1 / 50

Presenter: Teng-Han Tsai ( 蔡騰漢 )

Block-by-Block Blind Channel Estimation Algorithm Using Subcarrier Averaging for Multi-user OFDM Systems. Presenter: Teng-Han Tsai ( 蔡騰漢 ). Institute of Communications Engineering & Department of Electrical Engineering National Tsing Hua University Hsinchu, Taiwan 30013, R.O.C.

suzy
Download Presentation

Presenter: Teng-Han Tsai ( 蔡騰漢 )

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Block-by-Block Blind Channel Estimation Algorithm Using Subcarrier Averaging for Multi-user OFDM Systems Presenter: Teng-Han Tsai (蔡騰漢) Institute of Communications Engineering & Department of Electrical Engineering National Tsing Hua University Hsinchu, Taiwan 30013, R.O.C. E-mail: g945614@oz.nthu.edu.tw

  2. Outline 1. Introduction 2. Review of MVDR beamformer and MUSIC algorithm 3. MIMO Model for Post-FFT Beamforming Structure 4. Proposed Blind Channel Estimation Algorithm by Subcarrier Averaging 5. Simulation Results 6. Conclusions MVDR: Minimum Variance Distortionless Response MUSIC: Multiple Signal Classification MIMO: Multiple-input Multiple-output

  3. Introduction

  4. L1 LP : path gain of thelth path of user p : number of antennas : ( ) total number of paths (or DOAs) of all the users. : number of users Wireless Environment ULA (Uniform Linear Array) BS MS 1 Rx signals user 1 Tx signals ... ... user P ... MS P Noise : Direction of arrivals (DOA) of the lth path of userp : time delay of the lth path of user p :number of paths (or DOAs) associated with user p.

  5. : identity matrix Assumptions (A1),are QPSK ( BPSK ) zero-mean independent identically distributed (i.i.d.) random sequences with , and is statistically independent of for . , and L is known. (A2)for all , (A3) (A4)is zero-mean white Gaussian and statistically independent of , and . BPSK : Binary phase shift keying QPSK : Quadriphase shift keying

  6. Review of MVDR Beamformer and MUSIC Algorithm

  7. Beamforming (1/3) [2][3] • Signal separation and extraction • Interference suppression • Antenna gain enhancement • Spectral efficiency increase Beamformer #1 (Desired signal) #2 ... ... #Q (Interfering signal)

  8. Beamforming (2/3) path gain DOA (Direction of Arrival) • MIMO Model Beamformer #1 (source 1) #2 ... ... Beamformer Output #Q (source P ) • Assumptions: (M1),are wide-sense stationary random processes, and is statistically independent of for . (M2)for all , and (M3) is zero-mean white Gaussian with and statistically independent of

  9. Beamforming (3/3) • MVDR Beamformer: By Lagrange multiplier Criterion: subject to : correlation matrix of where : (known in advance) DOA of the path of user 1(Desired source) Under the assumption (M1) and (M2), as which implies the MVDR beamformer can perfectly extract the desired signal by processing . MVDR :Minimum Variance Distortionless Response

  10. DOA Estimation - MUSIC Method (1/2) EVD (Eigenvalue Decomposition) of Correlation matrix : where 1. are orthonormal basis. 2.

  11. DOA Estimation - MUSIC Method (2/2) • Signal subspace is orthogonal to noise subspace : , , • Construct projection matrix : • may be found by solving for . • Compute MUSIC spectrum : and search for “infinitely high” spectral peaks.

  12. MIMO Model for Post-FFT Beamforming Structure

  13. Post-FFT Beamforming Structure (1/2) channel information at subcarrier 0 for user p beamformer GI Removal N-point FFT A/D S/P … … GI Removal N-point FFT A/D S/P … … P/S GI Removal N-point FFT A/D S/P … … N beamformers

  14. FFT ( channel matrix) ( vector ) Post-FFT Beamforming Structure (2/2) • MIMO model for each subcarrier k: where Channel response of user p at subcarrier k ( source vector) ( white Gaussian noise vector)

  15. Proposed Blind Channel Estimation Algorithm by Subcarrier Averaging

  16. ( DOA matrix) ( source vector) ( ) MIMO Model (1/4) • MIMO Model re-expression where ( ) full column rank by Assumption (A2) (component) ( white Gaussian noise vector )

  17. MVDR MIMO Model (2/4) • Source Vector : • Under Assumption (A1) and Assumption (A3), check the components of the L× 1 source vector by statistical averaging : 1. Different users: 2. Same user but different path: The components of the source vector are statistically correlated.

  18. MVDR MIMO Model (3/4) • Define the subcarrier averaging of : Under Assumption (A1) and Assumption (A3), check the components of the L× 1 source vector by subcarrier averaging : where denotes “convergence in probability” as N . Each components of can be “de-correlated” by subcarrier averaging.

  19. ( DOA matrix) ( source vector) ( ) MIMO Model (4/4) • MVDR and MUSIC methods by subcarrier averaging: By subcarrier averaging, MVDR and MUSIC methods can be applied to post-FFT beamforming structure by processing where ( ) full column rank by Assumption (A2) (component) ( white Gaussian noise vector )

  20. Algorithm Procedure (received signal vector) MVDR Beamformer MUSIC DOA Source Time Delay Estimation Extraction Estimation Path Gain Classification Estimation and Grouping (estimate of channel matrix)

  21. Proposed Algorithm – DOA Estimation • MUSIC method : ( MUSIC spectrum ) where : EVD of correlation matrix ( the smallest Q- L eigenvalues ) ( noise eigenvectors ) : projection matrix All the DOAs can be estimated by finding the L largest local maxima of SMUSIC(θ) . EVD : eigenvalue decomposition

  22. Proposed Algorithm – Source Extraction By and , we have MVDR beamformer and MVDR beamformer output where : path gain associated with DOA and : data associated with DOA and : time delay associated with DOA and

  23. Proposed Algorithm – Time Delay Estimation (1/3) • Estimate time delay by processing : • Data sequence (QPSK signals): • Estimate time delay : where

  24. Proposed Algorithm – Time Delay Estimation (2/3) • Estimate time delay by processing : • Data sequence (BPSK signals): • Estimate time delay : where

  25. Proposed Algorithm – Time Delay Estimation (3/3) • Time compensated beamformer output associated with : where : path gain associated with DOA and : data associated with DOA and

  26. Proposed Algorithm – Classification and Grouping • Procedures of classification and grouping: • Define (Step 1) Select a path and set it to be . (Step 2) Calculate for all the paths to be analyzed. (Step 3) Extract all the paths that have and assign them as a new group. (Step 4) From the remaining paths, select another path and set it to be , where i = 2, …, P. (Step 5) Go to Step 2 until there is no more path to group. (Step 6) Finally, there will be P groups where all the paths of a group belongs to the same user.

  27. Proposed Algorithm – Path Gain Estimation (1/9) • After Classification and Grouping: • It is obtained P groups, where in each group there are Lp sequences with the same data symbol information multiplied by different coefficient: Group 1 Group P

  28. Proposed Algorithm – Path Gain Estimation (2/9) • Estimate path gain by processing : • Data sequence (QPSK signals): • Estimate path gain : • The 4 solutions have the same magnitude but different phase angle. Therefore, it needs to choose one of them.

  29. Proposed Algorithm – Path Gain Estimation (3/9) Im Re • Decision of the path gain phase angle: • For QPSK case: (Step 1) From the 4 phase angle solutions , select an angle , for i = 1, …, 4. (Step 2) Select its corresponding path (first path) and rotate the path by its corresponding phase angle estimate . Ambiguity phase After rotation

  30. Proposed Algorithm – Path Gain Estimation (4/9) • Decision of the path gain phase angle: • For QPSK case: Select another path , where l = 2, …, Lp and rotate it by its 4 possible path gain phase angles . (Step 3)

  31. Proposed Algorithm – Path Gain Estimation (5/9) • Decision of the path gain phase angle: • For QPSK case: Perform the inner product of the first rotated path and the four l-th rotated paths , for l= 2, …, Lp. . (Step 4) Calculate the phase angle of the four inner products , which the results will be approximated to {0, π/2, π, 3π/2}. (Step 5) Choose the path gain phase angle whose phase angle of inner product is closed to 0. (Step 6) They are in phase

  32. Proposed Algorithm – Path Gain Estimation (6/9) • Decision of the path gain phase angle: • For QPSK case: Go to the Step 2 until there is no more paths to rotate in the group. . (Step 7) (Step 8) Finally, the path gain phase angle for each path of the group will be obtain. Note:As the proposed algorithm is a blind method, the estimated path gain has an ambiguity scalar . This value depends on the choice of the Phase angle solution in Step 1.

  33. Proposed Algorithm – Path Gain Estimation (7/9) or • Estimate path gain by processing : • Data sequence (BPSK signals): • Estimate path gain : • The 2 solutions have the same magnitude but different phase angle. Therefore, it needs to choose one of them.

  34. Proposed Algorithm – Path Gain Estimation (8/9) • Decision of the path gain phase angle: • For BPSK case: (Step 1) From the 2 phase angle solutions , select a solution , for i = 1, 2. (Step 2) Select its corresponding path (first path) and rotate the path by its corresponding phase angle estimate . Ambiguity phase Select another path , where l = 2, …, Lp and rotate it by its 2 possible path gain phase angles . (Step 3) Perform the inner product of the first rotated path and the four l-th rotated paths , for l= 2, …, Lp. . (Step 4)

  35. Proposed Algorithm – Path Gain Estimation (9/9) • Decision of the path gain phase angle: • For BPSK case: Calculate the phase angle of the four inner products , which the results will be approximated to {0, π}. (Step 5) Choose the path gain phase angle whose phase angle of inner product is closed to 0. (Step 6) Go to the Step 2 until there is no more paths to rotate in the group. . (Step 7) (Step 8) Finally, the path gain phase angle for each path of the group will be obtain. Note:As the proposed algorithm is a blind method, the estimated path gain has an ambiguity scalar . This value depends on the choice of the Phase angle solution in Step 1.

  36. Proposed Algorithm – Channel Recovery • Estimate of channel matrix : With the estimated DOA, time delay and path gain of each path, the channel matrix can be obtain: group index P is P× Punknown permutation matrix. where ( ) user index Note: The permutation matrix P can be obtained using the information of the transmitted sources after the data sequence detection.

  37. Data Sequence Detection Once the channel information and the noise power (from MUSIC) are obtained. A MMSE beamformer can be applied: Then the estimated data sequence for a user p will be: Ambiguity phase For QPSK For BPSK

  38. Simulation Results

  39. Performance Index • Definition of Normalized Mean Square Error (NMSE): where : estimate of channel matrix : Frobenius norm

  40. Parameters Used • A two-user (P =2) OFDM system • Ng= 20 • : i.i.d. zero-mean Gaussian with . • Q = 10. • N = 1024. • L = 6 . • DOA randomly generated for all the users . • Time delay randomly generated for all the users . • Path gain randomly generated for all the users . • Input SNR:

  41. NMSE of A

  42. Symbol Error Ratio

  43. Conclusions • We have presented blind channel estimation algorithm by subcarrier averaging for the post-FFT beamforming structure over one OFDM block. This proposed algorithm basically includes DOA estimation using MUSIC method, source extraction using MVDR beamformer, time delay estimation and compensation, classification and grouping, path gain estimation and channel recovery. • The proposed channel estimation algorithm only needs one OFDM block to estimate channel with good performance. • Some simulation results were provided to support the blind beamformer designed by the proposed channel estimation algorithm, and its performance is very closed to the performance of the MMSE beamforming using perfect channel.

  44. References (1/3) [1] R. V. Nee and R. Prasad, OFDM for Wireless Multimedia Communications . Boston: Artech House, 1999. [2] J. C. Liberti and T. S. Rappaport, Smart Antennas for Wireless Communications: IS- 95 and Third Generation CDMA Applications. New Jersey: Prentice Hall, 1999. [3] L. C. Godara, “ Application of antenna arrays to mobile communications, Part II: Beam-forming and direction-of-arrival considerations,” IEEE Proceeding, vol. 85, No. 8, pp 1195-1245, Aug. 1997. [4] Ralph O. Schmidt,“Multiple emitter location and signal parameter,” Proc. IEEE Trans. Antennas and Propagation, vol. AP-34, No. 3, pp. 3381-3391, Dec. 1999. [5] Shinsuke Hara, Montree Budsabathon, and Yoshitaka Hara, “A pre-FFT OFDM adaptive antenna array with eigenvector combining,” Proc. IEEE International Conference on Communication., vol. 4, pp. 2412-2416, June. 2004. [6] Ming LEI, Ping ZHANG, and Hiroshi HARADA, and Hiromitsu WAKANA, “LMS adaptive beamforming based on pre-FFT combining for ultra high-data-eate OFDM system,” Proc. IEEE 60th Vehicular Technology Conference, vol. 5, Los Angeles, California, USA, Sept. 26-29, 2004, pp. 3664-3668.

  45. References (2/3) [7] Fred W. Vook and Kevin L. Baum, “Adaptive antennas for OFDM,” Proc. IEEE 48th Vehicular Technology Conference, vol. 1, Ottawa, Ont., May 18-21, 1998, pp. 606-610. [8] Chan Kyu Kim, Kwangchun Lee, and Yong Soo Cho, “Adaptive Beamforming Algorithm for OFDM Systems with Antenna Arrays,” IEEE Trans. Consumer Electronics, vol. 46, No. 4, pp. 1052-1058, Nov. 2000. [9] Hidehiro Matsuoka and Hiroki Shoki, “Comparison of pre-FFT and post-FFT processing adaptive arrays for OFDM systems in the presence of co-channel interference,” Proc. IEEE 14th International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 2, Beijing, China, Sept. 7-10, 2003, pp. 1603-1607.. [10] Zhongding Lei and Francois P.S. Chin, “Post and pre-FFT beamforming in an OFDM system,” Proc. IEEE 59th Vehicular Technology Conference, vol. 1, Milan, Italy, May 17-19, 2004, pp. 39-43.. [11] Matthias Munster and Lajos Hanzo, “Performance of SDMA multi-user detection techniques for Walsh-Hadamard-Spread OFDM Schemes,” Proc. IEEE 54th Vehicular Technology Conference,vol. 4, Atlantic City, NJ, USA, Oct. 7-11, 2001, pp. 2219-2323.

  46. References (3/3) [12] Samir Kapoor, Daniel J. Marchok, and Yih-Fang Huang, “Adaptive interference suppression in multiuser wireless OFDM systems using antenna arrays,” IEEE Trans. Signal Processing, vol. 47, No. 12, pp. 3381-3391, Dec. 1999. [13] Shenghao Yang and Yuping Zhao, “Channel estimation method for 802.11a WLAN with multiple-antenna,” Proc. 5th International Symposium on Multi-Dimensional Mobile Communications, 29 Aug.-1 Sept, 2004, pp. 297-300. [14] Bassem R. Mahafza and Atef Z. Elsherbeni, Matlab Simulations for Radar Systems Design. Boca Raton, FL :CRC Press/Chapman & Hall, 2004. [15] Kai-Kit Wong, Roger S.-K. Cheng, Khaled Ben Letaief, and Ross D. Murch, “Adaptive antennas at the mobile and base stations in an OFDM/TDMA system,” IEEE Trans. Signal Processing, vol. 49, No. 1, pp. 195-206, Jan. 2001. [16] Shiann-Shiun Jeng, Garret Toshio Okamoto, Guanghan Xu, Hsin-Piao Lin, and Wolfhard J. Vogel, “Experimental evaluation of smart antenna system performance for wireless communications,” IEEE Trans. Antennas and Propagation, vol. 46, No. 6, pp. 749-757, June 1998.

  47. Thank you very much

  48. , , : number of subcarriers : length of GI : data sequence of user Transmitter of OFDM Systems User p N-point IFFT GI Insertion D/A & Up Converter P/S S/P … … k : frequency-domain sample index n : time-domain sample index

  49. Symbol Error Ratio (QPSK)

  50. Symbol Error Ratio (BPSK)

More Related