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Overcoming Interference Limitations in Networked Systems

Overcoming Interference Limitations in Networked Systems. Prof. Brian L. Evans The University of Texas at Austin Cockrell School of Engineering Department of Electrical and Computer Engineering Wireless Networking and Communications Group. Selected Research Projects.

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Overcoming Interference Limitations in Networked Systems

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  1. Overcoming Interference Limitations in Networked Systems Prof. Brian L. Evans The University of Texas at Austin Cockrell School of Engineering Department of Electrical and Computer Engineering Wireless Networking and Communications Group

  2. Selected Research Projects DSP Digital Signal Processor FPGA Field Programmable Gate ArrayLTE Long-Term Evolution (cellular) LV LabVIEWMIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation

  3. Radio Frequency Interference 3 (WiMAX Basestation) (Microwave) (Wi-Fi) (Wi-Fi) (WiMAX) antenna (WiMAX Mobile) • Wireless Communication Sources • Closely located sources • Coexisting protocols Non-Communication Sources Electromagnetic radiations baseband processor (Bluetooth) • Computational Platform • Clocks, busses, processors • Co-located transceivers

  4. Radio Frequency Interference (RFI) 4 Limits wireless communication performance Impact of LCD noise on throughput for embedded WiFi (802.11g) receiver[Shi, Bettner, Chinn, Slattery & Dong, 2006]

  5. Radio Frequency Interference (RFI) 5 • Problem: Co-channel and adjacent channel interference, and computational platform noise degrade communication performance • Solution: Statistical modeling of RFI Listen to the environment Estimate parameters for statistical models Use parameters to mitigate RFI • Goal: Improve communication performance 10-100x reduction in bit error rate 10-100x increase in network throughput

  6. Poisson Field of Interferers 6 Middleton Class A (form of Gaussian Mixture Model) Symmetric Alpha Stable • Dense Wi-Fi networks • Networks with contention based medium access • Cellular networks • Hotspots (e.g. café) • Sensor networks • Ad hoc networks

  7. Poisson-Poisson Cluster Field of Interferers 7 Gaussian Mixture Model Symmetric Alpha Stable • In-cell and out-of-cell femtocell users in femtocell networks • Out-of-cell femtocell users in femtocell networks • Cluster of hotspots (e.g. marketplace)

  8. Fitting Measured Laptop RFI Data 8 • Radiated platform RFI • 25 RFI data sets from Intel • 50,000 samples at 100 MSPS • Laptop activity unknown to us • Smaller KL divergence • Closer match in distribution • Does not imply close match in tail probabilities • Platform RFI sources • May not be Poisson distributed • May not have identical emissions Statistical-physical models fit better than Gaussian

  9. Transceiver Design to Mitigate RFI 9 Guard zone RTS CTS Example: Wi-Fi networks RTS / CTS: Request / Clear to send Interference statistics similar to Case III Design receivers using knowledge of RFI statistics

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