doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 1 Application Throughput Measurements and Predictions using site-specific information Date: November 17, 2004 Author: Prof. Ted Rappaport Chen Na, Jeremy Chen Wireless Networking and Communications Group Department of Electrical and Computer Engineering The University of Texas at Austin

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 2 Performance and Prediction challenges Rapid, repeatable, accurate measurements for end-user experience What is the variability of throughput in a real-world scenario for different uses/platforms What can site-specific information do for network deployment? Set of Applications needed to test user scenarios (usage profiles)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 3 How will TG T be useful? Metrics, user profiles, and standard performance and prediction metrics from this task group will allow equipment to be compared and network deployments to be conducted using standard benchmarks Antenna gains, power levels, user application profiles, close-in transmitters, throughput vs. SNR/SIR curves, Noise and Interference models all impact network performance

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 4 Using results from Task Group T Wireless must become “easier to use” and “more reliable” with less customer support calls TGT will help standardize performance measurement of different vendor equipment in assembly line or chamber settings TGT data will be used in commercial software for network testing, network design, network management, and network layout Fundamentals of wireless performance require knowledge of specific environment

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 5 Fundamentals of Network Performance and Prediction The need for site-specific throughput prediction models –Information technologists and consumers deploy WLAN by trial and error due to their limited knowledge of antenna and propagation issues –Models exist for signal-strength predictions, but Internet users are interested in application throughput for different user profiles (streaming, VoIP, http) –To manage interference and costs, computer assisted testing, design, and deployment must be used, and results must be learned over time (histories built)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 6 Site-specific environment dictates network coverage, capacity, throughput, etc.

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 7 Related Works on WLAN Throughput [He03] The performance of all hosts are degraded if one host transmits at low rates [Bi00,Ch03] Analytical throughput at the MAC layer, as a function of bit error rate (BER) [Ga03] UDP aggregate throughput in b can reach up to 6.1 Mbps for a single user, and reduce to 5.75 Mbps for 5 users sharing the same AP (1.15 Mbps per user) [Va02] b can accommodate 6 VoIP users (ITU G711a-law codec)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 8 Proven Site-specific RF Prediction Models in real-world environments Predictions of signal strengths in buildings [Seidel, Rappaport,1994], [Durgin et al,1998]; Throughput prediction models [He01], [Ra00]

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 9 Using site-specific modeling SW to measure and predict performance Today’s off-the-shelf site-specific modeling tools are used by enterprises/industry Can we exploit this for multiple application scenarios (user profiles)? How variable is the “real world”? How variable are different vendors? How does this impact the end user

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 10 User Profile Testing Approach How similar are the Manufacturers that meet standard (production quality)? Heart-beat or VoIP applications (low latency) Generic Web applications – FTP-type bursty data Web- downloading Home Media – Streaming video

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 11 Extensive measurements validate site-specific throughput Sites: Three restaurants (Schlotzsky’s deli) Apparatus: laptops, IEEE b wireless network interface cards (NICs): Cisco and ORiNOCO Throughput Measuring software using 3 standards: LANFielder (Wireless Valley), Iperf, Wget (FTP) Measurements conducted outside of normal business hours Measurement Scenarios: 1. single user; 2. multiple users

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 12 LANFielder 3 options: TCP, TCP Flood, UDP Emulating a heart-beat or repeater function Send a packet back and forth [He01] Ethernet header IP header UDP header Raw data 1472 bytes maximum 1500 bytes maximum (network layer) 1514 bytes maximum (data link layer)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 13 Iperf Adjusts the sliding-window size of TCP according to how quickly the server responds Reports throughput as a function of the TCP window size Usually reports higher throughput than Wget (FTP) and LANFielder

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 14 Wget (FTP) A FTP client software Retrieve different sizes of packets from the server depending on channel conditions (3 MB, 300 KB) FTP is a good reference because of its popularity

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 15 Single-user Measurement Platform

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 16 Details of Single-user Measurements For each of the 3 restaurants, 11 locations were chosen 2 NICs: Cisco & ORiNOCO 3 applications: LANFielder, Wget, Iperf 4 directions: north, east, south, west Totally, 792 sets of ten-second measurement runs

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 17 The Guadalupe Restaurant Partition descriptionColorAttenuat ion (dB) Glass doors and windows Red5.26 Concrete block wallsDark gray6.83 Wooden partitionsLight blue4.70 Short countersLight gray0.50

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 18 The Northcross Restaurant Partition descriptionColorAttenua tion (dB) Glass doors and windowsRed5.65 Concrete block wallsDark gray 8.39 Wooden partitionsLight blue 0.59 Short countersLight gray 1.84 Metallic racksYellow7.47 TreeGreen0.10

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 19 The Parmer Restaurant Partition descriptionColorAttenuati on (dB) Glass doors and windows Red2.00 Concrete block wallsBlue5.10 Wooden partitionsYellow3.48 Short countersLight gray0.50 Stony pillarsPurple1.50 Thin pillarsGreen3.00

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 20 Multi-user Measurement Platform

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 21 Measurement Applications and Tools ClientServer ComputerDell C640 & HP OmnibookCompaq N600c OSWindows XP NICCisco & ORiNOCON/A FTPWgetIIS LANFielderLANFielder ClientLANFielder Server IperfIperf ClientIperf Server SNRLANFielder & netstumblerN/A

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide locations (Guadalupe)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide locations (Parmer)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide locations (Northcross)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 25 Details of Multi-user Measurements 4 client laptops were used For each of the 3 restaurants, 3 locations (A,B,C) were chosen (for 3 different SNR ranges) –Strong (30 dB or above); Medium (15 ~ 30 dB); Weak (below 15 dB) –15 combinations: AAAA, AAAB, AAAC, AABB, AABC, AACC, ABBB, ABBC, ABCC, ACCC, BBBB, BBBC, BBCC, BCCC, CCCC 1 NIC: Cisco 1 application: LANFielder 4 directions: north, east, south, west Each measurement run takes 10 seconds

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 26 3 locations (Guadalupe)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 27 3 locations (Parmer)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 28 3 locations (Northcross)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 29 Two Throughput Models that seem to work well The piecewise model The exponential model

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 30 Parameters T max (bits per second): throughput saturation level SNR 0 (dB): the SNR where throughput is zero SNR c (dB): the SNR threshold where throughput reaches T max A p (bps per dB): the slope of the line when SNR < SNR c A e (dB -1 ): the rate at which the throughput reaches saturation

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 31 Cisco card data Guadalupe Northcross Parmer All three restaurants

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 32 Cisco card data (spatial average) Guadalupe Northcross Parmer All three restaurants

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 33 Model Trends (cont’d) SNR 0 : between -6 and 13 dB A p : between 0.06 and 0.42 A e : between 0.04 and 0.20 SNR 0, A p, and A e determine the throughput when SNR < SNR c Correlation coefficients (R) are over 80% in most cases, and over 70% in all cases

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 34 For General Building Environments Spatial Average All Three Restaurants Cisco card Exponential model T max (Mbps) A e (dB -1 ) SNR 0 (dB) μ (Mbps)σ (Mbps) R(%) Iperf Wget LANFielder

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 35 Cisco card data (spatial average) Guadalupe Northcross Parmer All three restaurants

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 36 Different vendors and models T max : –Hardware-specific: in many cases, Cisco cards have slightly higher T max than ORiNOCO cards. ORiNOCO cards perform slightly better at low SNR –Application-specific: Iperf: 4.6~5.4 Mbps; FTP: 3.7~4.5 Mbps; LANFielder: 1.5~2.1 Mbps SNR c (used in the piecewise model) –On the order of 20 dB –Implies high throughput within 10 meters (35 feet) of the AP

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 37 Blind Throughput Predictions using a Standard Modeling Approach Predicted RSSI in dBm –Use [Se94,Du98] models, auto-tuning implemented in site- specific prediction tool LANPlanner by Wireless Valley The ambient noise level in dBm –Perform a quick calibration test in the new environment (typical value: -90 dBm) Mapping from SNR to throughput –Determine T max by back-to-back calibration tests; use A e and SNR 0 of foregoing results

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 38 Performing Tests in WNCG Noise is -90 dBm T max for LANFielder was calibrated as Mbps Reading the table, A e is dB -1, and SNR 0 is 8.25 dB

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 39 Predicted and Measured Throughput NoPredicted RSSI (dBm)Predicted SNR (dB)Predicted Throughput (Kbps)Measured Throughput (Kbps)

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 40 Summary of Blind Tests Error mean and standard deviation are reasonable The blind tests verified the prediction models

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 41 References [Ch04] Jeremy Chen, “Site Specific Network Throughput modeling,” M.S. Thesis, Summer 2004, WNCG, University of Texas at Austin [Na04] Chen Na, Jeremy Chen, T.S. Rappaport, “Public WLAN Traffic statistics and throughput prediction,” Electronics Letters, Sept. 13, 2004 [He01] B. E. Henty, T. S. Rappaport, “Throughput Measurements and Empirical Prediction Models for IEEE b Wireless LAN (WLAN) Installations”, ECE Dept., Virginia Tech technical report, MPRG 01-08, 2001 [Ra00] T. S. Rappaport, B. Henty, and R. Skidmore, “System and method for design, tracking measurement, prediction and optimization of data communication networks,” pending U.S. and International Patents. [Du98] G. Durgin, T. S. Rappaport, and H. Xu, “Measurements and models for radio path loss and penetration loss in and around homes and trees at 5.85 Ghz,” IEEE Transactions on Communications, vol. 46, no. 11, pp. 1484–1496, November [Se94] S. Y. Seidel and T. S. Rappaport, “Site-specific propagation prediction for wireless in-building personal communication system design,” IEEE Transactions on Vehicular Technology, vol. 43, no. 4, pp. 879–891, 1994.

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 42 References (II) [He03] M. Heusse et al. “Performance Anomaly of b”, INFOCOM 2003 [Bi00] G. Bianchi, “Performance Analysis of the IEEE Distributed Coordinated Function,” IEEE JSAC, vol. 18, pp , Mar [Ch03] P. Chatzimisios et al, “Influence of channel BER on IEEE DCF,” Electronics Letters, vol. 39, no. 23, pp. 1687–1689, November [Ga03] S. Garg et al, “An experimental study of throughput for UDP and VoIP traffic in IEEE b networks,” IEEE WCNC, 2003 [Va02] A. Vasan et al, “An empirical characterization of instantaneous throughput in b WLANs,” U of Maryland tech report

doc.: IEEE t Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 43 Conclusions Metrics for clients and APs are vital for reliable, repeatable test, deployment and management of networks Site-specific information is valuable and should be used for accurate, reliable network design and management We have presented some models and recent research results that map SNR/SIR (Phy) to end-to-end Throughput (Application) These models scale to various applications, and have been used in blind deployments