1. 24.01.2011 | Anja Sohl Pilot Assisted and Semiblind Channel Estimation for Interleaved and Block-Interleaved Frequency Division Multiple Access Anja Sohl

2. 24.01.2011 | Anja Sohl Future Mobile Radio Systems Mobile User 2 Mobile User 1 Base Station 1 Provide high performance transmission in order to support demanding applications

3. 24.01.2011 | Anja Sohl Future Mobile Radio Systems Mobile User 2 Mobile User 1 Base Station High frequency diversity Low Peak-to-Average Power Ratio... DFT-precoded OFDMA Promising Multiple Access Schemes for uplink transmission: Interleaved Frequency Division Multiple Access (IFDMA) Block-Interleaved Frequency Division Multiple Access (B-IFDMA) 1 Requirements for Multiple Access Schemes: Provide high performance transmission in order to support demanding applications

4. 24.01.2011 | Anja Sohl Future Mobile Radio Systems Mobile User 2 Mobile User 1 Base Station High frequency diversity Low Peak-to-Average Power Ratio... DFT-precoded OFDMA Promising Multiple Access Schemes for uplink transmission: Interleaved Frequency Division Multiple Access (IFDMA) Block-Interleaved Frequency Division Multiple Access (B-IFDMA) 1 Requirements for Multiple Access Schemes: Provide high performance transmission in order to support demanding applications

5. 24.01.2011 | Anja Sohl Outline 1.IFDMA System Model 2.Pilot Assisted Channel Estimation for IFDMA 3.Semiblind Channel Estimation for IFDMA i.Reduction of Pilot Symbols in Frequency Domain ii.Reduction of Pilot Symbols in Time Domain 4. Summary and Conclusion

6. 24.01.2011 | Anja Sohl Outline 1.IFDMA System Model 2.Pilot Assisted Channel Estimation for IFDMA 3.Semiblind Channel Estimation for IFDMA i.Reduction of Pilot Symbols in Frequency Domain ii.Reduction of Pilot Symbols in Time Domain 4. Summary and Conclusion

7. 24.01.2011 | Anja Sohl 2 1.IFDMA System Model Signal Generation in Frequency Domain IFDMA symbol at time instant k with cyclic prefix Allocation of Q equidistant subcarriers out of N subcarriers in total Q data symbols Q -point DFT N -point IDFT MAP cyclic prefix DFT of data symbols … Total bandwidth L=N/Q

8. 24.01.2011 | Anja Sohl 3 1.IFDMA System Model Signal Generation in Time Domain IFDMA symbol at time instant k with cyclic prefix Q data symbols compression by factor L phase shift L - fold repetition cyclic prefix

9. 24.01.2011 | Anja Sohl 3 1.IFDMA System Model Signal Generation in Time Domain IFDMA symbol at time instant k with cyclic prefix Q data symbols compression by factor L phase shift L - fold repetition cyclic prefix

10. 24.01.2011 | Anja Sohl 3 1.IFDMA System Model Signal Generation in Time Domain IFDMA symbol at time instant k with cyclic prefix Q data symbols compression by factor L phase shift L - fold repetition cyclic prefix

11. 24.01.2011 | Anja Sohl 3 1.IFDMA System Model Signal Generation in Time Domain IFDMA symbol at time instant k with cyclic prefix Q data symbols compression by factor L phase shift L - fold repetition cyclic prefix compressed data symbols

12. 24.01.2011 | Anja Sohl 3 1.IFDMA System Model Signal Generation in Time Domain IFDMA symbol at time instant k with cyclic prefix Q data symbols compression by factor L phase shift L - fold repetition cyclic prefix compressed data symbols user specific phase shift

13. 24.01.2011 | Anja Sohl 3 1.IFDMA System Model Signal Generation in Time Domain IFDMA symbol at time instant k with cyclic prefix Q data symbols compression by factor L phase shift L - fold repetition cyclic prefix compressed data symbols L+L G –times time duration T S user specific phase shift

14. 24.01.2011 | Anja Sohl 4 1.IFDMA System Model Signal Properties … + Low Peak-to-Average Power Ratio (PAPR) + Efficient implementation for signal generation compressed data symbols Time Domain user specific phase shift

15. 24.01.2011 | Anja Sohl DFT of data symbols … + Low Peak-to-Average Power Ratio (PAPR) + Efficient implementation for signal generation compressed data symbols Time Domain Frequency Domain + High frequency diversity But, in many cases: - For pilot assisted channel estimation, each subcarrier has to be used for pilot transmission user specific phase shift … Total bandwidth 4 1.IFDMA System Model Signal Properties

16. 24.01.2011 | Anja Sohl 1.IFDMA System Model Signal Properties IFDMA-signal depicted on a grid in time and frequency 5

17. 24.01.2011 | Anja Sohl 1.IFDMA System Model Signal Properties IFDMA-signal depicted on a grid in time and frequency User separation by TDMA Possibility of entering a micro sleep mode and achieve energy savings if K. T s is small 5

18. 24.01.2011 | Anja Sohl 6 1.IFDMA System Model Transmission over Mobile Radio Channel … CPDATA CP DATA channel impulse response vector

19. 24.01.2011 | Anja Sohl 1.IFDMA System Model Transmission over Mobile Radio Channel Each allocated subcarrier experiences a flat fading channel represented by a complex factor Channel transfer factors vary with frequency and time 7

20. 24.01.2011 | Anja Sohl 1.IFDMA System Model Transmission over Mobile Radio Channel Each allocated subcarrier experiences a flat fading channel represented by a complex factor Channel transfer factors vary with frequency and time The channel transfer factors shall be estimated for each of the Q allocated subcarriers each of the K IFDMA symbols 7

21. 24.01.2011 | Anja Sohl Outline 1.IFDMA System Model 2.Pilot Assisted Channel Estimation for IFDMA 3.Semiblind Channel Estimation for IFDMA i.Reduction of Pilot Symbols in Frequency Domain ii.Reduction of Pilot Symbols in Time Domain 4. Summary and Conclusion

22. 24.01.2011 | Anja Sohl 8 2.Pilot Assisted Channel Estimation Introduction Pilot symbols are inserted at the transmitter in order to estimate the channel at the receiver

23. 24.01.2011 | Anja Sohl No. of inserted pilot symbols shall be kept small 8 2.Pilot Assisted Channel Estimation Introduction Pilot symbols are inserted at the transmitter in order to estimate the channel at the receiver Low PAPR shall be maintained Requirements:

24. 24.01.2011 | Anja Sohl No. of inserted pilot symbols shall be kept small 8 2.Pilot Assisted Channel Estimation Introduction Pilot symbols are inserted at the transmitter in order to estimate the channel at the receiver Low PAPR shall be maintained Requirements: Symbolwise pilot insertion Subcarrierwise pilot insertion Chipwise pilot insertion

25. 24.01.2011 | Anja Sohl No. of inserted pilot symbols shall be kept small 8 2.Pilot Assisted Channel Estimation Introduction Pilot symbols are inserted at the transmitter in order to estimate the channel at the receiver Low PAPR shall be maintained Requirements: Symbolwise pilot insertion Subcarrierwise pilot insertion Chipwise pilot insertion

26. 24.01.2011 | Anja Sohl Subcarrierwise Pilot Insertion 9 2.Pilot Assisted Channel Estimation Pilot Insertion & Estimation of Channel Variations DFT N -point IDFT Data MAP cyclic prefix DFT Pilot MAP pilots data

27. 24.01.2011 | Anja Sohl Subcarrierwise Pilot Insertion 9 2.Pilot Assisted Channel Estimation Pilot Insertion & Estimation of Channel Variations Interpolation depth I =2 DFT N -point IDFT Data MAP cyclic prefix DFT Pilot MAP pilots data

28. 24.01.2011 | Anja Sohl Subcarrierwise Pilot Insertion 9 2.Pilot Assisted Channel Estimation Pilot Insertion & Estimation of Channel Variations Interpolation depth I =2 DFT N -point IDFT Data MAP cyclic prefix DFT Pilot MAP pilots data Wiener interpolation Least squares + Wiener interpolation / DFT interpolation

29. 24.01.2011 | Anja Sohl Subcarrierwise Pilot Insertion 9 2.Pilot Assisted Channel Estimation Pilot Insertion & Estimation of Channel Variations Interpolation depth I =2 DFT N -point IDFT Data MAP cyclic prefix DFT Pilot MAP pilots data Wiener interpolation Least squares + Wiener interpolation / DFT interpolation Fulfill sampling theorem in FD: at least one pilot per coherence bandwidth, i.e., O F =1 Fulfill sampling theorem in TD: at least one pilot per coherence time, i.e., O T =1

30. 24.01.2011 | Anja Sohl Analysis Assumptions 2.Pilot Assisted Channel Estimation Performance Analysis 10 ModulationQPSK Bandwidth No. of subcarriers Carrier frequency Subcarrier spacing Cyclic prefix duration No. of IFDMA symbols per TDMA slot ChannelWINNER SCM Channel scenarioUrban macro-cell Coherence bandwidth Pilot sequenceCAZAC

31. 24.01.2011 | Anja Sohl 2.Pilot Assisted Channel Estimation Performance Analysis Pilot Symbol Overhead Energy spent for pilot transmission remains unused for data transmission Overhead is represented as Signal- to-Noise Ratio (SNR) degradation in dB 11

32. 24.01.2011 | Anja Sohl 2.Pilot Assisted Channel Estimation Performance Analysis Pilot Symbol Overhead Energy spent for pilot transmission remains unused for data transmission Overhead is represented as Signal- to-Noise Ratio (SNR) degradation in dB 11 Total no. of transmitted symbols No. of payload symbols

33. 24.01.2011 | Anja Sohl No. Q of subcarriers per user 12 2.Pilot Assisted Channel Estimation Performance Analysis Pilot Symbol Overhead Overhead in dB M=8 M=128 Symbolwise Subcarrierwise O F =1 Subcarrierwise O F =2 Subcarrierwise O F =4 M - no. of channel delay taps

34. 24.01.2011 | Anja Sohl No. Q of subcarriers per user 12 2.Pilot Assisted Channel Estimation Performance Analysis Pilot Symbol Overhead Overhead in dB M=8 M=128 Symbolwise Subcarrierwise O F =1 Subcarrierwise O F =2 Subcarrierwise O F =4 M - no. of channel delay taps

35. 24.01.2011 | Anja Sohl O F =4 I=1 2.Pilot Assisted Channel Estimation Performance Analysis Mean Square Error (MSE) O F =2 I=2O F =1 I=4 13 for Q=512

36. 24.01.2011 | Anja Sohl 14 2.Pilot Assisted Channel Estimation Performance Analysis Mean Square Error (MSE) MSE E B /N 0 in dB * Subcarrierwise + Wiener interpolation Subcarrierwise + DFT interpolation Symbolwise O F =1 O F =2 O T =6 V =28 km/h O F =4

37. 24.01.2011 | Anja Sohl 2.Pilot Assisted Channel Estimation Performance Analysis Mean Square Error (MSE) MSE E B /N 0 in dB * Subcarrierwise + Wiener interpolation Subcarrierwise + DFT interpolation Symbolwise O F =1 O F =2 15 O T =2 V =84 km/h O F =4

38. 24.01.2011 | Anja Sohl Outline 1.IFDMA System Model 2.Pilot Assisted Channel Estimation for IFDMA 3.Semiblind Channel Estimation for IFDMA i.Reduction of Pilot Symbols in Frequency Domain ii.Reduction of Pilot Symbols in Time Domain 4. Summary and Conclusion

39. 24.01.2011 | Anja Sohl 16 3.Semiblind Channel Estimation Introduction in general, larger than coherence bandwidth of channel FD: each allocated subcarrier used for pilot transmission TD:two pilot carrying IFDMA symbols Pilot Assisted Channel Estimation

40. 24.01.2011 | Anja Sohl 16 3.Semiblind Channel Estimation Introduction in general, larger than coherence bandwidth of channel Semiblind subspace based estimation Semiblind correlation based estimation FD: reduction of pilot carrying subcarriers

41. 24.01.2011 | Anja Sohl 16 3.Semiblind Channel Estimation Introduction Semiblind subspace based estimation Semiblind correlation based estimation FD: reduction of pilot carrying subcarriers in general, larger than coherence bandwidth of channel

42. 24.01.2011 | Anja Sohl 16 3.Semiblind Channel Estimation Introduction FD: reduction of pilot carrying subcarriers Semiblind subspace based estimation Semiblind correlation based estimation TD: reduction of pilot carrying IFDMA symbols Decision directed channel estimation in general, larger than coherence bandwidth of channel

43. 24.01.2011 | Anja Sohl … CPDATA CP Considering two successive IFDMA symbols: DATA 17 At receiver : CP 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

44. 24.01.2011 | Anja Sohl … DATACP 17 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain CPDATACP Considering two successive IFDMA symbols: DATA At receiver : CP Assumptions:

45. 24.01.2011 | Anja Sohl 18 CP comprises information about 3 received blocks are dependent on 2 transmitted data blocks due to redundancy in the transmit signal 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

46. 24.01.2011 | Anja Sohl 18 CP comprises information about 3 received blocks are dependent on 2 transmitted data blocks due to redundancy in the transmit signal System matrix: Channel variations are assumed to be negligible 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

47. 24.01.2011 | Anja Sohl 18 CP comprises information about 3 received blocks are dependent on 2 transmitted data blocks due to redundancy in the transmit signal System matrix: Channel variations are assumed to be negligible Phase shift matrix 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

48. 24.01.2011 | Anja Sohl 18 CP comprises information about 3 received blocks are dependent on 2 transmitted data blocks due to redundancy in the transmit signal System matrix: Channel variations are assumed to be negligible Phase shift matrix 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

49. 24.01.2011 | Anja Sohl 18 CP comprises information about 3 received blocks are dependent on 2 transmitted data blocks due to redundancy in the transmit signal System matrix: Channel variations are assumed to be negligible Phase shift matrix 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain AWGN

50. 24.01.2011 | Anja Sohl is approximated by the arithmetic mean over K received IFDMA symbols Q eigenvectors of the noise subspace: Identification of signal and noise subspace Subspace analysis of 19 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

51. 24.01.2011 | Anja Sohl Orthogonality between signal and noise subspace Information about pilot-carrying subcarriers Channel estimate Due to arithmetic mean over K IFDMA symbols: represents a joint estimate for all K IFDMA symbols Least-Squares estimates 20 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

52. 24.01.2011 | Anja Sohl 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 21 So far:joint estimate of the channel impulse response for K IFDMA symbols... Estimate the channel variations in time domain as initialization for Decision Directed Channel Estimation

53. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate 1 … S 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

54. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

55. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

56. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

57. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

58. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

59. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

60. 24.01.2011 | Anja Sohl Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 1. Initialization always: use the nearest IFDMA symbols to get a Wiener filtered update estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22

61. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22 always: use the nearest IFDMA symbols to get a Wiener filtered update estimate

62. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22 always: use the nearest IFDMA symbols to get a Wiener filtered update estimate

63. 24.01.2011 | Anja Sohl 1. Initialization Equalization with semiblind subspace based estimate Decision directed channel estimation Wiener Filtering with filter coefficients of decision directed estimates Equalization with updated estimate 3.Semiblind Channel Estimation ii. Reduction of Pilot Symbols in Time Domain 22 always: use the nearest IFDMA symbols to get a Wiener filtered update estimate

64. 24.01.2011 | Anja Sohl 3.Semiblind Channel Estimation Performance Analysis Analysis Assumptions WINNER SCM urban macro-cell channel allocated subcarriers For PACE: each subcarrier has to be used for pilot transmission 23

65. 24.01.2011 | Anja Sohl 3.Semiblind Channel Estimation Performance Analysis 24 Mean Square Error (MSE) MSE E B /N 0 in dB V=0,...,50 km/h

66. 24.01.2011 | Anja Sohl Mean Square Error (MSE) 3.Semiblind Channel Estimation Performance Analysis 25 MSE E B /N 0 in dB V= 0 km/h V= 50 km/h Semiblind subspace based initialization PACE initialization

67. 24.01.2011 | Anja Sohl Outline 1.IFDMA System Model 2.Pilot Assisted Channel Estimation for IFDMA 3.Semiblind Channel Estimation for IFDMA i.Reduction of Pilot Symbols in Frequency Domain ii.Reduction of Pilot Symbols in Time Domain 4. Summary and Conclusion

68. 24.01.2011 | Anja Sohl 4.Summary and Conclusion 26 Pilot assisted channel estimation provides reliable estimation performance for IFDMA Reduction of pilot symbol overhead entails the usage of semiblind channel estimation algorithms: -Semiblind subspace based channel estimation: extension of sampling theorem in FD no. of unknowns to be estimated reduces to Q independent of no. M of channel delay taps time variability of channel transfer factors can be estimated with one pilot carrying IFDMA symbol -Reduction of pilot symbol overhead comes at the expense of increasing computational complexity and degrading estimation performance - Decision Directed Channel Estimation:

69. 24.01.2011 | Anja Sohl The introduced estimation algorithms can be adapted to the application to Block-IFDMA 27 4.Summary and Conclusion The introduced semiblind channel estimation is a promising technique for the application to IFDMA and Block-IFDMA

70. 24.01.2011 | Anja Sohl

71. CONFERENCE PROCEEDINGS / JOURNALS 1. A. Sohl, A. Klein, " Semiblind Channel Estimation for IFDMA in case of channels with large delay spreads", In EURASIP Journal on Advances in Signal Processing, Special Issue on Advances in Single Carrier Block Modulation with Frequency Domain Processing, Volume 2011 (2011), (accepted for publication) 2.A. Sohl, A. Klein, "Comparison of different channel estimation approaches for Block-IFDMA", In European Transactions on Telecommunications (ETT), Special Issue on Multi-Carrier CDMA, Volume 21, Issue 5, August 2010, Pages 417-425 (invited paper) 3.A. Sohl, A. Klein, "Channel Estimation for IFDMA - Comparison of Semiblind Channel Estimation Approaches and Estimation with Interpolation Filtering", In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2009), Tokyo, Japan, Sept. 2009 4. A. Sohl, A. Klein, "Block-IFDMA - Iterative Channel Estimation versus Estimation with Interpolation Filters", In Proc. 7th International Workshop on Multi-Carrier Systems & Solutions, Herrsching, Germany, May 2009 5.A. Sohl, A. Klein, "Blind Channel Estimation based on Second Order Statistics for IFDMA", In Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2008), Cannes, France, Sept. 2008 6.A. Sohl, A. Klein, "Comparison of Localized, Interleaved and Block-Interleaved FDMA in Terms of Pilot Multiplexing and Channel Estimation", In Proc. 15th European Signal Processing Conference, Poznan, Poland, Sept. 2007 (invited paper) 7.A. Sohl, T. Frank, A. Klein, "Channel Estimation for Block-IFDMA", In Proc. 6th International Workshop on Multi-Carrier Spread Spectrum, Herrsching, Germany, May 2007 8. A. Sohl, T. Frank, A. Klein, "Channel Estimation for DFT precoded OFDMA with blockwise and interleaved subcarrier allocation", In Proc. 11th International OFDM Workshop, Hamburg, Germany, August 2006 References

72. 24.01.2011 | Anja Sohl POSTER A. Sohl, A. Klein, "Channel Estimation for B-IFDMA: Interpolation Filters versus Decision Directed Estimation ", Single Carrier FDMA Workshop, New York, U.S.A., March 2009 (poster presentation) PATENT A. Sohl, T. Frank, E. Costa, A. Klein, "Method for coding data and data coding device," European Patent 07009866.0- 2415, 2007 References

73. 24.01.2011 | Anja Sohl The aforementioned algorithm is feasible if the number M of channel delay taps is smaller than or equal to Q Periodicity with Q : Estimation of unknown channel delay taps 1 Q 1 M=Q M > Q 1 Q 1 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain Periodicity with Q : Estimation of unknown channel delay taps

74. 24.01.2011 | Anja Sohl M > Q For each IFDMA symbol: Q channel transfer factors describe the transmission over the channel Time domain equivalent for Q channel transfer factors? 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

75. 24.01.2011 | Anja Sohl M > Q 1 Q 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain 1

76. 24.01.2011 | Anja Sohl M > Q 1 Q 1 Q 2Q 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

77. 24.01.2011 | Anja Sohl M > Q 1 Q 1 Q 1 Q 2Q 1 Q 1 Q 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

78. 24.01.2011 | Anja Sohl M > Q 1 Q 1 Q 1 Q 2Q 1 Q 1 Q Cyclic channel impulse response 1 Q 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

79. 24.01.2011 | Anja Sohl 1 Q 2Q M > Q 1 Q 2Q 1 Q 1 Q Cyclic channel impulse response 1 Q 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain 1 Q 2Q 1 Q

80. 24.01.2011 | Anja Sohl M > Q 1 Q 1 Q 2Q 1 Q 1 Q Cyclic channel impulse response 1 Q 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain Q unknowns to be estimated 1 Q 2Q

81. 24.01.2011 | Anja Sohl At the receiver, the parts of the signal have to be evaluated which can be expressed in dependency of two transmitted data blocks a system matrix containing the cyclic channel impulse response CP... 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain

82. 24.01.2011 | Anja Sohl At the receiver, the parts of the signal have to be evaluated which can be expressed in dependency of two transmitted data blocks a system matrix containing the cyclic channel impulse response CP... 3.Semiblind Channel Estimation i. Reduction of Pilot Symbols in Frequency Domain Algorithm is independent of the number M of channel delay taps