Performance Analysis of A-MPDU and A-MSDU Aggregation in IEEE n

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1 Performance Analysis of A-MPDU and A-MSDU Aggregation in IEEE 8n Boris Ginzburg Intel Corporation, Haifa, Israel Alex Kesselman Intel Corporation, Haifa, Israel Abstract With recent improvements in physical layer (PHY) techniques, the achievable capacity for wireless LANs (WLANs) has grown significantly However, the overhead of IEEE 8 MAC layer has limited the actual throughput of a WLAN A- MPDU aggregation suggested in IEEE 8n draft is a key enhancement reducing the protocol timing overheads that enables aggregation of several MAC-level protocol data units (MPDUs) into a single PHY protocol data unit (PPDU) Another aggregation scheme proposed in IEEE 8n 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 novel analytic model for estimating the performance of a 8n high throughput wireless link between a station and an Access Point (AP) We consider a MIMO system and investigate how the MAC goodput under and traffic is affected by the aggregation size, packet error rate and PHY settings Our results demonstrate that for traffic, A-MPDU aggregation allows to achieve a high channel utilization of 95% in the ideal case while without aggregation the channel utilization is limited by just 33% We also show that A-MPDU aggregation outperforms A-MSDU aggregation, whose performance considerably degrades for 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 throughput In today s 8 WLANs control frames are transmitted at a basic rate while the transmission time of physical headers is fixed As a result, the 8 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 8e 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 [], [8] derive analytical models of distributed coordination function The performance of block ACK schemes is analyzed in [4], [6], [9] and performance analysis over a 8 WLAN appears in [3], [] IEEE 8n [] is a new WLAN standard that provides both PHY and MAC enhancements to support high data rates over Mbps and up to 6Mbps The main PHY technologies of IEEE 8n are MIMO and adaptive channel coding One key MAC-layer enhancement reducing the protocol timing overheads is the A-MPDU aggregation scheme, which enables aggregation of several MAC-level protocoldata 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 8n 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 8n We present an analytical framework for estimating the maximum throughput of 8n using A-MPDU and A-MSDU aggregation schemes To the best of our knowledge, we are the first to perform analysis that (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 data packets with ACKs We consider a MIMO system The maximum throughput is achieved under the bestcase 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 depend 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 8n WLAN In particular, the best-case channel utilization for the mandatory PHY rate of 3Mbps is 84% under and 95% under traffic For the optional PHY rate of 3Mbps, the maximum channel utilization is slightly worse, that is 78% under and 9% under traffic For A-MSDU aggregation, the corresponding and channel utilization is 5% and 7% for the mandatory PHY rate of 3Mbps and 3% and 53% for the optional PHY rate of 3Mbps Thus, A-MPDU aggregation by far outperforms A-MSDU aggregation We also

2 demonstrate that the performance of A-MSDU aggregation significantly degrades for high packet error rates and high PHY rates Organization The rest of the paper is organized as follows Section II contains the model description The analysis of ideal and noisy channel conditions appears in Section III and Section IV, respectively We conclude with Section V II MODEL DESCRIPTION We analyze the throughput of IEEE 8n under the basic Distributed Coordination Function (DCF) In the maximum throughput setup there is an AP and only one active STA which always has backlogged frames to transmit A STA transmits a frame after it has observed an idle medium for a distributed inter-frame space (DIFS) plus a back-off duration When the STA transmits an aggregation, the AP responds with immediate implicit block ACK or a regular ACK after shortest inter-frame space (SIFS) In our analysis we assume that each frame is retransmitted until it is successfully received We also assume that failures of frames within an A-MPDU are not correlated The goodput is the average rate of the payload delivered by the link layer not counting retransmissions We define the channel utilization as the ratio between the goodput and the PHY rate We also call the number of frames within an aggregation the aggregation size We consider data frames of size 5 bytes, which is a typical MTU size in the Internet The maximum number of frames in an A-MPDU is 64 The maximal A-MSDU size is 7935 bytes and thus it may contain at most 5 frames We assume that PHY headers and block ACK frames are always transmitted successfully given the fact that they are transmitted at the basic and hence the safest rate We also assume that ACKs are always transmitted successfully because of their small size and that the Delayed Acknowledgements scheme is activated and one ACK is sent for every other received data segment (this is the default configuration in most platforms) We study the following 8n PHY settings in 5Ghz band Mandatory: Bandwidth MHz, Modulation Coding Scheme 64-QAM 5/6, Long Guard Interval (8ns), Physical Rate 3Mbps Optional: Bandwidth 4MHz, Modulation Coding Scheme 64-QAM 5/6, Short Guard Interval (4ns), Physical Rate 3Mbps The variables used in the analysis are presented in Table I A A-MPDU Aggregation III IDEAL CHANNEL ANALYSIS ) Traffic: Let T blk be the time required to transmit an A-MPDU of K data frames and receive a block ACK: T blk = T phy + K(T ap + T data ) + SIFS + T lphy + T back We get that the ideal goodput under is IdealGdpt = K L data DIFS+T bo +T blk, where K L data is the payload carried in K frames and DIFS + T bo is the channel access time under DCF ) Traffic: Remember that K/ ACKs are generated for K data packets under the Delayed Acknowledgements scheme Let T be be the extra time required to transmit an A-MPDU of K/ ACKs: T be = T phy + K(T ap+t tcp ack ) + SIFS + T lphy + T back We have that ACKs collide with data packets with probability of at most c = /CW min (approximately 6%) That is due to the fact that both the AP and STA select the same slot in the contention period with probability /CWmin and there are CW min such slots Note that after a successful transmission, the contention window is decreased to CW min and the probability of consecutive collisions is rather small Specifically, the probability of n-th subsequent collision is /(CW min n ) We approximate the throughput assuming a constant collision probability of /CW min The total time required to transmit an A-MPDU of K data frames and an A-MPDU of K/ ACKs is (DIFS +T bo )+T blk +T be The expected number of retransmission attempts before a successful transmission is /( c) Therefore, the ideal goodput K L data ( c) (DIFS+T bo )+T blk +T be under is IdealGdpt = 3) Numerical Results: The channel utilization as a function of the aggregation size for the mandatory and optional PHY rates is presented on Figure and Figure, respectively Note that the channel utilization improves as K increases However, starting from K = 3 the channel utilization stays almost flat The maximum channel utilization for the mandatory PHY rate of 3Mbps is 84% under and 95% under traffic For the optional PHY rate of 3Mbps, the maximum channel utilization is slightly worse, that is 78% under and 9% under traffic The performance difference between the mandatory and optional PHY rates is because fixed MAC TABLE I VARIABLES USED IN THE ANALYSIS: Name Description R physical rate = 3/3 Mbits/sec R basic basic physical rate = 54 Mbits/sec K maximum A-MPDU size W receiver window size = 64 T phy mixed mode PHY preamble and header time = 448us T lphy legacy PHY preamble and header time (8a) = 4us L mac MAC overhead = 34 bytes = 7 bits L as MAC overhead in A-MSDU = 4 bytes = bits L data data frame length = 5 bytes = bits L ack ACK frame length = 4 bytes = bits L back block ACK frame length = 3 bytes = 4 bits L tcp ack ACK length = 4 bytes = 3 bits T mac MAC header time = L mac/r T ap A-MPDU MAC header time = (L mac + 4)/R T as A-MSDU MAC header time = L as/r T data data frame time = L data /R T ack ACK frame time = L ack /R basic T back block ACK frame time = L back /R basic T tcp ack ACK time = L tcp ack /R CW min minimum contention window size = 5 T slot slot time = 9us SIF S shortest inter-frame space = 6us DIF S distributed inter-frame space = 34us p packet error rate c collision probability T bo average back-off interval = (CW min ) T slot /

3 9 PHY rate = 3Mbps, Channel width = Mhz, Frame size = 5 bytes 9 PHY rate = 3Mbps, Channel width = Mhz, Frame size = 5 bytes Fig A-MPDU ideal channel utilization for R= 3Mbps Fig 3 A-MSDU ideal channel utilization for R = 3Mbps 9 PHY rate = 3Mbps, Channel width = 4Mhz, Frame size = 5 bytes 9 PHY rate = 3Mbps, Channel width = 4Mhz, Frame size = 5 bytes Fig A-MPDU ideal channel utilization for R= 3Mbps Fig 4 A-MSDU ideal channel utilization for R = 3Mbps overheads constitute a larger fraction of the channel access time for higher PHY rates Observe that without aggregation, the channel utilization is limited by 8% and 33% under and traffic, respectively B A-MSDU Aggregation ) 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 obtain that the ideal goodput under is IdealGdpt = K L data DIFS+T bo +T frm ) Traffic: Let T fe be the extra time required to transmit an A-MSDU of K/ ACKs: T fe = T phy + T mac + K(Tas+T tcp ack) + SIFS + T lphy + T ack As in the previous section, we approximate the throughput assuming a constant collision probability of /CW min The overall time required to transmit an A-MSDU of K data frames and an A-MSDU of K/ ACKs is (DIFS + T bo )+T frm +T fe Therefore, the ideal goodput under is K L IdealGdpt = data ( c) (DIFS+T bo )+T frm +T fe, where /( 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 PHY rate of 3Mbps is 5% under and 7% under traffic For the optional PHY rate of 3Mbps, the maximum channel utilization is much worse, that is 3% under and 53% under traffic Note that the performance of A- MSDU aggregation degrades significantly for high PHY rates since MAC overheads are fixed while the time required to transmit payload decreases A A-MPDU Aggregation IV NOISY CHANNEL ANALYSIS We consider Selective Repeat ARQ retransmission scheme ) Traffic: For an A-MPDU, let: X be the average number of new frames; Y be the average number of retransmitted 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 ( p) i p, where p is the packet error rate The expected sending window shift 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 ( p) i= i p i Z + ( p) X+Y Z () X + Y (X + Y )( p) X+Y + (X + Y )( p) X+Y + p Z + ( p) X+Y Z, p(x + Y )

4 9 PHY rate = 3Mbps, Channel width = Mhz, Frame size = 5 bytes K=6 9 PHY rate = 3Mbps, Channel width = Mhz, Frame size = 5 bytes K= Fig 5 A-MPDU noisy channel utilization under for R = 3Mbps Fig 7 A-MPDU noisy channel utilization under for R = 3Mbps 9 PHY rate = 3Mbps, Channel width = 4Mhz, Frame size = 5 bytes K=6 9 PHY rate = 3Mbps, Channel width = 4Mhz, Frame size = 5 bytes K= Fig 6 A-MPDU noisy channel utilization under for R = 3Mbps Fig 8 A-MPDU noisy channel utilization under for R = 3Mbps where ( 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/( p), W) since the expected number of retransmissions of an individual frame is /( p) and the 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/( p) Now we can numerically find a value of X that best approximates Equality 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 is NoisyGdpt = X L data DIFS+T bo +T bln The channel utilization as a function of the packet error 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 = 64 is only slightly better than that of K = 3 ) Traffic: Let T ben be the extra time required to transmit an A-MPDU of X/ ACKs: T be = T phy + X(T ap+t tcp ack ) + SIFS + T lphy + T back, where X is taken from Equality In this way, we get that the noisy goodput X L under is NoisyGdpt = data ( c) (DIFS+T bo )+T bln +T ben, where c = /CW min The 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 Figure 7 and Figure 8, respectively Observe that performance degrades faster for high packet error rates compared to that of B A-MSDU Aggregation We have that the loss probability for an A-MSDU is p a = ( p) K ) Traffic: The probability that the n-th subsequent transmission of an A-MSDU frame is successful is ( p a ) The expected number of retransmissions before the first success is ( ( pa ) p i a i ) = /( p a ) We obtain p n a i= that the noisy goodput under is NoisyGdpt = K L data ( p a) DIFS+T bo +T frm The 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, 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

5 9 PHY rate = 3Mbps, Channel width = Mhz, Frame size = 5 bytes K= 9 PHY rate = 3Mbps, Channel width = 4Mhz, Frame size = 5 bytes K= Fig 9 A-MSDU noisy channel utilization under for R = 3Mbps Fig A-MSDU noisy channel utilization under for R = 3Mbps Fig 9 PHY rate = 3Mbps, Channel width = 4Mhz, Frame size = 5 bytes K= A-MSDU noisy channel utilization under for R = 3Mbps ) Traffic: Remember that in our model ACKs are always transmitted successfully (except collisions) We have that the noisy goodput under is N oisyt CP Gdpt = K L data ( c) The channel utilization +DIFS+T bo +T fe DIF S+T bo +T frm pa as a function of the packet error rate and the aggregation size for the mandatory and optional PHY rates can be found on Figure and Figure, 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 8n high throughput WLAN We consider a Fig 9 PHY rate = 3Mbps, Channel width = Mhz, Frame size = 5 bytes K= A-MSDU noisy channel utilization under for R = 3Mbps MIMO system, which is currently being implemented by the main vendors The numerical results show that for 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 degrades 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 and traffic Our analytic model can be useful for tuning 8n 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 8n standard REFERENCES [] IEEE Computer Society, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Enhancements for Higher Throughput, IEEE P8n-D, February 7 [] G Bianchi, Performance Analysis of the IEEE 8 DCF, IEEE Journal on Selected Area in Comm, Vol 8, No 3, March [3] R Bruno, M Conti, and E Gregori, Throughput Analysis of and Flows in IEEE 8b WLANs: A Simple Model and Its Validation, Proceedings of FIRB-Perf 5, pp [4] R R Choudhury, A Chen, and S Emeott An Analytical View of Data Aggregation in IEEE 8 LANs, Technical Report at Motorola Labs, 5 [5] S Kuppa and G R Dattatreya, Modeling and analysis of frame aggregation in unsaturated WLANs with Finite buffer stations, to appear in Proceedings of IEEE ICC 6, pp [6] T Li, Q Ni, T Turletti, and Y Xiao Performance Analysis of the IEEE 8e Block ACK Scheme in a Noisy Channel, Proceedings of IEEE BroadNets 5 [7] C Liu and AP Stephens, An analytic model for infrastructure WLAN capacity with bidirectional frame aggregation, Proceedings of IEEE WCNC 5, pp 3-9 [8] YC Tay and KC Chua, A Capacity Analysis for the IEEE 8 MAC Protocol, Wireless Networks, Vol 7, pp 59-7, [9] I Tinnirello and S Choi, Efficiency Analysis of Burst Transmissions with Block ACK in Contention-Based 8e WLANs, Proceedings of IEEE ICC 5, pp [] A De Vendictis, F Vacirca and A Baiocchi, Experimental Analysis of and Traffic Performance over Infra-structured 8b WLANs, Proceedings of the European Wireless 5