Site-Specific Knowledge for Next Generation Wireless Networks

1. Site-Specific Knowledge for Next Generation Wireless Networks Prof. Ted Rappaport Wireless Networking and Communications Group Department of Electrical and Computer EngineeringThe University of Texas at Austin November 17, 2004 www.wncg.org Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

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! Ted Rappaport, WNCG, Univ of Texas

5. Wireless Technology and Semiconductor ROADMAP Source: 1. Hotta, Imasao, Shoji Shukuri, and Koichi Nagasawa. “Trends of Semiconductor Techonology for Total System Solutions.” http://www.hitachi.com/rev/1999/revapr99/r2_101.pdf. 2. http://phys.cts.nthu.edu.tw/workshop/tp5/20031204/T.%20F.%20Lei.pdf Ted Rappaport, WNCG, Univ of Texas

6. ITRS Technology Nodes and Chip Capabilities Source: http://www.sia-online.org/downloads/itrs_2001.pdf Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

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) Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

12. Network Coverage Software Used by IT Admin./ Network Integrators Ted Rappaport, WNCG, Univ of Texas

13. Site-specific Prediction Models • Predictions of signal strengths in buildings [Seidel, Rappaport,1994], [Durgin et al,1998]; • Throughput prediction models [He01], [Ra00] Ted Rappaport, WNCG, Univ of Texas

14. Extensive measurements to validate site-specific throughput • Sites: Three restaurants (Schlotzsky’s deli) • Apparatus: laptops, IEEE-802.11b 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 Ted Rappaport, WNCG, Univ of Texas

15. Single-user Measurement Platform Ted Rappaport, WNCG, Univ of Texas

16. The Guadalupe Restaurant Ted Rappaport, WNCG, Univ of Texas

17. The Northcross Restaurant Ted Rappaport, WNCG, Univ of Texas

18. The Parmer Restaurant Ted Rappaport, WNCG, Univ of Texas

19. Multi-user Measurement Platform Ted Rappaport, WNCG, Univ of Texas

20. Multi-user Measurement Applications and Tools Ted Rappaport, WNCG, Univ of Texas

21. 11 locations (Guadalupe) Ted Rappaport, WNCG, Univ of Texas

22. Two Throughput Models that relate site-specific SNR to Throughput • The piecewise model • The exponential model Ted Rappaport, WNCG, Univ of Texas

23. Cisco card data Guadalupe Parmer Northcross All three restaurants Ted Rappaport, WNCG, Univ of Texas

24. Cisco card data (spatial average) Guadalupe Parmer Northcross All three restaurants Ted Rappaport, WNCG, Univ of Texas

25. For General In-Building Environments • Spatial Average • All Three Restaurants • Cisco card • Exponential model • Scales to 3 different apps. • Also see 802.11-04-1473-00-000t Ted Rappaport, WNCG, Univ of Texas

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 Tmax by back-to-back calibration tests; use Ae and SNR0 of foregoing results Ted Rappaport, WNCG, Univ of Texas

27. Performing Tests in WNCG • Noise is -90 dBm • Tmax for LANFielder was calibrated as 2.403 Mbps • Reading the table, Ae is 0.113 dB-1, and SNR0 is 8.25 dB Ted Rappaport, WNCG, Univ of Texas

28. Site-specific RF Network Management DESIGNED DEPLOYED RF REMEDIATION / RECONFIGURATION w/SITE SPECIFIC SSID COVERAGE Ted Rappaport, WNCG, Univ of Texas

29. Deployed Network Coverage Cube-farm has no coverage in the deployed network due to human deployment error or “bad” equipment Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

31. Home and Enterprise Network Management System using Site-specific knowledge • How does it work? • User spends approximately 30 - 60 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 Ted Rappaport, WNCG, Univ of Texas

32. Alternate Embodiments: Embedded Network– Centralized Hub, AP, Clients • 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 Ted Rappaport, WNCG, Univ of Texas

33. Embodiment of Embedded Network Software in Clients • 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 Ted Rappaport, WNCG, Univ of Texas

34. Association QOS in a Hybrid Environment AP2: Ch. 1, 30 mW 802.11g Client #1: Ch 1, -59 dBm 41 dB SIR 24 Mbps AP1: Ch. 6 1 mW 802.11g TV: Ch 6 -45 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. Ted Rappaport, WNCG, Univ of Texas

35. Association QOS in a Hybrid Environment Client #1: Ch 1, -42 dBm 28 dB SIR 1 Mbps AP1: Ch. 1 30 mW 802.11g AP2: Ch. 1 30 mW 802.11g 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. Ted Rappaport, WNCG, Univ of Texas

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 Ted Rappaport, WNCG, Univ of Texas

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 802.11b 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 1998. • [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. Ted Rappaport, WNCG, Univ of Texas

38. References (II) • [He03] M. Heusse et al. “Performance Anomaly of 802.11b”, INFOCOM 2003 • [Bi00] G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordinated Function,” IEEE JSAC, vol. 18, pp. 535-547, Mar. 2000 • [Ch03] P. Chatzimisios et al, “Influence of channel BER on IEEE 802.11 DCF,” Electronics Letters, vol. 39, no. 23, pp. 1687–1689, November 2003. • [Ga03] S. Garg et al, “An experimental study of throughput for UDP and VoIP traffic in IEEE 802.11b networks,” IEEE WCNC, 2003 • [Va02] A. Vasan et al, “An empirical characterization of instantaneous throughput in 802.11b WLANs,” U of Maryland tech report Ted Rappaport, WNCG, Univ of Texas