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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

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

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  1. 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 : seytnazarovsho@ynu.ac.kr)

  2. OUTLINE • Introduction • Frame aggregations • BAW sliding • The analytical model • Expected A-MPDU length derivation • Throughput derivation • Analytical results • Conclusion • References

  3. 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

  4. 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

  5. Introduction (3) • Simple example for BAW = 4

  6. 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

  7. 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,

  8. 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.

  9. Performance of BAW sliding under different channel conditions (1) • Expected length of A-MPDU for different window sizes under different channel conditions

  10. 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

  11. Discrete time Markov chain [3]

  12. 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 .

  13. 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)

  14. 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)

  15. Network throughput • Network throughput can be defined as: (19)

  16. Parameters for numerical analysis

  17. Performance analysis of IEEE 802.11n considering BAW sliding (1) • Network throughput “with BAW” at R = 300Mbps

  18. Performance analysis of IEEE 802.11n considering BAW sliding (2) • Network throughput “with BAW” at R = 600Mbps

  19. Performance analysis of IEEE 802.11n considering BAW sliding (3) • Network throughput comparison “with and without BAW” at R = 300Mbps

  20. Performance analysis of IEEE 802.11n considering BAW sliding (4) • Network throughput comparison “with and without BAW” at R = 600Mbps

  21. Performance analysis of IEEE 802.11n considering BAW sliding (5) • Difference (%) between 'with BAW' and 'without BAW' at different PHY rates

  22. 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

  23. 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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