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Per-survivor Based Detection ofDPSK Modulated High Rate Turbo Codes
Over Rayleigh Fading Channels
Bin Zhao and Matthew C. Valenti
Lane Dept. of Comp. Sci. & Elect. Eng.
West Virginia University
Morgantown, WV
This work funded by the Office of Naval Research under grant N00014-00-0655
Outline of Talk
Background– Iterative channel estimation and decoding.– Turbo DPSK (Hoeher & Lodge).
“Extended” turbo DPSK – Replace code in turbo DPSK with turbo code.– Analytical tool to predict location of “waterfall”.– Performance in AWGN and fading with perfect CSI– Performance in unknown fading channels using
PSP-based processing. Conclusions
Iterative Channel Estimation
Pilot-symbol filtering techniques:– Valenti and Woerner – “Iterative channel estimation and decoding of
pilot symbol assisted turbo codes over flat-fading channels,” JSAC, Sept. 2001.
– Li and Georghiades, “An iterative receiver for turbo-coded pilot-symbol assisted modulation in fading channels,” Comm. Letters, April 2001.
Trellis-based techniques:– Komninakis and Wesel, “Joint iterative channel estimation and
decoding in flat correlated Rayleigh fading channels,” JSAC, Sept. 2001.
– Hoeher and Lodge, “Turbo DPSK: Iterative differential PSK demodulation and channel decoding,” Trans. Comm., June 1999.
– Colavolpe, Ferrari, and Raheli, “Noncoherent iterative (turbo) decoding,” Trans. Comm., Sept. 2000.
Turbo DPSK Structure
From Hoeher/Lodge. K=6 convolutional code. Block interleaver: 20 frames. Trellis-based APP demodulation of DPSK with perfect CSI. In flat fading channels, per-survivor processing and linear
prediction are applied to estimate the channel information. Iterative decoding and APP demodulation.
RSC channel
Interleaver D P S K
Channel
APP Demod Deint
extrinsic infomation
convolutional decoder
channel Interleaver
input
+ -
- +
output
APP Demodulator for DPSK
Can use BCJR algorithm to coherently detect trellis-based DPSK modulation.– Only 2 state trellis when perfect CSI available.
With unknown CSI apply linear prediction and per-survivor processing to estimate the channel information.– Requires an expansion of the DPSK code-trellis.– Complexity of APP demodulator is exponentially
proportional to the order of linear prediction.– PSP algorithm must be modified to produce soft-outputs.
Construction of Super-Trellis
Use a sliding window to combine multiple adjacent stages of simple DPSK trellis to construct the super-trellis of APP demodulator.
Number of adjacent stages equals the order of the linear predictor.
Complexity of super-trellis is exponentially proportional to the order of linear prediction.
S0
S1
0
1
0
0
1
0
Window 1Window 2
S2
S3
S2
S3
1
0
S0
S1
S0
S1
0
1
0
0
Branch Metric of APP Demodulation in Correlated Fading Channel with PSP
k k
k
k k n k n k n
N
n n n
N k
k k n k n k n
N
n n n
N k
b
z
y b p y b
p ra
y b p y b
p ra
( )~
~ ~
~
~ ~
~
R
S
|||||
T
|||||
1
2
2
1
1
2
2
1
2 1
2 1
= 1
= 0
Channel LLR y and estimated channel input Prediction coefficient and Gaussian noise Prediction residue
pn
bk n
~
2 2 n
11
p rn n
N
Code polynomials (1,23/35) UMTS interleaver for turbo
code. Rate compatible puncturing
pattern. Block channel interleaver. Per-survivor based APP
demodulation for correlated fading channels.
Iterative decoding and demodulation.
RSC
RSC turbo interleaver
Turbo Encoder
PUNT
Channel Interleaver
DPSK
Channel
APP Demod Deint
extrinsic infomation
Turbo decoder
Channel Interleaver
+ - -
+
Extended Turbo DPSK Structure
-6 -4 -2 0 2 4 6 810
-7
10-6
10-5
10-4
10-3
10-2
10-1
100
Performance in AWGN Channel with Perfect CSI
Es/No in dB
BE
R
extended turbo DPSKturbo code (coherent BPSK)
1/3
4/7 4/58/9
2.5 dB1 dB
Framesize 1024 bits The energy gap between
turbo code and extended turbo DPSK:
The energy gap decreases as the rate increases except for the rate 8/9 case.
– Why?
Rate Energy Gap
8/9 2 dB
4/5 1 dB
4/7 1.5 dB
1/3 2.5 dB
Analytical Tool: Convergence Box Similar to the “tunnel theory”
analysis.– S. Ten Brink, 1999.
Suppose Turbo decoder and APP demodulator ideally transform input Es/No into output Es/No.
– APP demodulator • DPSK BPSK
– Turbo code decoder • Turbo Code BPSK
Convergence box shows minimum SNR required for converge.
– corresponds to the threshold SNR in the tunnel theory.
convergence box location:
rate Es/No Eb/No
1/2 0.5 dB 3.5 dB
1/3 -1.3 dB 3.5 dB-6 -4 -2 0 2 4 6 8Es/No in dB
10-3
10-2
10-1
100
BE
R
coherentDPSK
BPSK
1 iteration
10 iterations
r =⅓turbo code
Performance in Fading Channel:r = 4/5 case
BT=0.01 Block interleaver
improves the performance of turbo code by about 1.5 dB.
With perfect CSI, the energy gap between turbo code and extended turbo DPSK is 3 dB.
For extended turbo DPSK, differential detection works better than per-survivor based detection
Reason A: 1 local iteration of turbo decoding is sub-optimal.
Reason B: the punctured outer turbo code is too weak.
Performance in Fading Channel: r = 1/3 case
Per-survivor based detection loses about 1 dB to perfect CSI case.
Per-survivor based detection has 1 dB gain over extended turbo DPSK with differential detection.
Increasing the trellis size of APP demodulator provides a decreasing marginal benefit.
Performance in Fading Channel: r = 4/7 case
With perfect CSI, the energy gap between turbo code and extended turbo DPSK is around 2.5 dB.
Per-survivor based detection loses about 1 dB to perfect CSI case.
Per-survivor based detection has 1 dB gain over extended turbo DPSK with differential detection.
Increasing the trellis size of APP demodulator provides a decreasing marginal benefit.
Conclusions
“Extended turbo” DPSK = turbo code + DPSK modulation.– Performs worse than turbo codes with BPSK modulation and
coherent detection.– However, the gap in performance depends on code rate.– Large gap if code rate too low or too high. – “Convergence box” predicts performance.
Extended turbo DPSK suitable for PSP-based detection.– PSP about 1 dB worse than extended DPSK with perfect CSI.– For moderate code rates, PSP is 1 dB better than differential
detection.– However, if code rate too high, PSP can be worse than diff. detection.
• Performance can be improved by executing multiple local iterations of turbo decoding per global iteration (future work).
Future Work
Search for optimal puncturing patterns for extended turbo DPSK. Search for a better modulation structure for turbo codes with a
convergence region comparable or even better than that of BPSK modulated turbo codes.
Further develop analytical tools that leverage the concepts of Gaussian density evolution and convergence boxes of extended turbo DPSK in the error-cliff region.