Overcoming Interference Limitations in Networked Systems

Prof. Brian L. EvansThe University of Texas at Austin

Cockrell School of EngineeringDepartment of Electrical and Computer Engineering

Wireless Networking and Communications Group

1

Selected Research ProjectsSystem Contribution Software

releasePrototype Technology

transfer viaDSL equalization Matlab DSP/C Students

MIMO testbed LabVIEW LV/PXI ContractWimax/LTE resource alloc. LabVIEW DSP/C StudentsWimax/WiFi RFI mitigation Matlab LV/PXI StudentsCamera acquisition Matlab DSP/C StudentsDisplay image halftoning Matlab C StudentsDesign automation

fixed point conv. Matlab FPGA Studentsdist. computing. Linux/C++ Navy sonar Students

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

Radio Frequency Interference3

Wireless Communication Sources

•Closely located sources•Coexisting protocols

Non-CommunicationSources

Electromagnetic radiations

Computational Platform• Clocks, busses, processors• Co-located transceivers

antenna

baseband processor

(Wi-Fi)

(WiMAX Basestation)

(WiMAX Mobile)

(Bluetooth)

(Microwave)

(Wi-Fi) (WiMAX)

Radio Frequency Interference (RFI)

• Limits wireless communication performance• Impact of LCD noise on throughput for embedded

WiFi (802.11g) receiver [Shi, Bettner, Chinn, Slattery & Dong, 2006]

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Radio Frequency Interference (RFI)

• Problem: Co-channel and adjacent channel interference, and computational platform noise degrade communication performance

• Solution: Statistical modeling of RFIListen to the environmentEstimate parameters for statistical modelsUse parameters to mitigate RFI

• Goal: Improve communication performance10-100x reduction in bit error rate10-100x increase in network throughput

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Poisson Field of Interferers6

• Cellular networks• Hotspots (e.g. café)

• Sensor networks• Ad hoc networks

• Dense Wi-Fi networks• Networks with contention

based medium access

Symmetric Alpha Stable Middleton Class A (form of Gaussian Mixture Model)

Poisson-Poisson Cluster Field of Interferers7

• Cluster of hotspots (e.g. marketplace)

• In-cell and out-of-cell femtocell users in femtocell networks

• Out-of-cell femtocell users in femtocell networks

Symmetric Alpha Stable Gaussian Mixture Model

Fitting Measured Laptop RFI Data• Statistical-physical models fit better than Gaussian

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Smaller KL divergence•Closer match in distribution•Does not imply close match in tail probabilities

Radiated platform RFI• 25 RFI data sets from Intel• 50,000 samples at 100 MSPS• Laptop activity unknown to us

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Measurement Set

Kul

lbac

k-Le

ible

r di

verg

ence

Symmetric Alpha StableMiddleton Class AGaussian Mixture ModelGaussian

Platform RFI sources• May not be Poisson distributed• May not have identical

emissions

Transceiver Design to Mitigate RFI9

RTS

CTS

Example: Wi-Fi networksRTS / CTS: Request / Clear to send Interference statistics similar to Case III

Guard zone

• Design receivers using knowledge of RFI statistics