an experimental receiver design for diffuse ir channels based on wavelet analysis & artificial...
DESCRIPTION
An Experimental Receiver Design For Diffuse IR Channels Based on Wavelet Analysis & Artificial Intelligence. R J Dickenson and Z Ghassemlooy O ptical C ommunication R esearch G roup Sheffield Hallam University www.shu.ac.uk/ocr. Contents. Diffuse IR indoor multipath channel - PowerPoint PPT PresentationTRANSCRIPT
An Experimental Receiver DesignFor Diffuse IR Channels Based on
Wavelet Analysis & Artificial Intelligence
R J Dickenson and Z Ghassemlooy
Optical Communication Research GroupSheffield Hallam University
www.shu.ac.uk/ocr
Contents
• Diffuse IR indoor multipath channel• Compensating schemes• Traditional receivers• Wavelet and AI based receiver• Proposed receiver• Simulation results• Conclusions
Diffuse IR System - Major Performance Limiting Factors
Inter Symbol Interference
Noise Power Limitations
Tx Rx
Compensating Methods
Modulation Schemes– DH-PIM – DPIM – PPM
Diversity– Angle – Multi-beam
Tx
Rx Rx Rx
Rx Rx
Rx
Traditional Receiver Concepts
ZFE DFE Coding
- Block- Convolutional- Turbo
10-3
10-2
10-1
100
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
DT
Nor
mal
ised
opt
ical
pow
er re
quire
men
ts (d
B)
OOK-NRZ
32-DH-PIM2
32-DH-PIM1
32-DPIM
32-PPM
Normalised optical power requirements Vs. normalised delay spread for various modulation schemes
Alternative Techniques - Wavelet Analysis & Artificial Intelligence
De-noising Image Compression Earthquake Electrical Fault Detection Mechanical Plant Fault Prediction Apple Ripeness Communications
What Is A Wavelet?
Simple Description:
A finite duration waveform
Has an average value of zero
Is a basis function, just like a sine wave in Fourier analysis
Fourier Analysis And The Wavelet Transform
3 sine waves at different frequencies and times.
Frequency spectrum The peaks will remain statically
located regardless of where in time the frequencies occur
Fourier Analysis And The Wavelet Transform
Wavelet resultsIn the wavelet domain we have both a representation of frequency (scale), and also an indication of where the
frequency occurs in time.
Neural Networks
Loosely based on biological neuron
Neural networks come in many flavours
Used extensively as classifiers
Supervised and unsupervised learning
Input Layer
Hidden Layer 1
Hidden Layer 2
Output
Σ F
x 2
w 1 x 1
x n
w 2
w n
Out
Channel Model & Receiver Structure
• Input data format: OOK NRZ • Channel: Carruthers & Kahn Channel Model, with impulse
response of:
1 0 1 0... …1 0 1 0 Tx CHANNEL
NOISE
Rx Filter WAVELET ANALYSIS
NEURAL NETWORK
Feature Extraction
Pattern Recognition
Thresholder
Receiver
)(6),( 7
6
tuataath
where u(t) is the unit step function
Simulation Flow Chart
Incoming Data n bits long.
Low Pass Filter
Decimate Stream it to 5 Bit windows
CWT at 4 scales on every
window
Decimate each set of
coefficients to 100 sample
points
Pack samples into a 100xn
matrix
Offer each column to the
neuronal classifier
Threshold the output to 1 or 0
• ANN: - 4 layers with 176 neurons - 3 different activation functions, trained to detect the value of the centre bit from a 5 bit length window
• CWT:- 5 bit sliding window - coif1 mother wavelet- Operating scales of 60,
80, 100 and 120 using
Bit To Detect
5 Bit Window
Simulation Results – BER V. SNR
Data rate: 40 and 50 Mb/s Normalised delay spread: 0.44
and 0.55• for BER of 10-5 the wavelet-AI
scheme offers SNR improvement of:- ~ 8 dB at 40 Mbps - ~ 15 dB at 50 Mbps
over the filtered threshold scheme• For the wavelet-AI scheme the
penalty for increasing the data rate by 10 Mbps is ~ 5dB whilst it is around 15dB for the basic scheme.
Conclusions
A novel technique to combat multipath dispersion
Improvement of ~ 8 dB in SNR compared with the threshold based detection scheme
Promising results, however, significant further work is required.
Not intended to replace coding methods
Any Questions?
• Thank you for your kind attention. • I will attempt to answer any questions you
have.