Impact of block ack window sliding on ieee 802 11n throughput performance
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2014 YU-ANTL Lab Seminar. Impact of Block ACK Window sliding on IEEE 802.11n throughput performance. June 7, 2014 Shinnazar Seytnazarov Advanced Networking Technology Lab. ( YU-ANTL) Dept. of Information & Comm. Eng, Graduate School, Yeungnam University, KOREA

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Impact of Block ACK Window sliding on IEEE 802.11n throughput performance

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Impact of block ack window sliding on ieee 802 11n throughput performance

2014 YU-ANTL Lab Seminar

Impact of Block ACK Window sliding on IEEE 802.11n throughput performance

June 7,2014

Shinnazar Seytnazarov

Advanced Networking Technology Lab. (YU-ANTL)

Dept. of Information & Comm. Eng, Graduate School,

Yeungnam University, KOREA

(Tel : +82-53-810-3940; Fax : +82-53-810-4742

http://antl.yu.ac.kr/; E-mail : [email protected])


Outline

OUTLINE

  • Introduction

    • Frame aggregations

    • BAW sliding

  • The analytical model

    • Expected A-MPDU length derivation

    • Throughput derivation

  • Analytical results

  • Conclusion

  • References


Introduction 1

Introduction (1)

  • A-MPDU (Aggregation of MPDUs) - aggregation scheme [1]

    • Sender can aggregate up to 64 MPDUs in A-MPDU frame

    • If receiver receives at least one of the MPDUs successfully, it sends back Block ACK (Block acknowledgement) frame informing about transmission status MPDUs


Introduction 2

Introduction (2)

  • Block ACK Window (BAW) sliding [1]

  • BAW size is equal to 64 that is the maximum allowed A-MPDU length

  • Sender can transmit the MPDUs that are within the BAW

  • BAW continues sliding forward unless any of the MPDUs inside the BAW fails


Introduction 3

Introduction (3)

  • Simple example for BAW = 4


Expected a mpdu length derivation 1

Expected A-MPDU length derivation (1)

  • We introduce several random variables:

    • L – number of MPDUs in A-MPDU i.e. length of A-MPDU, L = 1, 2, . . , 64

    • N – number of new MPDUs in A-MPDU, N = 0, 1, 2, . . , L

    • S – number of successful MPDUs in A-MPDU, S = 0, 1, 2, . . , L

    • F – number of failed/erroneous MPDUs in A-MPDU, F = 0, 1, 2, . . , L

    • X – number of successful MPDUs until the first failure in A-MPDU, X = 1, 2, . . , L

  • We need to find:

    • Expected number of MPDUs in A-MPDU - E[L]

    • Expected number of successful MPDUs in A-MPDU - E[S]

    • Expected number of failed MPDUs in A-MPDU - E[F]

  • Assumptions:

    • Sender’s buffer always has enough number of MPDUs to fill the BAW window

    • MPDU errors occur independently and identically over MPDUs of A-MPDU


Expected a mpdu length derivation 2 2

Expected A-MPDU length derivation (2) [2]

  • Considering assumption (2), the number of failed MPDUs has binomial distribution F ~ B(pe, L), where pe is MPDU error probability and L is the number of MPDUs in A-MPDU:

    (1)

  • So, the expected number of failed/erroneous MPDUs is:

    (2)

  • Number of successfully transmitted MPDUs also has a binomial distribution S ~ B(1 - pe, L):

    (3)

  • So, the expected number of successful MPDUs per A-MPDU is:

    (4)

  • PMF for the number of first successful MPDUs in A-MPDU can be written as:

    (5)

  • Using the above PMF we can calculate expected number of new MPDUs in A-MPDU; gives the expected window shift, where W depicts the window size which is 64:

    (6)

    Here,


Expected a mpdu length derivation 3 2

Expected A-MPDU length derivation (3) [2]

  • The length of A-MPDU – L is the composition of failed MPDUs of previous A-MPDU and newly included MPDUs.

    (7)

  • It is obvious that under certain channel conditions, the expected length of A-MPDU is the sum of the expectations of failed MPDUs and new MPDUs:

    (8)

  • Thus, we will use the expected A-MPDU length instead of A-MPDU length for Equations (1-6):

    (9)

  • Equation (9) has unique solution for E[L] under the given peand can be solved numerically.


Performance of baw sliding under different channel conditions 1

Performance of BAW sliding under different channel conditions (1)

  • Expected length of A-MPDU for different window sizes under different channel conditions


Performance of baw sliding under different channel conditions 2

Performance of BAW sliding under different channel conditions (2)

  • Expected length of A-MPDU, expected number of successful and failed MPDUs under different channel conditions


Discrete time markov chain 3

Discrete time Markov chain [3]


Transmission probability

Transmission probability

  • Transmission probability that a station transmits in a randomly chosen slot time.

    (10)

  • p is backoff stage increment probability due to either collision or A-MPDU failure because of channel noise:

    (11)

  • Equations (10) and (11) can be solved using numerical method and have unique solution for .


Slot durations

Slot durations

  • Idle slot duration Ti: When all STAs are counting down, no station transmits a frame and we have

    (12)

  • Successful slot duration Ts: At least one MPDU in A-MPDU successfully received by receiver, the slot duration is the sum of a A-MPDU, a SIFS and an Block ACK duration

    (13)

  • Collision and ‘A-MPDU failure due to noise’ slot durations Tcand Tf:

    (14)


Probabilities of time slots

Probabilities of Time Slots

  • Idle slot is observed if none of the stations transmits:

    (15)

  • Successful slot is observed if only one station transmits and A-MPDU is not fully failed

    (16)

  • Failure slot is observed if only one station transmits and A-MPDU is fully failed

    (17)

  • Collision slot is observed if none of other slots is observed:

    (18)


Network throughput

Network throughput

  • Network throughput can be defined as:

    (19)


Parameters for numerical analysis

Parameters for numerical analysis


Performance analysis of ieee 802 11n considering baw sliding 1

Performance analysis of IEEE 802.11n considering BAW sliding (1)

  • Network throughput “with BAW” at R = 300Mbps


Performance analysis of ieee 802 11n considering baw sliding 2

Performance analysis of IEEE 802.11n considering BAW sliding (2)

  • Network throughput “with BAW” at R = 600Mbps


Performance analysis of ieee 802 11n considering baw sliding 3

Performance analysis of IEEE 802.11n considering BAW sliding (3)

  • Network throughput comparison “with and without BAW” at R = 300Mbps


Performance analysis of ieee 802 11n considering baw sliding 4

Performance analysis of IEEE 802.11n considering BAW sliding (4)

  • Network throughput comparison “with and without BAW” at R = 600Mbps


Performance analysis of ieee 802 11n considering baw sliding 5

Performance analysis of IEEE 802.11n considering BAW sliding (5)

  • Difference (%) between 'with BAW' and 'without BAW' at different PHY rates


Conclusion

Conclusion

  • In this presentation

    • We analyzed the BAW sliding effect on A-MPDU length under different channel conditions

      • When MPDU error probability increases from 0.0 to 0.3 BAW decreases the A-MPDU length from

        • 64 to 14.57 for window size of 64

        • 128 to 20.65 for window size of 128

    • BAW model was applied in DTMC model for IEEE 802.11n

      • Network throughput was analyzed for different number of nodes and different channel conditions

      • Existing DTMC models for IEEE 802.11n performance have huge difference:

        • Over 20% when MPDU error probability 0.1 at 600Mbps PHY rate

        • Over 10% when MPDU error probability 0.1 at 300Mbps PHY rate

    • Conclusion

      • BAW sliding has significant impact on A-MPDU size and network performance under erroneous channel conditions

      • It is essential to consider BAW effect in order to have an accurate network performance estimations


References

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] Ginzburg, Boris, and Alex Kesselman. "Performance analysis of A-MPDU and A-MSDU aggregation in IEEE 802.11 n." In Sarnoff symposium, 2007 IEEE, pp. 1-5. IEEE, 2007.

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

[4] 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.

[5] Chatzimisios, P., A. C. Boucouvalas, and V. Vitsas. "Influence of channel BER on IEEE 802.11 DCF." Electronics letters 39.23 (2003): 1687-9.


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