Transcript UTILIZING CHANNEL CODING INFORMATION IN CIVA

UTILIZING CHANNEL CODING
INFORMATION IN CIVABASED BLIND SEQUENCE
DETECTORS
Xiaohua(Edward) Li
Department of Electrical and Computer Engineering
State University of New York at Binghamton
[email protected],
http://ucesp.ee.binghamton.edu/~xli
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Contents
I.
II.
III.
IV.
V.
Introduction
System models
Joint CIVA and channel decoding
Simulations
Conclusions
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I Introduction
• Problems in blind equalization
– Not robust to ill-conditioned channels
– Local convergence, slow convergence
(SISO methods)
– Can not resolve common roots among
sub-channels (SIMO methods)
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• CIVA (channel independent Viterbi
algorithm) [1]: effective blind equalizer
– Robust to all channel conditions
– Superior near MLSE performance
– Fast convergence
• Propose: CIVA/decoder
– Integrate convolutional decoder in CIVA
– Use channel coding information to
reduce complexity, save hardware
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II System Model
 SystemDescription
Conv olutionalencoder( K , N ) : c n  D  b n
1  1
PAMmapping: 
, bits cn  symbols sn
0   1
xn  hT s n  vn
Equalizer/decoderoutput: bˆn  bn
FIR channelresponse:
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• Vector model
X( n)  H S( n)  V( n)  H D  B( n)PAM  V( n)
 xn
 xn  P 


Receiv edsamples: X( n )   
 
 xn  M  L  xn  L 1 
s




Transmitted binary digits: bn , , bn  Lb 1  B( n )
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III CIVA/Decoder
• Blind sequence detection
 DesignprobesGi :
f [Xi , Gl ]   i l
 Find probesequence:
{G( n )}  arg min  f [ X( n ), G( n )]
n
 Determinesymbolsequence{bn } from{G( n )}
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• Implement as trellis search
– Metric updating rule
 j (n)  mini {i (n  1)  f [X(n), Gl ]}
– Example of trellis and probe
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• CIVA/Decoder Properties
– Have the advantages of both CIVA and
channel decoder
– Reduce complexity and hardware
Trellis constraint length Compare:
conv olutional encoder: Lc
CIVA : Ls
Ls
CIVA/decoder : (  1)K  Lc
N
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IV Simulations
• Convolutional
encoder: rate ½
• Channel length: 3
• CIVA/Decoder: 32
states
• CIVA: 128 states
• CIVA/decoder
performs better with
reduced complexity
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Random 3-tap channels. BPSK. 400 samples.
Compare:
• CIVA: blind
CIVA/decoder
• MLSE:
optimal
• VA: training
• MMSE:
training
• PSP: with
decision
feedback
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V Conclusions
• CIVA/Decoder: new blind equalizer, integrate
convolutional decoding in the channelindependent Viterbi algorithm
– Superior near MLSE performance
– Robust to even ill-conditioned channels
– Reduced complexity and hardware
• Reference:
– X. Li, “Blind sequence detection without channel estimation,” to
appear in IEEE Trans. Signal Processing. A part appears in the
35th Asilomar Conf. Signals, Syst., Comput., Oct 2001.
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