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Doc.: IEEE 802.11-04-1478-00-0wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 1 Site-Specific Knowledge for Next Generation Wireless Networks.

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Presentation on theme: "Doc.: IEEE 802.11-04-1478-00-0wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 1 Site-Specific Knowledge for Next Generation Wireless Networks."— Presentation transcript:

1 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 1 Site-Specific Knowledge for Next Generation Wireless Networks Prof. Ted Rappaport Wireless Networking and Communications Group Department of Electrical and Computer Engineering The University of Texas at Austin November 17, 2004

2 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 2 Some Wireless Next Generation Activities WiFi and 3G combination chipsets UWB/Home Media Gateway MiMo and OFMD-based modulation Advanced Security Cross Layer, Universal MAC Integrated Multiband/colocated antennas Mesh Networks/Site-specific Radio Management

3 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 3 Site Specific knowledge is needed in Next Generation Networks We can substantially increase battery life, network performance, enhance coexistence, reduce support calls, and deploy no-fault wireless using “site specific” knowledge PHY/MAC/Radio Resources of today will move to baseband processing and digital “environmental map” in each client Power vs. processing tradeoffs: RF power consumption and Network Inefficiencies (today) versus baseband processing and client’s environmental awareness (next gen) Myriad new services, capabilities become viable

4 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 4 Computing and device trends Vector graphics, 3-D processing capability evolving naturally as part of microprocessor Multiple radios, frequency bands, applications, to become part of PCs, phones, home media, enterprise network products Memory costs and cost per MIPS decreasing exponentially, at much faster rate than battery and RF antenna/propagation breakthroughs History of wireless has not exploited environmental/spatial knowledge in the network, yet propagation depends solely on this!

5 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 5 Wireless Technology and Semiconductor ROADMAP Source: 1. Hotta, Imasao, Shoji Shukuri, and Koichi Nagasawa. “Trends of Semiconductor Techonology for Total System Solutions.” 2.

6 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 6 ITRS Technology Nodes and Chip Capabilities Microprocessor Speeds (MHz) 1,6845,17311,51128,751 Gate Length (nm) DRAM Cost/bit (micro- cents) DRAM memory size512M2G8G64G Source:

7 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 7 A paradigm shift – learning from Qualcomm Qualcomm changed the wireless world: Narrowband radios became wideband radios Tight RF filtering became sloppy RF filtering Channel selection became a baseband processing chore, not an RF/IF chore –plays to Moore’s law Moving the processing to baseband enhanced the network coordination/interoperability and led to flexible upgrade path to data/3G Intellectual property enforcement

8 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 8 Challenges Qualcomm faced Convincing carriers that CDMA improved spectral efficiency, made network deployment easier, increased users and revenue per MHz Convincing carriers to relearn how to design and install base stations (no frequency planning, but code offset planning and soft handoff thresholds) End User has to wait 8 seconds for Qualcomm phone to detect pilot and synch channels, 50 ms speech coder delay, and immediate “hard dropped” calls

9 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 9 A paradigm shift Site-Specific propagation knowledge Site-specific knowledge will change the wireless world: MAC/PHY/QoS/applications will match the propagation environment, instead of being rigid/iteratively implemented Channel selection, power level settings, and network provisioning becomes a baseband processing chore, not an RF/IF chore involving radio usage. Moving the processing to baseband enhances network coordination/interoperability and leads to flexible upgrades,interference mitigation, position location, 4G Intellectual property enforcement (Standards – sharing)

10 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 10 Challenges for Site-specific adoption Convincing chip makers that networks perform better with lower battery drain, plays to Moore’s law if “environmental map” is digitized and exploited Convincing OEM/ODM/ box makers that site- specific network planning and management reduces support calls, reduces user problems, and enhances network performance and features Some site-specific data must be obtained at some point

11 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 11 Today: Network Deployment The need for site-specific prediction models –Many consumers and IT professionals deploy WLAN by trial and error due to limited awareness of antenna and propagation issues. Poor experiences….. –Models exist for signal-strength predictions, throughput coverage, viable CAD software. –Internet users and vendors are interested in application throughput for many different user profiles. –To manage interference, improve QoS, and end-user quality, site-specific CAD design/deployment now being used – large deployments starting to rely on CAD –Eventually, this must become a commodity and brought into networks for management of devices

12 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 12 Network Coverage Software Used by IT Admin./ Network Integrators

13 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 13 Site-specific Prediction Models Predictions of signal strengths in buildings [Seidel, Rappaport,1994], [Durgin et al,1998]; Throughput prediction models [He01], [Ra00]

14 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 14 Extensive measurements to 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: LANFielder (Wireless Valley Inc.), Iperf, Wget (FTP) Measurements conducted outside of normal business hours Measurement Scenarios: 1. single user; 2. multiple users

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

16 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 16 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

17 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 17 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

18 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 18 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

19 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 19 Multi-user Measurement Platform

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

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

22 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 22 Two Throughput Models that relate site- specific SNR to Throughput The piecewise model The exponential model

23 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 23 Cisco card data Guadalupe Northcross Parmer All three restaurants

24 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 24 Cisco card data (spatial average) Guadalupe Northcross Parmer All three restaurants

25 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 25 For General In-Building Environments Spatial Average All Three Restaurants Cisco card Exponential model Scales to 3 different apps. Also see t T max (Mbps) A e (dB -1 ) SNR 0 (dB) μ (Mbps)σ (Mbps) R(%) Iperf Wget LANFielder

26 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 26 Blind Throughput Predictions for a New Environment using Site Specific map 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 SNR to throughput for different apps –Determine T max by back-to-back calibration tests; use A e and SNR 0 of foregoing results

27 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 27 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

28 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 28 Site-specific RF Network Management DESIGNEDDEPLOYED SSID COVERAGE RF REMEDIATION / RECONFIGURATION w/SITE SPECIFIC

29 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 29 Deployed Network Coverage Cube-farm has no coverage in the deployed network due to human deployment error or “bad” equipment

30 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 30 Deployed Network Coverage- Autonomous Network Management using Site-specific knowledge AP01 is automatically reconfigured using digitized map at switch; cube-farm now has coverage in the deployed network

31 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 31 Home and Enterprise Network Management System using Site-specific knowledge How does it work? –User spends approximately seconds inputting basic site-specific information into a GUI –Software uses site-specific algorithms on a digital map to determine coverage areas and optimal equipment positions/configurations within the environment; digitizes finalized infrastructure map and pushes to clients –Devices share site-specific knowledge and measured responses through the network to monitor, control, and diagnose changing RF conditions. –Unless desired, the end user never needs to interact with the software beyond the initial network setup stages and added infrastructure – everything else is automated behind the scenes (power levels, handoff, auto-reconfig. with new nodes). –Hidden node problem, next door neighbor is diagnosed and controlled much more reliably using site-specific knowledge

32 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 32 –Embedded software on a centralized network appliance (e.g., media gateway, hub, switch, etc.) and/or on APs or clients. Leverage site-specific information stored locally on the device to make informed decisions regarding network configurations. Site specific knowledge shared with clients. How does it work? –Site-specific information regarding the environment and network infrastructure is downloaded to the embedded software Embedded software may be pre-loaded on the device or downloaded from the Home NMS –The embedded software monitors network and radio activity it sees in the environment –As events occur that negatively impact network performance, the embedded software can independently analyze the event in the context of the overall network and can respond quickly with device configuration changes that are in the best interests of the overall network Alternate Embodiments: Embedded Network– Centralized Hub, AP, Clients

33 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 33 Embedded software runs on clients either as services in the operating system, as part of a device driver, or directly integrated onto the hardware in some fashion Why do we need it? –This technology places intelligence in the hands of the client devices, with greatest power concern and in closest contact to end-users –Site-specific knowledge, combined with Moore’s law in processing power, allows mobile devices to know where, when, and how to properly manage its power, and applicability, while improving overall network performance. –Memory and CPU requirements scale to allow this to be viable in next one to three years –Ties in with intelligent infrastructure, security, new services –Site-specific knowledge of the client offers ultimate intelligence for communication. Why God gave us eyes, why we like maps in new cars Embodiment of Embedded Network Software in Clients

34 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 34 Client #1: Ch 1, -59 dBm 41 dB SIR 24 Mbps AP1: Ch. 6 1 mW g AP2: Ch. 1, 30 mW g Association TV: Ch dBm 55 dB SIR 54 Mbps. AP1 lowers its power levels to a minimum in order to avoid serving distant clients who can be served by AP2. Client PDA stays with AP2 and has good service. TV on AP1 retains good service. QOS in a Hybrid Environment

35 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 35 AP1: Ch mW g AP2: Ch mW g Client #1: Ch 1, -42 dBm 28 dB SIR 1 Mbps TV: Ch 6, -35 dBm 36 dB SIR 11 Mbps Without site-specific NMS, client associates with AP1 because AP1 offers higher power levels, but interferes with TV on same channel, reduces bandwidth of TV streaming video, and experiences its own reduced bandwidth. QOS in a Hybrid Environment Association

36 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 36 The Site-Specific Revolution…. Coming to Next Generation Networks Theoretical formulations for quantifiable data, metrics, and tradeoffs for semiconductor baseband, RF, software, site-specific traffic, and power overhead are needed, but are emerging. Computing power is evolving to allow “electronic maps” to be exploited in devices for new wireless devices This is an entirely new and unexploited dimension to MAC and PHY – and is cross-layer processing unlike previous solutions in the wireless world Broad scale market adoption is likely, and IEEE should begin studying and standardizing this concept Why did God give us eyes, and why do we like cars with navigation systems in them – they make us more efficient

37 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 37 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.

38 doc.: IEEE wng Submission Nov 2004 Ted Rappaport, WNCG, Univ of TexasSlide 38 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


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