On-line Measurement and Modeling of Link Quality in Mobile Wireless Networks

B671801b688016898718badee0abbf0f?s=47 Yongsen Ma
February 20, 2013

On-line Measurement and Modeling of Link Quality in Mobile Wireless Networks

Graduate Defense of Yongsen Ma at SJTU

B671801b688016898718badee0abbf0f?s=128

Yongsen Ma

February 20, 2013
Tweet

Transcript

  1. £Ä ä& 3‚ÿÁ†A^ ê [Ü >f&E†>íó§Æ gÄzX 2013/03/09 ê [Ü

    (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 1 / 50
  2. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 2 / 50
  3. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 3 / 50
  4. £Ä ä  äuÐׄ§A 3¤k/œU0XÚ¥ÑU é § A ^§'XœU[Ø!œU¢‰!œUš !œU¢½!œ¦/¥"

     ä’ÖI¦¥±YO\ª³§ÓžE¤Ã‚ªÌ] LÝ P@"cÙéu£Ä ä ó§Ù I)û ¯K´µXÛ3 y 䌂5 Ä:þ§¦þJpÙDÑ5U9ªÌ|^ Ǻ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 4 / 50
  5. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 5 / 50
  6. Ï&ŸþÿÁ & G †ó´ŸþQ´ïþ ä5U -‡•I§Óž3 äû üL§¥už-‡Š^[1]§Ïd'…¯K´XÛO(p /é& G

    †ó´Ÿþ?1¢žÿþ§l ¢yŒ‚5†DÑ5U k ²ï" [1] Ian F. Akyildiz et al. “NeXt generation/dynamic spectrum access/cognitive Radio Wireless Networks: A Survey”. In: COMPUTER NETWORKS JOURNAL (ELSEVIER) 50 (2006), pp. 2127–2159. ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 6 / 50
  7. Ï&ŸþÿÁ-p„£Ä ä p„c´ ć¯K´ yS 5§Ì‡dGSM-R ä5¢y¶ éGSM-R äcٴ •?1¢žÿÁ[2]§±

    yS $1" GSM基础设施 eMLPP VGCS VBS 功能寻址 位置有关 寻址 接入 矩阵 功能号 表示 调度通信 自动列车控制(CTCS) 远程控制 铁路紧急救援移动服务 平面调车 轨道维护移动服务 高速数字通信 尾部风压检测 区间移动通信 旅客列车综合移动信息服务 GSM应用 ASCI业务 铁路业务 铁路应用 (a) ’Ö . GPRS BSS SSS OSS 电路域数据 应用系统 PCU BSC SGSN GGSN GPRS接口服务器 铁路信息系统 其他信息网络 BTS TRAU BTS BTS MSC/VLR EIR GCR GMSC/VLR 铁路固定电话网 公共电话网 HLR OMC 智能网业务平台 FAS FAS 调度台 车站台 BSS:基站子系统 OSS:操作维护子系统 SSS:网络交换子系统 GPRS:通用分组无线业务子系统 Um Abis A PRI AUC (b) äe ã 1: GSM-R ä’Ö .† äe [2] G. Baldini et al. “An early warning system for detecting GSM-R wireless interference in the high-speed railway infrastructure”. In: International Journal of Critical Infrastructure Protection (2010). ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 7 / 50
  8. Ï&ŸþÿÁ-Âە ä 802.11n äæ^MIMO-OFDM9Ùƒ'Eâ[3]§l k /J, ä DÑ5U§ y•p óéþ†CX‰Œ"

    PHY MIMOµæ^õU‚9˜mE^Eâ§J,PHYóéþ9CX‰ Œ§ÓžJpXÚ-½5¶ HT20/HT40µæ^(¹& E⧕Ð/)û1Å f!Ûõ/ ³ªà ¯K" MAC A-MPDUµvàÜEâ=õ‡v ^˜‡MACÞܧӞü $ACKuxªÇ§ü$ux/ Âm•§JpDÑ Ç¶ SGIµ400ns om…§ü$žmm•§JpMACóéþ" [3] E. Perahia et al. “Next Generation Wireless LANs: throughput, robustness and reliability in 802.11n”. In: Recherche 67 (2008), p. 02. ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 8 / 50
  9. Ï&ŸþÿÁ-ž˜A5 £ÄÂ ä žC5† ˜ É5 1 Â&Òr݆DѤõÇÉÂD‚¸K•§Xã 2a ¤«¶

    2 Â&ÒrÝäk ˜ É5§Xã 2b ¤«¶ 3 DѤõÇ3ØÓžmºÝSu)Cz§Xã 2c ¤«" -100 -80 -60 -40 -20 0 0 20 40 60 80 100 RSS(dBm) LOS NLOS -85 -80 -75 0 20 40 60 80 100 0 20 40 60 80 100 PDR(%) Time(s) (a) 0 0.2 0.4 0.6 0.8 1 -90 -60 -30 0 CDF RSS(dBm) T2, r4 T2, r5 T2, r6 (b) 20 40 60 80 100 0 1 2 3 PDR(%) Time(s) static Δt=10ms Δt=50ms Δt=100ms (c) ã 2: Â&Òr݆DѤõÇ žC59 ˜ É5 ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 9 / 50
  10. Ï&ŸþÿÁ-Ø©( ©Ì‡ é£ÄÂ ä žm†˜mA5§©OJÑ& G †ó ´Ÿþ Ä ÿÁŽ{§±ü$ÿÁm•¿JpÿÁ°Ý§?

    k J , ä DÑ5U" & G æ † O 1 Šâp„£Ä äÂD‚¸ A:§Šâ c äG ?1Ä æ §Óž ¤Páëê O†æ ªÇOŽ¶ 2 3 yÿÁ°Ý cJeü$ÿÁm•§3p„£Ä^‡eü$& æ éêâDÑ Ø|K•" ó´ŸþÿÁ†ï 1 ÏLÄ wIJþŽ{éDѤõÇ?1¢žÿþ§Šâ äG ¢yó´ŸþÿÁ°Ý†m• k ²ï¶ 2 O¿¢yó´Ÿþ3‚ï µe§Óž|^Ôn †ó´ •I ¢y£ÄMIMO-OFDM ä „Ç· " ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 10 / 50
  11. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 11 / 50
  12. ÂDÂÿÁ-ykóŠ ÿÁL§ £Ä ä ÂDÂÿþL§Xã 3 ¤«§Ì‡©• /þŠ O!à ‚DÂýÿ9

    .? " Local Power Estimation Received Signal AMP Propagation Prediction Model Correction ) (x P r ) (x S ) (x M 2 1 , K K ã 3: £Ä äÂDÂÿþL§ duGSM-R äéS 5äk¦§ykÿÁ•{þæ^pªæ §¤ p…=·^ul‚ÿÁ§Ã{A^u3‚ÿÁ9] NÝ" ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 12 / 50
  13. ÂDÂÿÁ-ykóŠ DÚ•{ Lee¼æ Ž{•kJÑ£Ä ä Â&ÒrÝ /þŠ O¯K§Äua|Pá©Û ÚO«m†æ :ê

    'X¶[4] Ù¦Äu˜&«m½•Œq, O •{§Ó 3a|Pá& e?1©Û¶[5] DÚÄu4dPá& æ Ž{ÿÁm• p§Ã{† A^uGSM-R ä¶[6] Ï^Lee¼æ Ž{ØI‡Pá& ©Ù¼ê§ •`æ ëêI‡ÏLŒþêâ©Û §Ó äk p ÿÁm•§•·^ul‚ÿÁ"[7] ÿÁm•µF=900MHz§V=300km/h¶T=480ms§Measure/Data=1/25 Lee¼æ Ž{ ÚO«m•14.4m½172ms§I‡2.8 ÿþžY§óéþü$7.2%¶ ó§A^¥ÚO«m˜„À •1.6m½19.2ms§I‡25 ÿþžY§óéþü$96%" [4] W.C.Y. Lee. “Estimate of local average power of a mobile radio signal”. In: IEEE Trans. on Vehicular Technology (1985), pp. 22–27. [5] Bo Ai et al. “Theoretical analysis on local mean signal power for wireless field strength coverage”. In: WCSP ’2009. [6] C. Tepedelenlio˘ glu et al. “Estimation of Doppler spread and signal strength in mobile communications with appli- cations to handoff and adaptive transmission”. In: Wireless Commun. and Mobile Computing (2001), pp. 221–242. [7] D. de la Vega et al. “Generalization of the Lee Method for the Analysis of the Signal Variability”. In: IEEE Trans. on Vehicular Technology 58.2 (2009), pp. 506 –516. ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 13 / 50
  14. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 14 / 50
  15. Ä æ † O-Ž{ O ÿÁL§ 4dPá& Â&ÒrÝÄ ÿÁL§Xã 4

    ¤«§Ì‡ÏLëê O(½ÚO«m•ÝÚæ :ê8" 1 ŠâÂD‚¸¢žN æ ë꧷A äG Cz¶ 2 3 yÿÁ°Ý cJeü$æ ªÇ§l ~ ÿÁm•" Signal Strength Dynamic Sampling Signal Strength Dynamic Sampling A. Fading Parameters Dynamic Estimating A. Fading Parameters Dynamic Estimating B. Statistical Intervals B. Statistical Intervals Geographic Information System Geographic Information System Mobile Station's Speed and Direction Mobile Station's Speed and Direction Received Signal C. Sampling Numbers ) , , , ( } , {         i z N EM ) , ( /    d f d    r p i z ) , ( / 2 2    L f L  ) ( N f N  ã 4: 4dPá& Ä ÿÁL§ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 15 / 50
  16. Ä æ † O-Ž{ O 1!ÚO«m•Ý Pe = 10 log10

    ˆ s + σˆ s ˆ s − σˆ s = 10 log10       2σ2+ν2 2σ2 n + 2(1 + n) n 0 g ν2 2σ2 ; ρ dρ 2σ2+ν2 2σ2 n − 2(1 + n) n 0 g ν2 2σ2 ; ρ dρ       (1) 2!æ :ê8 Qe = 10 log10 ¯ r2 + σ ¯ r2 ¯ r2 = 10 log10   σ2 N 2N + ν2 + 2σ2 N N + ν2 σ2 N (2N + ν2)   = 10 log10 2N + ν2 + 2 N + ν2 2N + ν2 (2) ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 16 / 50
  17. Ä æ † O-Ž{ O Ν=0 (Rayleigh) Ν=10 Ν=8 Ν=6

    Ν=4 Ν=2 0 20 40 60 80 100 0.0 0.5 1.0 1.5 2.0 2L Λ P_e dB Pe (dB) 2L/Ȝ (a) σ = 1 Ν=0 (Rayleigh) Ν=2 Ν=4 Ν=6 Ν=8 Ν=10 0 20 40 60 80 100 0.5 1.0 1.5 2.0 2L Λ P_e dB Pe (dB) 2L/Ȝ (b) σ = 3 Ν=0 (Rayleigh) Ν=2 Ν=10 Ν=8 Ν=6 Ν=4 0 20 40 60 80 100 0.5 1.0 1.5 2.0 2.5 2L Λ P_e dB Pe (dB) 2L/Ȝ (c) σ = 5 Ν=0 (Rayleigh) Ν=10 Ν=8 Ν=6 Ν=4 Ν=2 0 20 40 60 80 100 0.5 1.0 1.5 2.0 2.5 2L Λ P_e dB Pe (dB) 2L/Ȝ (d) σ = 7 ã 5: ÚO«m•Ý8˜zØ ÚO«m•Ý Pe = 1dB ⇓ 2L = f2L(λ; ν, σ) (Ø1 ÚO«m•Ý8˜z Ø Pe †ν2/σ2¥é ê‚5'X" ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 17 / 50
  18. Ä æ † O-Ž{ O Ν=0 (Rayleigh) Ν=10 Ν=8 Ν=6

    Ν=4 Ν=2 0 20 40 60 80 100 0.4 0.6 0.8 1.0 1.2 N Q_e dB N Qe (dB) ã 6: æ :ê88˜zØ æ :ê8 Qe = 1dB ⇓ N = fN(λ; ν, σ) (Ø2 æ :ê88˜zØ Qe †ν2¥éê‚5 'X§†σ2ÚλÃ'" æ ªÇµ∆d = 2L/N = f2L (λ; ν, σ)/fN (λ; ν, σ) = fd (λ; ν, σ) ∆d ⇐ ÚO«m•Ý2LÚæ :ê8N¶ ∆d ⇒ ÿþ°Ý†m•" ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 18 / 50
  19. Ä æ † O-5Uµ ÿÁ² M‡² µ¥%?nìCPUµCME137686LX-W¶GSM-RÂÏ& ¬µCOM16155RER ^‡² µmu‚¸µVisual

    Studio 2010¶muŠóµC#¶$1² µMicrosoft .NET Compact Framework¶$1XÚµWindows XP/CE/Mobile (a) M‡² (b) ^‡² ã 7: GSM-R 䘥 •ÿÁXÚ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 19 / 50
  20. Ä æ † O-5Uµ Ž{ O†XÚ¢y 3‚ÿÁŽ{̇•)4dPáëê O†æ ëêOŽ§©•PáÏfν0 Úσ0

    Щz †¢ž•#§Óž(½e˜Óæ ëê2LÚN"• ÏLÚO«m•ÝÚæ :êOŽ æ m…∆d = 2L/N§¿m©#˜Ó &Òæ †ëê O" ÿÁXÚé ä?1¢žiÿ§3 枉Ñý´&E¶‰Ñ ä nÜ5Uµ §¿JÑ ëêN ïƶ•¹Ôn !ó´ †’Ö •I§¢yé ä ¡ÿÁ" Hardware Algorithm Software RSS Current cell Neighbor cell TCH Data Voice Predict and Warning Raw RSS and Traffic GSM-R Networks Δd, Δt 2L, N ν, σ vtrain EM update (a) Ž{ O GSM-R Networks Um Abis A MS------BTS------BSC------MSC------OMC------ 无线传播测试 模型修正 传播预测 参数估计 空中接口 物理层 LAPDm层 无线资源管理 移动性管理 呼叫管理 CC SS SMS 无线信道 适配层 服务层 AT 命令 AT 命令 链路质量测试 网络层 设备层 链路层 语音业务 数据业务 列控业务 硬件&软件 平台 测试算法 空 中 接 口 测 试 系 统 (b) XÚ¢y ã 8: Ž{ O†XÚ¢y ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 20 / 50
  21. Ä æ † O-5Uµ XÚõU GSM-R 䘥 •ÿÁXÚ̇ ¤ äÏ&Ÿþ

    ÿÁ!?n! ýÿ!w«†ý´" 数据采集 参数统计 性能分析 数 据 库 预测 模型 网络性能 评估报告 实时显示 事件报告 预警信息 网络参数 调整建议 存储 读取 修正 读取 (a) 当 前 小 区 信 息 当 前 小 区 信 息 邻 居 小 区 信 息 邻 居 小 区 信 息 业 务 信 道 信 息 业 务 信 道 信 息 GSM-R网络通信质量 GSM-R网络空中接口测试系统 参 数 统 计 性 能 分 析 数据采集 数 据 库 网 络 指 标 预测 模型 数 据 库 其 他 用户终端 操 作 维 护 实 时 显 示 事 件 报 告 预 警 信 息 评 估 报 告 参 数 调 整 (b) ã 9: êâ?n†Ä õU ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 21 / 50
  22. Ä æ † O-5Uµ ÿÁ(J 3ƒÓ ÿÁ°Ýe§=8˜zØ ©O•1dB§Ä Ž{ ÿÁm•

    wÍü$§l k y ä ~Ï&" 4dÏfK = 0ž§ÚO«m2L = 55λ§æ m…•3.7λ§ Lee¼Ž{•1.1λ¶ ‘X4dÏfK OŒ§ÚO«m†æ m…Åìü$¶ ν ≥ 8ž§æ :êN ≤ 10BŒ± y /þŠ O(5§æ m…Œu1m" L 1: ÿÁ(J Terrain K(dB) ν σ 2L(λ) N ∆d(λ) ∆d(m) vtrain(km/h) 200 250 300 ∆t(ms) NLOS* 0 - - 40 36 1.1 0.367 2.20 1.76 1.47 Intensive 0 0 1 55 15 3.7 1.222 7.33 5.86 4.89 2 4 2 18 12 1.5 0.500 3.00 2.40 2.00 4 5.6 2 9 9 1.0 0.333 2.00 1.60 1.33 6 6 3 20 7 2.9 0.967 5.80 4.64 3.87 8 12 3 8 1 8.0 2.667 16.00 12.80 10.67 Open 10 18 4 12 1 12.0 4.000 24.00 19.20 16.00 * Caculated by Lee’s method in the case of Rayleigh fading ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 22 / 50
  23. Ä æ † O-5Uµ ÓžŒºÝÚ ºÝPáU k ©l§•þ A^JøG &Eµ

    1 ŒºÝPáµÏLML½MMSE O?1DÂýÿ[8]¶ 2 ºÝPᵃ†Ž{ ƒ†€•ÀJ[9]" -100 -90 -80 -70 -60 -50 -40 0 50 100 150 200 250 300 350 400 450 500 Signal strength (dBm) Distance along the route (m) ν=18, σ=4 2L=12, N=1 Δd=4, Δt=19 ν=4, σ=2 2L=18, N=12 Δd=0.5, Δt=2.5 received long-term (a) Signal Strength -30 -25 -20 -15 -10 -5 0 5 10 15 20 0 50 100 150 200 250 300 350 400 450 500 Short-term fading (dB) Distance along the route (m) (b) Short-term Fading ã 10: ÿÁ(J [8] L. Gopal et al. “Power Estimation in Mobile Communication Systems”. In: Comp. and Info. Science (2009), P88. [9] K.I. Itoh et al. “Performance of handoff algorithm based on distance and RSSI measurements”. In: IEEE Trans. on Vehicular Technology (2002), pp. 1460–1468. ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 23 / 50
  24. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 24 / 50
  25. ó´ŸþÿÁ-ykóŠ DÚ•{ DÚ•{½ÄuÌÄ&ÿ§½Äu ½PDR-RSS .§Xã 11 ¤«" 1 £Ä äD‚¸†

    äG E,õC§ü$ó´ŸþÿÁ°Ý¶ 2 802.11næ^õ«Ôn Úó´ ˜§O\ó´ŸþÿÁm•¶ 3 MIMO-OFDM õ ˜A5O\ PDR-RSSï E,5" £Ä802.11n ä £Ä5Úõ ˜5ü$ ó´Ÿþ ÿÁ†ýÿ° ݧ?˜ÚK• ä N5U" PDR-RSS Model PDR RSS Model Measure Rate Adaption TX RX Probe Predict Database Channel Access Power Control Routing PHY/MAC Settings Application ã 11: · PDR-RSSµe ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 25 / 50
  26. ó´ŸþÿÁ-ykóŠ ÿÁ•{EWMA: ˆ Pw [k] = α ˆ Pw [k

    − 1] + (1 − α)P[k] 1 \ ÏfαéÿÁ°ÝkéŒK•§nØþ˜„ ˜30.1 0.4ƒ m[10]"¢SXÚ˜„æ^ ½Š£0.125½0.25¤§¦ ÿÁ°( ÝN´É .‚¸† ä ˜Cz K•¶ 2 I••ÝWÓ K•ÿÁ°Ý§· ä¥I••Ý˜„• ½Š £100ms½50ms¤§ ä$13pDÑ„Çž§EWMA¬¢¦ KPDR ]žeü§? ü$ ä5U" 0 1 xi Last Window W; P[k-1] Current Window W; P[k] [10] NIST/SEMATECH. e-Handbook of Statistical Methods. 2012. URL: www.itl.nist.gov/div898/handbook . ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 26 / 50
  27. ó´ŸþÿÁ-ykóŠ 0 20 40 60 80 100 -90 -80 -70

    -60 -50 -40 PDR(%) RSS(dBm) δ- δ+ HT20/LGI MCS 8 MCS 10 MCS 12 MCS 14 (a) PDR-RSS . 0 0 0 0 -90 -80 -70 -60 -50 -40 -30 -20 HT/GI/MCS RSS(dBm) HT20/LGI HT20/SGI HT40/LGI HT40/SGI 15 15 15 15 [δ- ,δ+ ] MCS (b) PDR-RSSLÞI• ã 12: PDR-RSS .LÞI• Xã 12 ¤«§3¤k ÀJ HT/GI/MCS ˜¥§34%á\LÞI• ¥§8%¦ PDR<10%§ù«œ¹3£Ä802.11n 䥕•²w" ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 27 / 50
  28. Jj 1 µ0 £Ä ä Ï&ŸþÿÁ 2 & G æ

    † O ÂDÂÿÁ Ä æ † O 3 ó´ŸþÿÁ†ï ó´ŸþÿÁ 3‚ÿÁ†ï ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 28 / 50
  29. 3‚ÿÁ†ï -Ž{ O ó´Ÿþ3‚ÿÁ†ï 1 éó´Ÿþ ž˜A59 ˜õ 5§æ^Ä wÄI•²þJpÿÁ°Ý¶

    2 æ^3‚ï µe§O(•x c ˜e äG §•þ A^JøŒ‚&E¶ 3 (ÜÄ ÿÁ†3‚ï §Jp„Ç· džO(5§? J, ä5U" Database PDR-RSS Model PDR RSS HT-GI-MCS Index Model Application Measure GradedM GradedR DSWA HT-GI-MCS Channel Access Power Control Routing Rate Adaption TX RX Update PHY/MAC Settings DSWA → GradedM → GradedR 1 •I© ÿÁµDSWA Ôn µRSS/SNR/SINR/CSI ó´ µDѤõÇ(PDR) 2 3‚¢ÿ .µGradedM Ôn µMIMO§HT20/HT40 ó´ µLGI/SGI 3 þ ] © µGradedR APsµ& \!ªÌ+n STAsµ„Ç››!´dÀJ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 29 / 50
  30. 3‚ÿÁ†ï -Ž{ O ÿÁ .µ2ÂËã|‘ÅL§ uxêâ• ÂG •xi = {0,

    1}§ ¤õVÇpi dSINR .•x" pi = Prob[SINRi(t) > δ] = Prob[ Ri(t) Ii(t) + n > δ] = ˆ p (Ri(t)) (3) ÿÁ•{DSWA: ˆ Ps [k] = βP [k] + (1 − β)P[k] £Ä802.11n ä¥ PDRÿþ§Q‡·A‚¸CzE¤ äG ] žÅħq‡÷võ« ˜ÀJ ‡¦" 1 ÏLwÄÏfβÀ þ˜gOŽ¥‚C cž• Ü©êâ§l ü$PDR ]žCzéÿÁ(J K•¶ 2 I••ÝW•¯‡°Ä…† ä ˜Ã'§ ´Šâ c äG Cz§éÿÁ°Ý†m•?1²ï" ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 30 / 50
  31. 3‚ÿÁ†ï -Ž{ O ÿÁ•{DSWA: ˆ Ps [k] = βP [k]

    + (1 − β)P[k] 0 1 xi Last Window W(k-1,n) ; P[k-1] Current Window (1-β(k,n) )W(k,n) ; P[k] xi = 0 xi = 1 β(k,n) W(k,n) ; P'[k] Sliding Window W(k,n) = n i=1 ωi γi ηi n i=1 ωi (4) β(k,n) = 1 + n i=1 ωi γi n i=1 ωi (5) Ù¥γi •PDRCzÏfµ γi = 1+P[k −n+i]−P[k −n+i −1], 1 ≤ i ≤ n, ωi •\ Ïfµ ωi = 1 2 n−i 2 , 1 ≤ i ≤ n ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 31 / 50
  32. 3‚ÿÁ†ï -Ž{ O 3‚ï 1 & ‘°µHT40•N´É‚¸CzK•§ƒÓœ¹e €••p§LÞI••Ý••¶ 2 Dфǵ

    €•‘XDÑ„Ç Jp OŒ§ MCSpu14žI••ÝŒu10dB¶ 3 om…µ$„ÇÄ Ã«O§MCSpu14žSGI²wJ,PDR§cÙéuHT40& " 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 PDR(%) RSS(dBm) HT20/SGI MCS 8 MCS 10 MCS 12 MCS 14 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 -10 RSS(dBm) HT40/SGI MCS 8 MCS 10 MCS 12 MCS 14 (a) MCS 8-14, GI = 400ns 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 PDR(%) RSS(dBm) HT20/LGI MCS 8 MCS 10 MCS 12 MCS 14 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 -10 RSS(dBm) HT40/LGI MCS 8 MCS 10 MCS 12 MCS 14 (b) MCS 8-14, GI = 800ns 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 PDR(%) RSS(dBm) HT20/MCS15 SGI LGI 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 -10 RSS(dBm) HT40/MCS15 SGI LGI (c) MCS 15, GI = 400/800ns ã 13: PDR-RSS . ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 32 / 50
  33. 3‚ÿÁ†ï -Ž{ O 3‚ï Ž{ Ž Ž Ž{ { {

    1 GradedMµPDR-RSS3‚ï †¢ž•# Ñ Ñ Ñ\ \ \µ µ µ pdr-now, rss-now Ñ Ñ ÑÑ Ñ Ñµ µ µ ht-gi-mcs-index 1: struct GradedT { 2: graded-delta[r][2]; // r=8/16/24 éAuU‚êþ1/2/3 3: } graded-table[w][g]; // w=g=2 éAu& HT20/HT40† om…LGI/SGI 4: // 1. PDR-RSS . . .¢ ¢ ¢ž ž ž• • •# # # 5: if graded-delta-changed then 6: graded-table ← update-delta(pdr-now,rss-now); 7: end if 8: // 2. HT/GIÀ À ÀJ J JS S S ü ü üS S S 9: mcs-index ← sort(graded-table,rss-now); 10: // 3. HT/GI/MCSÀ À ÀJ J JS S S ü ü üS S S 11: ht-gi-mcs-index ← sort(mcs-index,mcs-rate); 12: return ht-gi-mcs-index; ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 33 / 50
  34. 3‚ÿÁ†ï -Ž{ O 3‚ï 1. Щz¼ ©PDR-RSS .§¿;•uGradedT¥§ ëêu) Czž?13‚•#§éGradedT?1üS¼

    HT/GI/MCS¢Ú" 0 0 0 0 -90 -80 -70 -60 -50 -40 -30 -20 HT/GI/MCS RSS(dBm) HT20/LGI HT20/SGI HT40/LGI HT40/SGI 15 15 15 15 ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 34 / 50
  35. 3‚ÿÁ†ï -Ž{ O 3‚ï 2. T¢Ú‡A ¤k ˜3 cG eŒ¼

    DÑ5U†Œ‚5§Œ ±Š•PHY/MAC ˜ëêJø‰þ A^" 0 0 0 0 -90 -80 -70 -60 -50 -40 -30 -20 HT/GI/MCS RSS(dBm) HT20/LGI HT20/SGI HT40/LGI HT40/SGI 15 15 15 15 ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 35 / 50
  36. 3‚ÿÁ†ï -Ž{ O 3‚ï 2. T¢Ú‡A ¤k ˜3 cG eŒ¼

    DÑ5U†Œ‚5§Œ ±Š•PHY/MAC ˜ëêJø‰þ A^"                              ǻRSS=-3dB, R=78Mbps ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 36 / 50
  37. 3‚ÿÁ†ï -Ž{ O 3‚PDR-RSSï Ž{ 3. c ˜ePDR<90%½RSS< δ+ §½öPDRÚRSS±Y-½§

    Ã{÷v„ÇI¦§Šâht-gi-mcs-table-#ÀJÜ· ˜"                              ǻRSS=6.5dB, R=52Mbps ǻRSS=6dB, R=43.3Mbps ǻRSS=5dB, R=108Mbps ǻRSS=4dB, R=90Mbps ǻRSS=-3dB, R=78Mbps ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 37 / 50
  38. 3‚ÿÁ†ï -Ž{ O 3‚ï 3. c ˜ePDR<90%½RSS< δ+ §½öPDRÚRSS±Y-½§ Ã{÷v„ÇI¦§Šâht-gi-mcs-table-#ÀJÜ·

    ˜"                              ǻRSS=5dB, R=108Mbps ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 38 / 50
  39. 3‚ÿÁ†ï -5Uµ ¢ ÿÁ ÏLLinuxöŠXÚ±9ath9km ° Ä¢yÂÏ&§é'Ž{•)µ 1 Minstrel-EWMA 2

    Minstrel-DSWA 3 GradedR µ •I•)µ 1 ÿÁŽ{°Ý9m• 2 DѤõÇ 3 óéþ Wireless device Linux kernel xmit.c rc.c recv.c tx rx ath9k nl80211 cfg80211 mac80211 ieee80211 P(.) Ra (.) Rc (.) D(.) G(.) D(.): DSWA G(.): Graded Model P(.): Packet Delivery Rc (.): Rate Control Ra (.): RSS Average W,β PDR RSS η,γ [δ- ,δ+ ] Network Layer Device Layer DSWA.c GradedM.c AP2 AP1 r2 AP r5 P1 P2 P3 P7 P8 P9 P10 P6 P5 P4 P11 r1 r3 10 ft 50 ft r4 r6 T1 T2 ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 39 / 50
  40. 3‚ÿÁ†ï -5Uµ 1!ÿÁŽ{°Ý9m•µ oN ÿÁØ •¡§DSWA²w$uEWMA¶ l\ȩټꌱwѧEWMAÿþŠ–•pu¢SŠ¶ DSWA I••Ý•‹ äG

    k'§†DÑ„ÇÃ'¶ DÑ„Çl6.5Mbps 300Mbps§EWMAŽ{ I••ÝlW = 20O\ W = 500§ W = 500žEWMA¬¢¦KPDR]žeü &E" 0 0.2 0.4 0.6 0.8 1 -1 -0.5 0 0.5 1 CDF Error DSWA EWMA (a) \È©Ù¼ê 100 200 300 400 0 10 20 30 40 50 W 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 β 0 20 40 60 80 100 0 10 20 30 40 50 PDR(%) Time(s) accurate measured (b) ²þI••Ý ã 14: DѤõÇÿÁØ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 40 / 50
  41. 3‚ÿÁ†ï -5Uµ 2!DѤõǵ GradedRÿÁŽ{– 91% PDRpu90%§ Minstrel k63% PDR$u90%¶ ‘XMIMOŒ^U‚êþ

    O\§GradedRŒ‚5‘ƒþ,§Ù$ u90% PDR'~d9%ü•5%§ Minstrell37% þ,•51%" 0 50 100 150 0 20 40 60 80 100 Throughput(Mbps) Time(s) {5%,32%} {0%,9%} GradedR Minstrel (a) 1x3 0 50 100 150 200 250 0 20 40 60 80 100 Throughput(Mbps) Time(s) {8%,34%} {0%,8%} GradedR Minstrel (b) 2x3 0 50 100 150 200 250 300 0 20 40 60 80 100 Throughput(Mbps) Time(s) {0%,5%} {9%,42%} GradedR Minstrel (c) 3x3 ã 15: óéþ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 41 / 50
  42. 3‚ÿÁ†ï -5Uµ 3!óéþµ éu1X3 MIMO ˜§GradedRŽ{•k3žm20¦ƒcóéþ puMinstrelŽ{5-15Mbps§Ù¦œ¹e óéþÄ ƒÓ¶ Ù¦

    ˜e§32X3 ˜eGradedRŽ{puMinstrel5-20Mbps§ 33X3 ˜e$–ˆ 30Mbps 5UJ," 0 10 20 30 40 50 60 70 80 90 100 0 20 40 60 80 100 120 140 CDF (%) Throughput (Mbps) Minstrel GradedR (a) 1x3 0 10 20 30 40 50 60 70 80 90 100 0 20 40 60 80 100 120 140 160 180 200 220 CDF (%) Throughput (Mbps) Minstrel GradedR (b) 2x3 0 10 20 30 40 50 60 70 80 90 100 0 50 100 150 200 250 300 CDF (%) Throughput (Mbps) Minstrel GradedR (c) 3x3 ã 16: óéþ\È©Ù¼ê ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 42 / 50
  43. 3‚ÿÁ†ï -5Uµ 3!óéþµ GradedR31X3Ú3X3 ˜e©OJ,óéþ15MbpsÚ40Mbps¶ RSS$u-60dBmž§ØÓŽ{3üU‚ÚVU‚XÚ óéþÄ ƒÓ§GradedR33X3 ˜e•ŒóéþJ,•5Mbps¶ RSSpu-40dBmž§éu1X3MIMOXÚ§óéþJ,•8Mbps§

    éu2X3Ú3X3MIMOXÚ§óéþJ,©O•25/30Mbps" 0 50 100 150 -80 -70 -60 -50 -40 -30 -20 Throughput(Mbps) Ave. RSS(dBm) Minstrel-EWMA Minstrel-DSWA GradedR (a) 1x3 0 50 100 150 200 250 -80 -70 -60 -50 -40 -30 -20 Throughput(Mbps) Ave. RSS(dBm) Minstrel-EWMA Minstrel-DSWA GradedR (b) 2x3 0 50 100 150 200 250 300 -80 -70 -60 -50 -40 -30 -20 Throughput(Mbps) Ave. RSS(dBm) Minstrel-EWMA Minstrel-DSWA GradedR (c) 3x3 ã 17: óéþ†²þ&ÒrÝ ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 43 / 50
  44. Ø©o( © é£Ä ä Ï&ŸþÿÁ§JÑ& G †ó´Ÿþ Ä ÿÁŽ{§¿©O ¤ÿÁXÚ

    ^M‡² ‡ï§• ÏL¢ é ÿÁŽ{?1ÿÁ!é'†µ " 1 JÑp„£Ä ä& G Ä ÿÁŽ{§3p„£Ä‚¸e yÿÁ°Ý¿k ü$ÿÁm•¶ 2 ‡ïGSM-R 䘥 •iÿXÚ§¿gÌmu3‚ÿÁ^‡² §u®qp„c´?1¢ ÿÁ¶ 3 OÄ wIJþŽ{¢yDѤõÇ O(ÿþ§k ü$d äG žCA5¤E¤ Ø|K•¶ 4 O¿¢yó´Ÿþ3‚ï µe§Óž|^Ôn †ó´ •I ¢y£ÄMIMO-OFDM ä „Ç· ¶ 5 muÂە äÏ&ŸþÿÁ^‡§¿éÿÁŽ{9„Ç· Ž {?1 y!é'†µ " ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 44 / 50
  45. £Ä ä& 3‚ÿÁ†A^ THANKS! ê [Ü (þ° ÏŒÆ) £Ä ä&

    3‚ÿÁ†A^ 2013/03/09 45 / 50
  46. /þŠ O 1!DÂ .µp2 r (x) = s(x)h(x) 1 ÒKPáµ

    s(x) ∼ N m(x), σ2 s (6) 2 õ»Páµ h(x) = 1 √ 1 + K lim M→∞ 1 √ M M m=1 amej(2π λ cos(θmx)+φm ) NLOS Components + K 1 + K ej( 2π λ cos(θ0x+φ0)) LOS Component (7) ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 46 / 50
  47. /þŠ O 2!4d& O[11] νk+1 = 1 N N i=1

    I1 νk zi σ2 k I0 νk zi σ2 k zi (8) σ2 k+1 = max 1 2N N i=1 z2 i − ν2 k 2 , 0 (9) Ù¥N•æ :ê8"ν†σ ЩŠ•µ ν0 =  2 1 N N i=1 z2 i 2 − 1 N N i=1 z4 i   1/4 (10) σ2 0 = 1 2 1 N N i=1 z2 i − ν0 (11) [11] T.L. Marzetta. “EM algorithm for estimating the parameters of a multivariate complex Rician density for polarimetric SAR”. In: International Conference on Acoustics, Speech, and Signal Processing, 1995. IEEE. 1995, pp. 3651–3654. ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 47 / 50
  48. /þŠ O 3!ÚO«m•Ý /þŠÏLp2 r (x) È©²þ O µ ˆ

    s = 1 2L y+L y−L p2 r (x)dx = s 2L y+L y−L h(x)dx (12) σ2 ˆ s = 2(n − 1) n2(1 + K)2 n 0 g(K; ρ)dρ (13) ⇓ Pe = 10 log 10 ˆ s + σˆ s ˆ s − σˆ s = 10 log 10       2σ2+ν2 2σ2 n + 2(1 + n) n 0 g ν2 2σ2 ; ρ dρ 2σ2+ν2 2σ2 n − 2(1 + n) n 0 g ν2 2σ2 ; ρ dρ       (14) ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 48 / 50
  49. /þŠ O 4!æ :ê8 Â&ÒõÇL«•r2 = 2σ2 + ν2 ≈

    1 N N i=1 z2 i £ª(8)Ú(9)¤§Kr2 OŠ9Ù• •µ ¯ r2 = E r2 = 1 N E N i=1 z2 i = σ2 N 2N + ν2 (15) σ¯ r2 = D r2 = 1 N2 D N i=1 z2 i = σ4 N2 4N + 4ν2 (16) ⇓ Qe = 10 log 10 ¯ r2 + σ ¯ r2 ¯ r2 = 10 log 10 σ2 N 2N + ν2 + 2σ2 N √ N + ν2 σ2 N (2N + ν2) = 10 log 10 2N + ν2 + 2 √ N + ν2 2N + ν2 (17) ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 49 / 50
  50. ó´Ÿþï ó´ŸþÿÁØ 1! ½I•µ E[∆PDRf ] = E[ 1 W

    W i=1 xi − p] = 1 W W i=1 E[xi ] − p = 0 (18) D[∆PDRf ] = D[ 1 W W i=1 xi − p] = 1 W W i=1 D[xi ] = p(1 − p) (19) 2!\ I•µ E[∆PDRw ] = E[αPDRf + (1 − α)PDRf − p] = αp + (1 − α)p − p = 0 (20) D[∆PDRw ] = D[αPDRf + (1 − α)PDRf − p] = [α2 + (1 − α)2]p(1 − p) (21) 3!wÄI•µ E[∆PDRs[k + 1]] = E[βP [k] + (1 − β)P[k + 1] − pn] = βp [k] + (1 − β)p[k + 1] − pn (22) D[∆PDRs[k + 1]] = D[βP [k] + (1 − β)P[k + 1] − pn] = βq [k] + (1 − β)q[k + 1] W (23) ê [Ü (þ° ÏŒÆ) £Ä ä& 3‚ÿÁ†A^ 2013/03/09 50 / 50