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學生： 周信宏

Maximum-Likelihood Synchronization and Channel Estimation for OFDMA Uplink Transmissions Pun, M.-O.; Morelli, M.; Kuo, C.-C.J.; Communication, IEEE Transactions on. 學生： 周信宏. Outline. Introduction S ignal models for OFDMA uplink transmissions CFO Estimation

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學生： 周信宏

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  1. Maximum-Likelihood Synchronization and Channel Estimation for OFDMA Uplink TransmissionsPun, M.-O.; Morelli, M.; Kuo, C.-C.J.;Communication, IEEE Transactions on 學生： 周信宏

  2. Outline • Introduction • Signalmodelsfor OFDMA uplinktransmissions • CFO Estimation • Estimationofthetimingoffsets and channel responses • Simulationresults • Conclusion • References

  3. Introduction • The exact maximum-likelihood (ML) solution to this problem is prohibitively complex since it demands a search over a multidimensional space. • We resort to the alternating-projection algorithm and propose a simpler scheme, in which CFOs are estimated sequentially instead of jointly.

  4. Signalmodelsfor OFDMA uplinktransmissions

  5. Signalmodelsfor OFDMA uplinktransmissions

  6. Signalmodelsfor OFDMA uplinktransmissions

  7. CFO Estimation • A. ML Estimation the log-likelihood function for the unknown parameters ω and ξ takes the form

  8. CFO Estimation Then, we see that is maximum when

  9. CFO Estimation • B. Iterative Estimation via Alternating Projection Based on alternating projection techniques, the multidimensional maximization problem in is split into a series of simpler 1-D searches.

  10. CFO Estimation The resulting procedure consists of cycles and steps. A cycle is made of K steps. whereis used to indicate the functional dependence of on

  11. CFO Estimation • C. Alternating-Projection Frequency Estimator (APFE) Computing requires a matrix inversion of order KNg for each value of

  12. CFO Estimation

  13. CFO Estimation Note that computing demands the inversion of a matrix of dimension Ng×Ng, which is significantly smaller than that required to compute .

  14. CFO Estimation • D. Approximate APFE (AAPFE) Inspection of (20) reveals that computing still requires a matrix inversion of dimension for each value of . Sinceis independent of , we can approximate the quantity using the von Neumann series truncated to the Mth order term.

  15. CFO Estimation • This leads to a significant reduction of the system complexity, since we only need one matrix inversion for each user, irrespective of the cycle number.

  16. Estimationofthetimingoffsets and channel responses Resort to an ML-based scheme and exploit the CFO estimate provided by either APFE or AAPFE. The ML estimates μof h' and could be obtained by searching for the minimum of

  17. Estimationofthetimingoffsets and channel responses First minimizewith respect to , which leads to Then, substituting (27) back into (26) and minimizing with respect to , we obtain

  18. Estimationofthetimingoffsets and channel responses • The multidimensional maximization problem in (28) can be efficiently solved by resorting to the alternating-projection technique. This results in a scheme called the alternating-projection timing estimator (APTE). • After is computed using APTE, it is employed in (27) to estimate the users’ channel responses as

  19. Simulationresults N=128 CP=28 modulation：QPSK Kmax=N/Nk=2、3、4 CFO=[ -0.32,0.32] channel：exponential channel

  20. Simulationresults • Case 1: Convergence Rate and Acquisition Range of the CFO Estimators

  21. Simulationresults

  22. Simulationresults • Case 2: System Performance in the Presence of Two Users With Equal Power

  23. Simulationresults

  24. Simulationresults • Case 3: Impact of K on the Accuracy of the CFO Estimates

  25. Conclusion • The frequency estimates were obtained using an ML approach, and the alternating-projection algorithm was employed to circumvent the maximization of the likelihood function over a multidimensional space. • The proposed schemes exhibit improved performance and provide more flexibility, as they can be used in conjunction with any subcarrier allocation strategy. It is fair to say that the above advantages are obtained at the price of a certain increase of the system complexity.

  26. References • M.-O. Pun; S.-H. Tsai ; C.-C.J. Kuo , “Joint maximum likelihood estimation of carrier frequency offset and channel in uplink OFDMA systems” in Proc. IEEE Global Telecommun. Conf. 2004 GLOBECOM’04 , Vol. 6, 29 Nov.~3 Dec. 2004 , pp. 3748-3752 • M.O. Pun ; C.C.J. Kuo ; M. Morelli , “Joint synchronization ans channel estimation in uplink OFDMA systems” in Proc. IEEE Int. Conf. on Acoust., Speech , and Signal Process.2005 ICASSP’05, Vol. 3, 18-23 March 2005, pp. iiV857-iiV860

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