under TCP

and 91% under UDP traffic. For A-MSDU aggregation, the

corresponding TCP and UDP channel utilization is 51% and

71% for the mandatory PHY rate of 130Mbps and 32% and

53% for the optional PHY rate of 300Mbps. Thus, A-MPDU

aggregation by far outperforms A-MSDU aggregation. We also





Fig. 1. A-MPDU ideal channel utilization for R= 130Mbps.

Fig. 2. A-MPDU ideal channel utilization for R= 300Mbps.

overheads constitute a larger fraction of the channel access

time for higher PHY rates. Observe that without aggregation,

the channel utilization is limited by 18% and 33% under TCP

and UDP traffic, respectively.

B. A-MSDU Aggregation

1) UDP Traffic: Let T frm be the time required to transmit

an A-MSDU of K data frames and receive an ACK: T frm =T phy+T mac+K (T as+T data)+SIFS +T lphy+T ack. We ob-

tain that the ideal goodput under UDP is IdealUDPGdpt =K∗Ldata

DIFS+T bo+T frm.

2) TCP Traffic: Let T fe be the extra time required to

transmit an A-MSDU of K/2 TCP ACKs: T fe = T phy +

T mac +K(T as+T tcp−ack)

2 + SIFS + T lphy + T ack. As in

the previous section, we approximate the TCP throughput

assuming a constant collision probability of 1/CW min. The

overall time required to transmit an A-MSDU of K data

frames and an A-MSDU of K/2 TCP ACKs is 2(DIFS +T bo) + T frm+ T fe . Therefore, the ideal goodput under TCP is

IdealTCPGdpt = K∗Ldata∗(1−c)2(DIFS+T bo)+T frm+T fe

, where 1/(1− c)is the expected number of retransmission attempts before a

successful transmission.

3) Analysis of Results: The channel utilization as a function

of the aggregation size for the mandatory and optional PHY

rates is presented on Figure 3 and Figure 4, respectively.

Observe that the channel utilization grows almost linearly as K increases. The maximum channel utilization for the mandatory

Fig. 3. A-MSDU ideal channel utilization for R = 130Mbps.

Fig. 4. A-MSDU ideal channel utilization for R = 300Mbps.

PHY rate of 130Mbps is 51% under TCP and 71% under UDP

traffic. For the optional PHY rate of 300Mbps, the maximum

channel utilization is much worse, that is 32% under TCP

and 53% under UDP traffic. Note that the performance of A-

MSDU aggregation degrades significantly for high PHY ratessince MAC overheads are fixed while the time required to

transmit payload decreases.

IV. NOISY CHANNEL ANALYSIS

A. A-MPDU Aggregation

We consider Selective Repeat ARQ retransmission scheme.

1) UDP Traffic: For an A-MPDU, let: X be the average

number of new frames; Y be the average number of retrans-

mitted frames and Z be the average span of sequence numbers.

We assume that the positions of corrupt frames are uniformly

distributed over the sending window. The probability that

exactly i first frames are transmitted successfully is (1− p)i∗ p,

where p is the packet error rate. The expected sending windowshift in this case is i∗Z/(X + Y ). Note that X is the expected

sliding window shift after transmitting an A-MPDU. After

performing some calculations, we get

X ≈

È X+Y −1i=1

(1 − p)i ∗ p ∗ i ∗ Z

X + Y + (1− p)

X+Y Z (1)

=

(X + Y − 1)(1 − p)X+Y +1− (X + Y )(1 − p)X+Y + 1 − p

∗ Z

p(X + Y )

+ (1− p)X+Y

Z,



Fig. 5. A-MPDU noisy channel utilization under UDP for R = 130Mbps.

Fig. 6. A-MPDU noisy channel utilization under UDP for R = 300Mbps.

where (1− p)X+Y is the probability that all the frames within

the A-MPDU have been transmitted successfully and Z is

the expected window shift in this case. We can approximate

Z as Z ≈ min(K/(1 − p), W ) since the expected number

of retransmissions of an individual frame is 1/(1 − p) andthe sending window size is an upper bound on the sequence

numbers span within an A-MPDU. We can also approximate

the average number of corrupt frames as Y ≈ p(X + Y ),

because Y is also the expected number of retransmissions in

the next A-MPDU. It follows that Y = pX/(1− p).

Now we can numerically find a value of X that best

approximates Equality 1 subject to the constraint that X +Y ≤K . Let T bln be the time required to transmit an A-MPDU

of X + Y data frames and receive a block ACK: T bln =T phy + (X + Y )(T ap + T data) + SIFS + T lphy + T back. We

get that the noisy goodput under UDP is NoisyUDPGdpt =X∗Ldata

DIFS+T bo+T bln.

The UDP channel utilization as a function of the packeterror rate and the aggregation size for the mandatory and

optional PHY rates is presented on Figure 5 and Figure

6, respectively. Note that the channel utilization deteriorates

quickly for low error rates and more slowly for high error

rates. Similarly to the ideal case, the performance of K = 64is only slightly better than that of K = 32.

2) TCP Traffic: Let T ben be the extra time required to

transmit an A-MPDU of X/2 TCP ACKs: T be = T phy +X(T ap+T tcp−ack)

2 + SIFS + T lphy + T back, where X is taken

Fig. 7. A-MPDU noisy channel utilization under TCP for R = 130Mbps.

Fig. 8. A-MPDU noisy channel utilization under TCP for R = 300Mbps.

from Equality 1. In this way, we get that the noisy goodput

under TCP is NoisyTCPGdpt =X∗Ldata∗(1−c)

2(DIFS+T bo)+T bln+T ben,

where c = 1/CW min. The TCP channel utilization as a

function of the packet error rate and the aggregation size for

the mandatory and optional PHY rates can be found on Figure7 and Figure 8, respectively. Observe that TCP performance

degrades faster for high packet error rates compared to that of

UDP.

B. A-MSDU Aggregation

We have that the loss probability for an A-MSDU is pa =1− (1− p)K .

1) UDP Traffic: The probability that the n-th subsequent

transmission of an A-MSDU frame is successful is (1− pa) ∗ pn−1a . The expected number of retransmissions before the first

success is∞

i=1

(1− pa) ∗ pi−1a ∗ i

= 1/(1− pa). We obtain

that the noisy goodput under UDP is NoisyUDPGdpt =K∗Ldata∗(1− pa)DIFS+T bo+T frm

. The UDP channel utilization as a function

of the packet error rate and the aggregation size for the

mandatory and optional PHY rates appears on Figure 9 and

Figure 10, respectively. Remarkably, the channel utilization

of large aggregations degrades faster and eventually becomes

worse than that of smaller aggregations as the packet error

rate increases. That is due to the fact that if just one of the

aggregated frames contains bit errors, the other frames cannot

be recovered.



Fig. 9. A-MSDU noisy channel utilization under UDP for R = 130Mbps.

Fig. 10. A-MSDU noisy channel utilization under UDP for R = 300Mbps.

2) TCP Traffic: Remember that in our model TCP ACKs

are always transmitted successfully (except collisions). We

have that the noisy goodput under TCP is NoisyTCPGdpt =K∗Ldata∗(1−c)

DIFS+T bo+T frm1−pa

+DIFS+T bo+T fe. The TCP channel utilization

as a function of the packet error rate and the aggregation sizefor the mandatory and optional PHY rates can be found on

Figure 11 and Figure 12, respectively.

V. CONCLUDING REMARKS

In this work we develop an analytical framework to evaluate

the maximum goodput of A-MPDU and A-MSDU aggregation

in IEEE 802.11n high throughput WLAN. We consider a

Fig. 11. A-MSDU noisy channel utilization under TCP for R = 130Mbps.

Fig. 12. A-MSDU noisy channel utilization under TCP for R = 300Mbps.

2 × 2 MIMO system, which is currently being implemented

by the main vendors. The numerical results show that for

UDP traffic, A-MPDU aggregation allows to achieve a high

channel utilization of 95% in the ideal case. At the same time,

the channel utilization without aggregation is limited by 33%.

We also demonstrate that A-MPDU aggregation outperforms

A-MSDU aggregation, whose performance considerably de-

grades for high packet error rates and high PHY rates. Finally,

we investigate how the aggregation size, the packet error rate

and the PHY settings affect the MAC goodput under TCP

and UDP traffic. Our analytic model can be useful for tuning

802.11n aggregation parameters for maximal performance. We

plan to extend our results to multi-hop environments and

perform practical experiments to complement the theoretical

analysis.

Acknowledgements. We are very grateful to Solomon

Trainin and Adrian Stephens for their expert advise on IEEE

802.11n standard.

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802.11 AMPDU and AMSDU Performance 2007