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 1

2. Selected Research Projects SystemContributionSoftware release PrototypeTechnology transfer via DSLequalizationMatlabDSP/CStudents MIMO testbedLabVIEWLV/PXIContract Wimax/LTEresource alloc.LabVIEWDSP/CStudents Wimax/WiFiRFI mitigationMatlabLV/PXIStudents CameraacquisitionMatlabDSP/CStudents Displayimage halftoningMatlabCStudents Design automation fixed point conv.MatlabFPGAStudents dist. computing.Linux/C++Navy sonarStudents DSP Digital Signal Processor FPGA Field Programmable Gate Array LTE Long-Term Evolution (cellular) LV LabVIEW MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation

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

4. 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] 4

5. Radio Frequency Interference (RFI) 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 5

6. Poisson Field of Interferers 6 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)

7. Poisson-Poisson Cluster Field of Interferers 7 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

8. Fitting Measured Laptop RFI Data Statistical-physical models fit better than Gaussian 8 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 Platform RFI sources May not be Poisson distributed May not have identical emissions

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