1. Estimation and detection from coded signals
Overview of research at UGent (37) and cooperation with NEWCOM partners
Marc Moeneclaey, UGent - TELIN dept.

2. Outline Coding + linear modulation, AWGN channel
MAP detection * carrier phase and timing known * carrier phase and timing unknown
ML synchronization * Cramer-Rao bound, modified Cramer-Rao bound * expectation-maximization (EM) algorithm
Numerical results
Related research (channel estimation, OFDM, MIMO, CDMA, multipath fading, time-varying parameters, ...)
Cooperation with NEWCOM partners

3. Coding + linear modulation, AWGN channel
coded symbols
pulse h(t)
infobits
encoding, interleaving, mapping
r(t)
linear modulation
AWGN channel b a
received signal
q : carrier phase t : time delay
h(t) : square-root Nyquist pulse
b  a : turbo code, LDPC code, BICM, ....

4. MAP detection, (q, t) known (1/2)
MAP detection (on individual infobits) achieves minimum BER
r : vector representation of r(t)
Assuming (q, t) = (q0, t0) is known, APP is given by
(many terms !)
Summation over information sequences can be done efficiently (message-passing in factor graph of encoder+interleaver+mapper)

5. MAP detection, (q, t) known (2/2)
Receiver structure
zm(t0)
conventional MAP detector
r(t)
h*(-t) x
mT+t0
exp(-jqo)

6. MAP detection, (q, t) unknown
When (q, t) is unknown, APP is given by
 conventional MAP detector hard to compute, because of integration over (q, t)  we look for a (suboptimum) alternative

7. Synchronization : estimate of (q, t)
Strategy : use conventional MAP detector, but provide estimate (instead of correct value) of (q, t)  no integration over (q, t)
conventional MAP detector
r(t)
h*(-t) x
Synchronizer

8. ML synchronization ML estimation of (q, t) :
no integrations required, but sum contains many terms that are function of (q, t)  ML estimate hard to compute (2-D search)

9. Cramer-Rao lower bound (1/2)
Motivation for ML estimation : for long observation intervals, mean-square estimation error (MSEE) converges to Cramer-Rao lower bound (CRB) on MSEE
When the data symbols are not a priori known to the receiver, computation of the CRB is hard, especially for coded transmission. A simpler but less tight bound is the modified CRB (MCRB), which basically assumes that the data symbols are known to the receiver

10. Cramer-Rao lower bound (2/2)
QPSK
BPSK
(phase estimation)
Large SNR : CRB  MCRB Small SNR : CRBuncoded > CRBcoded > MCRB
 use code properties during estimation

11. Expectation-maximization algorithm (1/2)
Direct application of ML estimation is complicated.
ML estimate can be obtained iteratively by means of EM algorithm
soft symbol decision :
requires APPs of symbols (by-product of MAP detector)

12. Expectation-maximization algorithm (2/2)
conventional MAP detector
r(t)
h*(-t) x
soft decisions
Compute

13. Convergence behavior of EM algorithm
phase estimation error at i-th iteration :
negative zero-crossings :
stable
positive zero-crossings :
unstable

14. Convergence behavior of EM algorithm
phase estimation error at i-th iteration :
 initialization is critical
acquisition range

15. Initialization of EM algorithm (1/4)
DA initialization (pilot symbols) : - consumes power and bandwidth resources - does not exploit unknown data symbols (many pilot symbols needed)
NDA initialization (O&M for timing, V&V for phase) - gives rise to phase and timing ambiguities

16. Initialization of EM algorithm (2/4)
Proposed solution
- Different initializations, e.g.,
- Parallel EM algorithms with different initializations, each running for only one (or a few) iterations
- Continue with the EM algorithm that gave the largest maximum in the maximization step after one (or a few) iterations

17. Initialization of EM algorithm (3/4)
Example 1 : phase estimation, QPSK (perfect ambiguity resolution)
other equil. points

18. Initialization of EM algorithm (4/4)
Example 2 : phase estimation and ambiguity resolution, QPSK
other equil. points

19. Numerical results : exploiting code properties
partial (or no) exploitation of code properties  degradation of MSE
does not exploit code properties
uses infobit APPs only, assumes parity bits are uncoded
uses infobit APPs and parity bit APPs

20. Numerical results : phase estimation (1/2)

21. Numerical results : phase estimation (2/2)

22. Numerical results : phase estimation + ambiguity resolution

23. Numerical results : timing estimation (1/2)

24. Numerical results : timing estimation (1/2)

25. Numerical results : timing estimation + frame synchronization

26. Related research at UGent
Extension of phase and timing estimation from linear modulation transmitted over AWGN channel to the following :
Carrier frequency estimation and channel estimation
Multiuser CDMA, OFDM, UWB
Multipath fading channel, MIMO channel
Multidimensional mapping + optimization
Estimation of time-vaying parameters by means of iterative feedback algorithm
etc.

27. Cooperation within NEWCOM (1/3)
joint papers on EM-based parameter estimation
EURASIP JWCN paper accepted UGent + UCL(Vandendorpe) + UoP(Luise)
ISSSTA’04 UCL(Vandendorpe) + UGent
IEEE Trans.Comm. paper submitted UCL(Vandendorpe) + UGent
Lecture notes in Computer Science, 2004 ETH(Loeliger)+UGent

28. Cooperation within NEWCOM (2/3)
joint papers on estimation of time-varying parameters
EUSIPCO 2005 paper submitted ISIK(Panayirci) + UGent
Globecom 2005 paper submitted ISIK(Panayirci) + UGent

29. Cooperation within NEWCOM (3/3)
UGent would like to
Continue existing cooperations
Enter new cooperations
Broad topic : “estimation and detection from coded signals”
Type of cooperation :
Joint publications
Joint research projects
etc.

30. Joint publications (1/2)
1) C. Herzet, H. Wymeersch, L. Vandendorpe and M. Moeneclaey , "On Maximum-Likelihood Timing Estimation", Submitted to IEEE Transactions on Communications. Joint UCL(038) - UGent (037) contribution
2) N. Noels, V. Lottici, A. Dejonghe, H. Steendam, M. Moeneclaey, M. Luise , L. Vandendorpe, "A Theoretical Framework for Soft Information Based Synchronization in Iterative (Turbo) Receivers”, accepted by EURASIP Journal on Wireless Communications and Networking Joint UGent(37)-UCL (38)- UoP (13) contribution
3) Ramon V., Herzet C., Vandendorpe L. and Moeneclaey M. , "EM algorithm-based
estimation of amplitude, carrier phase and noise variance in multiuser turbo receivers", Proc. Eighth International Symposium on Spread Spectrum Techniques and Applications, 2004, ISSSTA04, Sydney, Australia, Aug 30 - Sep , pp Joint UCL (038)-UGent (037) contribution

31. Joint publications (2/2)
4) J. Dauwels, H. Wymeersch, H.-A. Loeliger and M. Moeneclaey "Phase Estimation and
Phase Ambiguity Resolution by Message Passing", Telecommunications and Networking,
Lecture Notes in Computer Science, vol. 3124, pp , Springer-Verlag, Berlin, Joint ETH(26)- UGENT(37) contribution
5) E. Panayirci, H. A. Cirpan and M.Moeneclaey, ”A Sequential Monte Carlo Method for Blind Phase Noise Estimation and Data Detection”, submitted to EUSIPCO 2005 Joint ISIK(07)-UGENT(37) contribution
6) E. Panayirci, H. A. Cirpan, M. Moeneclaey and N. Noels "Blind Data Detection in the presence of PLL phase noise by sequential Monte Carlo method”, submitted to Globecom’05
Joint ISIK(07)-UGENT(37) contribution