ICAbased Blind and GroupBlind Multiuser Detection - PowerPoint PPT Presentation

Title: ICAbased Blind and GroupBlind Multiuser Detection


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ICA-based Blind and Group-Blind Multiuser
Detection
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Independent Component Analysis(ICA)
What is Independence? Independence is much
stronger than Uncorrelated.
Definition
Uncorrelated
Independence
What is ICA ? Independent Component Analysis
(ICA) is an analysis technique where the goal is
to represent a set of random variables as a
linear transformation of statistically
independent component variables.
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Independent Component Analysis(ICA)

• ICA Model (Noise-free)

Unknown Mixing Matrix
Unknown Random Vector
are assumed independent

• ICA Model (Noise)

Noise
ICA Goal Find a Matrix which recovers

HOW?
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ICA Principles and Measures
Independence Nongaussian Want
to be one independent component Central Limit
Theorem
Differential entropy
Measures of Nongaussian 1. Kurtosis 2.
Negentropy and Approximation
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ICA Principles and Measures
Measures of Nongaussian (continued) 3. Mutual
information 4. Kullback-Leibler
divergence
Real density      
Factorized density
Kullback-Leibler divergence can be considered as
a kind of a distance between the two probability
densities, though it is not a real distance
measure because it is not symmetric
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Principle Component Analysis

• Principle Component Analysis
• 1. Goal is to identify a few variables that
  explain all (or nearly all) of the total
  variance.
• 2. Intended to narrow number of variables down
  to only those that are of importance.
• 3. Faithful in the Mean-Square sense. Faithful
  Interesting!

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Synchronous CDMA

• Received signal

• where
• bk Î -1,1 is the kth users transmitted bit.
• hk is the kth users channel coefficient
• sk(t) is the kth users waveform (code or PN
  sequence)
• n(t) is additive, white Gaussian noise.

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Blind Multi-user Detection

• Multiple Access Interference (MAI)
• Due to non-orthogonal of codes
• Caused by channel dispersion

• What does Blind Mean?
• Only the Interested users Spreading code is
  Known to the receiver
• Channel is Unknown

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Group-Blind MUD

• Multiple-Access Interference (MAI)
• Intra-cell interference users in same cell as
  desired user
• Inter-cell interference users from other cells
• Inter-cell interference 1/3 of total interference

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Blind Multi-User Detection

• Non-Blind multi-user detection
• Codes of all users known
• Cancels only intracell interference
• Blind multi-user detection
• Only code of desired user known
• Cancels both intra- and inter-cell interference

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Group-blind MUD

• users with known codes
• users with unknown codes
• Signal is sampled at chip rate (from matched
  filter)
• Cancels both intra- and inter-cell interference

Wnated Uniform Signal Model
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Synchronous Signal Model
Uniform Received Model
Chip Matched Filter
chip1
chip2
chip3

Discrete Model
Spreading Gain of is N
Synchronous!
Total Number of Users
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Sub-space Concept
Auto-correlation Matrix of Received Data
Auto-correlation Matrix (EVD)
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FastICA Challenges in CDMA

• Fixed-point algorithm for ICA (FastICA)
• Based on the Kurtosis minimization and
  maximization
• Two advantages
  
  
• 1. Neural network learning rule into a simple
  fixed-point iteration
• 2. Fast convergence speed Cubic

• Ambiguities
• Variance Undetermined variances (energies) of
  the independent components
• Order Undetermined order of the independent
  components.

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ICA in CDMAHints
Hints
ICA Model
Data whitening
Ignore noise
Blind MMSE Solution
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Two Questions
What we Know?
Question No.1
?
are Independent.
Not only Independent but also
1or-1with with equal probability!

• Question No.2
• FastICA Many Local local minima or maxima
  MMSE ICA Near MMSE local minima or
  maxima
• Finding a tradeoff between two objective
  functions.
• Can we find a better local minima or maxima which
  gives better performance by starting from other
  initial points?

?
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ICA-based Blind Detectors

• Question No.1

Lemma For a BPSK Synchronous DS-CDMA
system,the maximization of Approximated Negentroy
using high-order moments is same as the
minimization of the Kurtosis.
?
Further Work
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ICA-based Blind Detectors
Data Whitening
Question No.2
MMSEICA Detector
Initial Point for FastICA
Zero-Forcing ICA Detector
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Performance of Blind Detector
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Performance of Blind Detector
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Summary for Blind Detectors
Advantages
1. ICA-based blind detectors have better
performance than the subspace detectors in high
SNRs.
2. ZFICA Detector has better performance than
MMSEICA Detector. Reduced complexity and robust
to estimated length.
3. ICA-based blind detectors are free to BER
floor.
4. When system is high loaded the performance
of ZFICA is close the non-blind MMSE detector.
Disadvantages
1. ZFICA Detector needs know K
2. ICA-based blind detectorsless flexibility to
estimated length.
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Group-blind MUD Detector
What is the Magic? Make use of the signature
waveforms of all known users suppress the
intra-cell interference,while blindly suppressing
the inter-cell interference.
Group-blind Zero-Forcing Detector

• ICA-based group-blind detector
• 1. Non-blind MMSE (Partial MMSE) to eliminate
  the interference from the intra-cell users
• 2. Zero-Forcing ICA Detector based on output
  of Partial MMSE

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Performance of Group-blind Detectors
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Performance of Group-blind Detectors
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Summary for Group-blind Detectors
1. Group-blind ZFICA detector has better
performance than group- blind zero-forcing
subspace detector.
2. Group-blind ZFICA detector Worse performance
than the totally blind ZFICA method.
Partial MMSE Destroyed the Independence of
desired random variables. Independent gt
Interference!!
End
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Reference
References 1 J.Joutsensal and
T.Ristaniemi,Blind Multi-User Detection by Fast
Fixed Point Algorithm without Prior Knowledge of
Symbol-Level Timing, Proc. IEEE Signal
Processing Workshop on Higher Order Statistics
Ceasarea,Israel, June 1999,pp.305-308. 2
T.Ristaniemi and J.Joutsensal, Advanced
ICA-Based Receivers for DS-CDMA Systems, Proc.
11th IEEE International Symposium on Personal,
Indoor, and Mobile Radio Communications, London,
September 18-21, 2000, pp.276-281. 3
T.Ristaniemi,Synchronization and blind signal
processing in CDMA systems,Doctoral
Thesis,University of Jyvaskyla, Jyvaskyla
Studies in Computing, August 2000. 4 X.Wang and
A.Høst-Madsen, Group-blind multiuser detection
for uplink CDMA, IEEE Journal on Selec. Areas in
Commun, vol. 17, No. 11, Nov. 1999. 5 X. Wang
and H.V. Poor, Blind Equalization and Multiuser
Detection in Dis-persive CDMA Channels, IEEE
Transactions on Communications, vol. 46, no. 1,
pp. 91-103, January 1998. 6 P. Comon,
Independent Component Analysis, A new Concept?,
Signal processing, Vol.36, no.3, Special issue on
High-Order Statistics, Apr. 1994.
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Reference
References 7 A.Hyvarinen and E.Oja, A Fast
Fixed-Point Algorithm for Independent Component
Analysis, Neural Computation, 91483-1492,
1997. 8 A.Hyvarinen, Fast and Robust
Fixed-Point Algorithm for Independent Component
Analysis, IEEE Trans. on Neural Networks,
1999. 9 A.Hyvarinen, Survey on Independent
Component Analysis, Neural Com-puting Systems,
294-128, 1999. 10 S. Verdu, Multiuser
Detection. Cambridge, UK Cambridge Univ. Press,
1998.