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
Selected Research Projects
System
Contribution
Software
release
Prototype
Technology
transfer via
DSL
equalization
Matlab
DSP/C
Students
MIMO testbed
LabVIEW
LV/PXI
Contract
Wimax/LTE
resource alloc.
LabVIEW
DSP/C
Students
Wimax/WiFi
RFI mitigation
Matlab
LV/PXI
Students
Camera
acquisition
Matlab
DSP/C
Students
Display
image halftoning
Matlab
C
Students
Design
automation
fixed point conv.
Matlab
FPGA
Students
dist. computing
.
Linux/C++
Navy sonar
Students
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
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)
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
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
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)
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
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
0
5
10
15
20
25
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Measurement Set
Kullback-Leibler divergence
Symmetric Alpha Stable
Middleton Class A
Gaussian Mixture Model
Gaussian
Platform RFI sources
•
May not be Poisson distributed
•
May not have identical
emissions
Transceiver Design to Mitigate RFI
9
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
Physical Layer
(this talk)
•
Receiver pre
-
filtering
•
R散敩e敲e摥t散瑩潮
•
F潲w慲搠敲e潲潲o散瑩潮
Medium
Access Control Layer
•
Interference sense and avoid
•
佰瑩浩m攠杵慲d
z潮o 獩s攠
⡥(朮⁴ 慮a浩m⁰ w敲ec潮瑲潬)
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