analytical study of frame aggregation in error- prone channels 2014 yu-antl lab seminar may 29, 2014...

Analytical study of frame aggregation in error-prone channels

Analytical study of frame aggregation in error-prone channels

2014 YU-ANTL Lab Seminar

May 29, 2014

Shinnazar SeytnazarovAdvanced Networking Technology Lab. (YU-ANTL)

Dept. of Information & Comm. Eng, Graduate School, Yeungnam University, KOREA

(Tel : +82-53-810-3940; Fax : +82-53-810-4742http://antl.yu.ac.kr/; E-mail : [email protected])

http://antl.yu.ac.kr/

http://antl.yu.ac.kr/

Advanced Networking Tech. Lab.Yeungnam University (YU-ANTL)

YU-ANTL Lab. SeminarShinnazar Seytnazarov2

OUTLINE

Introduction Overview of 802.11n MAC enhancements• Frame Aggregation

• Block Acknowledgment

The analytical model• Model assumptions

• Model description

Analytical results Frame size dynamic adaptation Conclusion References



Introduction

Motivation Frame aggregation mechanism can increase the efficiency of

MAC layer under ideal channel conditions However, under high Bit Error Rate (BER) sub-frame failures

increases consequently can greatly affect the performance due to their retransmission cost

So, there is a need to examine the effect of frame aggregation feature on the performance under different channel error conditions

Contribution of this paper Authors derive an analytical model to study the impact of the

frame aggregation on the saturation throughput and access delay under lossy channels

Based on numerical results, they propose an algorithm which can dynamically adjust the MPDU sub-frame size based on the maximum FER tolerable by frame's access category



Overview of 802.11n MAC enhancements:

Frame Aggregation Frame aggregation mechanisms

Block ACK

PLCP header Trailer

DA SA MSDU payload Pad

MAC header Subframe 1 ... FCS

A-MSDU

Subframe 2 Subframe n

Len

PLCP header Tail/Pad

MPDU delimiter

MAC header MAC payload FCS Pad

Subframe 1 Subframe 2 ... Subframe 64

A-MPDU



The analytical model

A. Model Assumptions To study the performance of frame aggregation under different

channel conditions, we extend Bianchi’s model [4] to be suitable with 802.11n enhancements.

Stations competing to access to the medium and operating in saturated conditions. Each station always has a traffic available for transmission.

RTS/CTS access scheme Only A-MPDU aggregation

http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6583565#ref_4



Model description



Model description (1)

Model description The probability that a station transmits in a randomly chosen

time slot can be expressed as:

where p is referred to unsuccessful transmission probability, caused by collisions and errors transmission [4], [11].

If we have transmitted frame in a time slot, a conditional collision can occur with the probability when at least one of the remaining n - 1 stations transmit.

If at least 1 bit of frame with FS bits is received with error, the error is applied to hole frame




and given and , we derive the as:

Using the equations (2) and (3) we can deduct the probability of unsuccessful transmission caused by either collisions or transmission errors

Let us first consider the probability that a time slot is empty.

The probability that at least one station transmits in a chosen time slot

The probability for having a transmission without collisions is

The probability for having an erroneous transmission (without collisions):

Finally, the probability that a successful transmission occurs without collisions and errors is:

http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6583565#deqn2

http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6583565#deqn3




1) The Saturation Throughput The network's saturation throughput is calculated as the ratio

of the average number of bits being successfully transmitted in a time slot and the expected average length of a time slot

Expected length of slot time:

In the case of an RTS/CTS access mechanism (Fig. 2), they are determined as follows:

http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6583565#fig_2




Let us examine what happens when we have an A-MPDU aggregated data frame We consider that is the probability that a subframe is erroneous.

Assuming independent errors, the number of erroneous subframes follows a binomial distribution , where denotes the total number of subframes in an A-MPDU aggregated frame. The probability that k of are erroneous is given by

Therefore the average number of erroneous subframes in A-MPDU is

In this case the variable number of bits successfully transmitted can be expressed as

where the total size of each subframes header (MAC header, delimiter, and FCS).




2) The Access Delay The average access delay which is defined as the required time

for an aggregated frame to reach the receivers MAC. Using the network saturation throughput S, each A-MPDU frame

takes an average of to be transmitted. Since, we have n stations competing for the channel we can

derive the average access delay as



Analytical results (1)



Frame size dynamic adaptation (1)

QoS requirements for some ACs according to paper VolP traffics are delay-sensitive and they should tolerate less

than 1–2% packet loss with delays greater than 30ms Streaming video traffic is sensitive to the loss rate less than 5%

and more tolerable to the delay where the latency should be no more than 4 to 5 seconds.

Station runs Algorithm 1 upon receiving Block ACK frame STA measures frame error rate (mFER)

Then it can obtain bit error rate (mBER)

After that, it compares the mFER with the maximum FER tolerable by the corresponding frame access category FERmax_AC




If mFER ≥ FERmax_AC , STA has to use smaller frame aggregation size to meet the AC QoS requirements. Thus the subframe size is reduced using the following equation

Else, it can increase the subframe size to enhance the throughput. However, the step size X_AC of this increase is variable corresponding to the access category parameters. For example we can not use large size for voice traffics. That's authors we have fixed a maximum subframe size for each AC (FSmax_AC).



Conclusion

In this paper Authors derived an analytical model capturing the effect of

frame aggregation on the saturation throughput and the access delay under different channels conditions.

The results showed that the network performance depends significantly on the sub-frame size.

Authors designed an adaptive frame aggregation size algorithm. In this algorithm, the MPDU subframe size is dynamically adjusted

according to the measured FER value from the block acknowledgement frame.

In low FER channels, larger frame size is used to increase the throughput.

And in error-prone channels smaller frame aggregation size is used to meet applications QoS requirements.



References

[1] IEEE 802.11n, Part 11: Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 5: Enhancements for Higher Throughput, Sept. 2009.

[2] N. Wart, R. K. Sheshadri, W. Zheng, and D. Koutsonikolas, “A first look at 802.11n power consumption in smartphones,” in ACM Mobicom International Workshop on Practical Issues and Applications in Next Generation Wireless Networks (PINGEN), Istanbul, Turkey, Aug. 2012.

[3] N. Hajlaoui, I. Jabri, M. Taieb, and M. Benjemaa, “A frame aggregation scheduler for qos-sensitive applications in IEEE 802.11n WLANs,” in International Conference onCommunications and Information Technology (ICCIT), Hammamet, Tunisia, June 2012.

[4] G. Bianchi, “Performance analysis of the IEEE 802.11 distributed coordination function,” IEEE JSAC, vol. 18, no. 3, pp. 535–547, Mar. 2000.

[5] T. Li, Q. Ni, D. Malone, D. Leith, Y. Xiao, and R. Turletti, “Aggregation with fragment retransmission for very high-speed WLANs,” IEEE/ACM Transactions on Networking, vol. 17, no. 2, pp. 591–604, Apr. 2009.

[6] E. Charfi, L. Chaari, and L. Kamoun, “Analytical analysis of applying aggregation with fragment retransmission on IEEE 802.11e EDCA network in saturated conditions,” in International Conference on Communications and Networking (ComNet), Hammamet, Tunisia, Apr. 2012.

[7] S. Frohn, S. Gubner, and C. Lindemann, “Analyzing the effective throughput in multi-hop IEEE 802.11n networks,” Computer Communications, vol. 34, no. 16, pp. 1912–1921, Oct. 2011.

[8] B. S. Kim, H. Y. Hwang, and D. K. Sung, “Effect of frame aggregation on the throughput performance of IEEE 802.11n,” in IEEE WCNC, Las Vegas, Nevada, USA, Apr. 2008, pp. 1740–1744.

[9] R. Hoefel, “IEEE 802.11n MAC improvements: A MAC and PHY crosslayer model to estimate the throughput,” in IEEE VTC, Calgary, Alberta, Canada, Sept. 2008.

[10] Y. Daldoul, T. Ahmed, , and D. Meddour, “Ieee 802.11n aggregation performance study for the multicast,” in Wireless Days’11, Niagara Falls, Ontario, Canada, Oct. 2011.

[11] Y. Lin and V. W. S. Wong, “Frame aggregation and optimal frame size adaptation for ieee 802.11n WLANs,” in GLOBECOM, San Francisco, CA, USA, Nov. 2006.

[12] W. J. F. Heereman, E. Tanghe, D. Plets, L. Verloock, and L. Martens, “Path loss model and prediction of range, power and throughput for 802.11n in large conference rooms,” AEU-International Journal Of Electronics And Communications, vol. 66, no. 7, pp. 561–568, 2012.

[13] J. Yin, X. Wang, , and D. P. Agrawal, “Optimal packet size in errorprone channel for IEEE 802.11 distributed coordination function,” in IEEE WCNC, Atlanta, USA, Mar. 2004.

[14] S. Choi, J. DelPrado, and S. Mangold, “IEEE 802.11e contention-based channel access (EDCF) performance evaluation,” in ICC, Anchorage, AL, USA, May 2003.

analytical study of frame aggregation in error- prone channels 2014 yu-antl lab seminar may 29, 2014...

Documents

yuantl lab seminar

seminar shinnazar seytnazarov

mpdu aggregation slide

bianchis model

ack slide

mpdu subframe size

frames access category

n enhancements