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The Study of the performance in IEEE 802.11e EDCA

The Study of the performance in IEEE 802.11e EDCA. Student :Fu-Yuan Chuang Advisor : Ho-Ting Wu Date : 2008.1.14. Outline. Introduction to IEEE 802.11e Proposed Contention Window Adjustment Algorithm Introduction to Fuzzy Control Method Simulation result Conclusion and Future Work.

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The Study of the performance in IEEE 802.11e EDCA

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  1. The Study of the performance in IEEE 802.11e EDCA Student :Fu-Yuan Chuang Advisor : Ho-Ting Wu Date : 2008.1.14

  2. Outline • Introduction to IEEE 802.11e • Proposed Contention Window Adjustment Algorithm • Introduction to Fuzzy Control Method • Simulation result • Conclusion and Future Work

  3. Introduction of IEEE 802.11e • New terminology • QAP – QoS Access Point • QSTA – QoS Station • HC – Hybrid Coordinator • A new mechanism defined in IEEE 802.11e – Hybrid Coordination Function(HCF) • HCF has two access mechanisms • Contention based • Enhanced distributed channel access (EDCA) • Controlled channel access • HCF Controlled Channel Access (HCCA)

  4. IEEE 802.11 MAC architecture Used for Contention Services and basis for PCF Required for Contention Free Services Point Coordination Function (PCF) MAC Extent Distributed Coordination Function (DCF)

  5. IEEE 802.11e MAC architecture Required for Prioritized QoS Services Required for Parameterized Qos Services Required for Contention Free Services for non-Qos STA, optional otherwise Used for Contention Services and basis for PCF HCF Contention Access (EDCA) HCF Controlled Access (HCCA) Point Coordination Function (PCF) MAC Extent Distributed Coordination Function (DCF)

  6. Enhanced Distributed Channel Access (EDCA) • EDCA defines four Access Categories (AC) • Voice • Video • Best Effort • Background • An AC is an enhanced variant of the DCF which contends for TXOP using the following parameters: CWmin[AC], CWmax[AC], AIFS[AC] • The QAP announces the EDCA parameters in selected Beacon frames and in all Probe Response and (Re)Association Response frames

  7. UP-to-AC mappings

  8. IEEE 802.11 DCF • BackoffTime = Random() * aSlotTime • Random() = [0, CW] • aCWmin ≤ CW ≤ aCWmax • If successful transmission then CW = aCWminelse CW = (CW + 1) * 2 - 1 DIFS Contention Window PIFS DIFS SIFS Busy Medium Backoff-Window Next Frame

  9. Contentions among Different ACs in EDCA Contention among EDCAFs (AC, AIFS, CWmin , CWmax ) to win a TXOP AIFS[0] AIFS[1] AIFS[2,3] =DIFS Low Priority AC Medium Priority AC PIFS DIFS/AIFS SIFS Busy Medium High Priority AC Next Frame Backoff Slots

  10. EDCA Parameter Set element AC_BE Parameter Record AC_BK Parameter Record AC_VI Parameter Record AC_VO Parameter Record Element ID (12) Length (18) QoS Info Reserved 1 1 1 1 4 4 4 4 ECWmin / ECWmax ACI / AIFSN TXOP Limit 1 1 2 B0 B3 B4 B5 B6 B7 B0 B3 B4 B7 AIFSN ACM ACI Reserved ECWmax ECWmin ACM : admission control mandatory ACI : Access category identify 0 ≤ CWmin = 2ECWmin – 1 ≤ 32767 0 ≤ CWmax = 2ECWmax - 1 ≤ 32767

  11. Default EDCA Parameter Set element parameter AIFS[AC] = AIFSN[AC] × aSlotTime + aSIFSTime aCWmin = 15 aCWmax = 1023 aSlotTime = 9 us aSIFSTime = 16 us

  12. Problem description • Using the default CWmin is not always adaptive under various situations of network • Only a few of QSTAs • Waste the waiting time • Many QSTAs • High collision probability

  13. Idea • In order to solve the problem, we make QAP dynamically adjust the contention window size of each AC and broadcast the result to all QSTAs every beacon interval • The contention window size of each kind of AC should be adaptive to system loading

  14. Proposed Algorithm • CWmini : new CWmin for each ACs • CWmin_oldi : announced by QAP in the last time • αi : the degree of the effect of ACi, 0 ≤ αi ≤ 1 • defaultCWmin ≤ CWmin ≤ 32767 • Use Fuzzy control method to obtain the αi

  15. Fuzzy Control architecture 模糊規則庫 Fuzzy Rule 模糊化 Fuzzification 模糊推論 Fuzzy Inference 解模糊化 Defuzzification input output

  16. (1.)定義輸入輸出變數 • 找出有意義之狀態加以觀測當作是控制器的輸入變數,同時也找出所需的控制系統的參數當作輸出變數。 • 例如:希望藉由氣溫來判斷該穿多少件衣服則氣溫為輸入變數,穿衣服的量為輸出變數 • In our algorithm: • 輸入變數:(1.)碰撞所佔的時間比例(2.)ACi對網路系統效能的影響程度 • 輸出變數:ACi影響網路狀況的程度

  17. (2.)定義歸屬函數 • 模糊集合(Fuzzy set): • 將一個集合的特性函數ΦA(x)是介於0與1之間,也就是說,x屬於集合A之程度有輕重大小之分。如此這個集合A就是一個不明確的元素隸屬關係,這樣的集合稱之為「模糊集合」。 • 歸屬函數(Membership function): • 模糊集合的特性函數通常稱之為「歸屬函數」。 • 常見的歸屬函數的形狀有梯形、三角形和高斯函數圖形。

  18. (2.)定義歸屬函數 (cont.) • 範例: • 若輸入變數為年齡,將年齡分成三個模糊集合分別為A1(年輕人)、A2(中年人)和A3(老年人),其年齡的歸屬函數定義如下圖。

  19. (2.)定義歸屬函數 (cont.)– in our algorithm • 輸出變數: • ACi影響網路狀況的程度,所以我們將影響的程度區分成五類,語句變數(Linguistic Variable)分別為”VL(很低)”、”L(低)”、 ”M(適中)”、 ”H(高)”與”VH(很高)”,其歸屬函數如下圖

  20. (2.)定義歸屬函數 (cont.) – in our algorithm • 輸入變數: • (1.)碰撞所佔的時間比例 • 定義了三個模糊集合,分別為”L(碰撞機率低)”、”M(碰撞機率適中)”與”H(碰撞機率高)”。

  21. (2.)定義歸屬函數 (cont.) – in our algorithm • 輸入變數: • (2.)ACi對網路系統效能的影響程度 • AC的連線數目 • 比自己高優先權的CWmin • 自己本身的CWmin • 定義了三個模糊集合,分別為”L(影響低)”、 ”M(影響適中)”與”H(影響高)”

  22. AC0 AC1 AC2 AC3

  23. (3.) 模糊化 • 有一個宇集合X,其中一個元素,假使經過一個程序成為一個以X為宇集合之模糊集合A,這個程序就稱為”模糊化(Fuzzification)”。 • 範例: • 以上一個例子為例,若輸入年齡為25歲,經由模糊化可得: μ A1(25) = 0.75 (表示年齡25歲屬於年輕人的程度是0.75) μ A2(25) = 0.25 (表示年齡25歲屬於中年人的程度是0.25) μ A3(25) = 0 (表示年齡25歲屬於老年人的程度是0)

  24. (4.)模糊規則庫 • 設計者可依據過去的經驗、控制知識與系統的特性,擬定適合的控制策略。一個模糊規則庫是由多個模糊推理句,”if…...then…...”組合而成。 • In our algorithm • X1 : 碰撞所佔的時間比例 • X2 : AC對網路系統效能的影響程度 X1 X2

  25. (5.) 模糊推論 • 將輸入的模糊變數轉換成模糊輸出變數,這可說是模糊控制的核心。 • 利用歸屬函數取得各規則的適合程度,然後綜各則的適合程度得到適當的推論,即使規則條件部分的命題不完全一致,也能一句一致的高低比較得到合適的推論 • Minimum Inference Engine

  26. Minimum Inference Engine - ex • (碰撞所佔的時間比例, AC3對網路系統效能的影響程度) = (0.1, 0.04) • 模糊化: • 碰撞所佔的時間比例(X1): • μL(0.1) = 0.5 • μM(0.1) = 0.5 • AC3對網路系統效能的影響程度(X2): • μL(0.04) = 2/3 • μM(0.04) = 1/3 X1 X2

  27. Minimum Inference Engine - ex 碰撞所佔的時間比例 AC3對網路系統效能的影響程度 輸出 L L VL 1 1 1 2/3 0.5 0.5 0 0 0 0.02 0.08 0.25 0.2 0.1 0.04 M L L 1 1 0.5 1/3 1/3 0 0.25 0.2 0.02 0.08 0.14 0.1 0 0.04

  28. 碰撞所佔的時間比例 AC3對網路系統效能的影響程度 輸出 L M L 1 1 1 2/3 0.5 0.5 0 0.2 0.4 0.6 0 0.02 0.08 0 0.25 0.04 0.1 M M M 1 0.5 1/3 1/3 0 0.02 0.08 0.14 0.25 0.5 0.75 0.2 0.4 0.6 0.04 0.1 L M VL 1 0.5 0

  29. (6.) 解模糊化 • 將模糊推論所獲得的結果一個模糊集合B(y),y Y,轉換至一個明確值y*的動作。也就是說找一個最適合代表模糊集合B(y)的明確點y* Y。 • 重心解模糊化法(Center of Gravity Defuzzification, CGD)

  30. Simulation – parameter setting EDCA相關參數設定 CSMA/CA相關模擬參數設定 IEEE 802.11e CWmin、CWmax預設值設定

  31. Simulation – parameter setting(cont.) • Traffic generation • Connection arrival: • Exponential random variable with λ • Connection duration • Exponential random variable with 0.2 • Environment • Simulation time : 200s • Number of QSTAs : 30 • QAP announce EDCA Parameter Set : 3 beacon interval

  32. Simulation-1 • There are 4 ACs in a QSTA

  33. Simulation-1 AC throughput

  34. Simulation-1 AC throughput

  35. Simulation-1 AC delay

  36. Simulation-1 AC delay

  37. Simulation-2 • There are 2 ACs in a QSTA : AC2, AC3

  38. Simulation-2 AC throughput

  39. Simulation-2 AC delay

  40. Simulation-3 • There is only one AC in a QSTA : AC3

  41. Simulation-3 AC delay

  42. Conclusion • 所提出模糊控制動態調整競爭窗口的演算法是可以比標準IEEE 802.11e獲得更高的效能。 • Light loading: • CWmin最小值為原本標準的預設值,因此整體的效能就與標準IEEE 802.11e相當。 • High loading: • 有效的提升系統的throughput、降低系統碰撞的機率,降低各個AC的delay時間。

  43. Future work • Fuzzy control system: • 因為模糊控制系統的設計,導致於必須在網路輕負載的情況做犧牲,只有辦法與原本標準IEEE 802.11e的效能相當。 • Other QoS control: • Admission control

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