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

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)