Position Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems

Cited by: 0|Bibtex|Views26
Other Links: arxiv.org
Weibo:
We presented a new position-based compressed channel estimation method for high-mobility Orthogonal frequency-division multiplexing systems

Abstract:

With the development of high speed trains (HST) in many countries, providing broadband wireless services in HSTs is becoming crucial. Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, OFDM is sensitive to the time selectivity caus...More

Code:

Data:

0
Introduction
  • Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communication systems due to its high spectral efficiency [1].
  • OFDM is sensitive to the time selectivity, which is induced by rapid time variations of mobile channels.
  • High speed trains (HST) have been increasingly developed in many countries and OFDM has been adopted for high data rate services [2].
  • In high-mobility environments, wireless channels are both fast time-varying and frequency selective and can be considered as the doubly
Highlights
  • Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communication systems due to its high spectral efficiency [1]
  • Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods in the high-mobility environment
  • We presented a new position-based compressed channel estimation method for high-mobility Orthogonal frequency-division multiplexing systems
  • The estimation complexity is reduced by the proposed channel model by utilizing the position information
  • With a pre-designed pilot codebook, the proposed scheme is feasible for many current wireless Orthogonal frequency-division multiplexing communication systems
Results
  • Under the high-mobility environment, the authors illustrate the performances of the proposed pilot design method using two typical compressed channel estimators, BP [27] and OMP [28].
  • The mean square error (MSE) and the bit error rate (BER) performances are considered versus the the signal to noise ratio (SNR) and the HST position.
  • The performances of the conventional LS and the linear minimum mean square error (LMMSE) [9] estimators are considered.
Conclusion
  • The authors presented a new position-based compressed channel estimation method for high-mobility OFDM systems.
  • The estimation complexity is reduced by the proposed channel model by utilizing the position information.
  • The pilot symbol and the placement are jointly designed by the proposed algorithm to minimize the system average coherence.
  • Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods over high-mobility channels.
  • With a pre-designed pilot codebook, the proposed scheme is feasible for many current wireless OFDM communication systems
Summary
  • Introduction:

    Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communication systems due to its high spectral efficiency [1].
  • OFDM is sensitive to the time selectivity, which is induced by rapid time variations of mobile channels.
  • High speed trains (HST) have been increasingly developed in many countries and OFDM has been adopted for high data rate services [2].
  • In high-mobility environments, wireless channels are both fast time-varying and frequency selective and can be considered as the doubly
  • Results:

    Under the high-mobility environment, the authors illustrate the performances of the proposed pilot design method using two typical compressed channel estimators, BP [27] and OMP [28].
  • The mean square error (MSE) and the bit error rate (BER) performances are considered versus the the signal to noise ratio (SNR) and the HST position.
  • The performances of the conventional LS and the linear minimum mean square error (LMMSE) [9] estimators are considered.
  • Conclusion:

    The authors presented a new position-based compressed channel estimation method for high-mobility OFDM systems.
  • The estimation complexity is reduced by the proposed channel model by utilizing the position information.
  • The pilot symbol and the placement are jointly designed by the proposed algorithm to minimize the system average coherence.
  • Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods over high-mobility channels.
  • With a pre-designed pilot codebook, the proposed scheme is feasible for many current wireless OFDM communication systems
Tables
  • Table1: HST COMMUNICATION SYSTEM PARAMETERS
Download tables as Excel
Funding
  • This work is supported by the National 973 Project #2012CB316106, by NSF China #61161130529, #61328101, and #61322102, by the STCSM Science and Technology Innovation Program #13510711200, and by the SEU National Key Lab on Mobile Communications #2013D11
Reference
  • P. Schniter, “Low-complexity equalization of OFDM in doubly selective channels,” IEEE Transactions on Signal Processing, vol. 52, no. 4, pp. 1002-1011, April 2004.
    Google ScholarLocate open access versionFindings
  • O. B. Karimi, J. Liu, and C. Wang, “Seamless wireless connectivity for multimedia services in high speed trains,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 4, pp. 729-739, May 2012.
    Google ScholarLocate open access versionFindings
  • W. U. Bajwa, A. M. Sayeed, and R. Nowak. “Sparse multipath channels: modeling and estimation,” IEEE Digital Signal Processing Education Workshop, pp. 320-325, Jan. 2009.
    Google ScholarLocate open access versionFindings
  • W. U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, “Compressed channel sensing: a new approach to estimating sparse multipath channels,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1058-1076, June 2010.
    Google ScholarLocate open access versionFindings
  • W. U. Bajwa, A. M. Sayeed, and R. Nowak, “Learning sparse doublyselective channels,” 46th Annual Allerton Conference on Communication, Control and Computing, pp. 575-582, Sept. 2008.
    Google ScholarLocate open access versionFindings
  • S. Sung and D. Brady, “Spectral spatial equalization for OFDM in time varying frequency-selective multipath channels,” Proc. IEEE Workshop Sensor Array Multichannel Signal Process., pp. 434-438, 2000.
    Google ScholarLocate open access versionFindings
  • Y. Mostofi and D. C. Cox, “ICI mitigation for pilot-aided OFDM mobile systems,” IEEE Trans. Wireless Commun., vol. 4, no. 2, pp. 765-774, March 2005.
    Google ScholarLocate open access versionFindings
  • H. Hijazi and L. Ros, “Polynomial estimation of time-varying multipath gains with intercarrier interference mitigation in OFDM systems,” IEEE Transactions on Vehicular Technology, vol. 58, no. 1, pp. 140-151, Jan. 2009.
    Google ScholarLocate open access versionFindings
  • Z. Tang, R. C. Cannizzaro, G. Leus, and P. Banelli, “Pilot-assisted timevarying channel estimation for OFDM systems,” IEEE Transactions on Signal Processing, vol. 55, no. 5, pp. 2226-2238, May 2007.
    Google ScholarLocate open access versionFindings
  • X. Ma, G. Giannakis, and S. Ohno, “Optimal training for block transmissions over doubly-selective wireless fading channels,” IEEE Trans. Signal Process, vol. 51, no. 5, pp. 1351-1366, May 2003.
    Google ScholarLocate open access versionFindings
  • G. Taubock, F. Hlawatsch, D. Eiwen, and H. Rauhut, “Compressive estimation of doubly selective channels in multicarrier systems: leakage effects and sparsity-enhancing processing,” IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 2, pp. 255-271, April 2010.
    Google ScholarLocate open access versionFindings
  • G. Taubock and F. Hlawatsch, “A compressed sensing technique for OFDM channel estimation in mobile environments: exploiting channel sparsity for reducing pilots,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2885-2888, March 2008.
    Google ScholarLocate open access versionFindings
  • D. L. Donoho, M. Elad, and V. N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006.
    Google ScholarLocate open access versionFindings
  • M. Elad, “Optimized projections for compressed sensing,” IEEE Transcation on Signal Processing, vol. 55, no. 12, pp. 5695-5702, Dec. 2007.
    Google ScholarLocate open access versionFindings
  • E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
    Google ScholarLocate open access versionFindings
  • E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Processing Mag., vol. 25, no. 2, pp. 21-30, March 2008.
    Google ScholarLocate open access versionFindings
  • X. He and R. Song, “Pilot pattern optimization for compressed sensing based sparse channel estimation in OFDM systems,” International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1-5, Oct. 2010.
    Google ScholarLocate open access versionFindings
  • N. Jing, W. Bi, and L. Wang, “Deterministic pilot design for MIMO OFDM system based on compressed sensing,” International Conference on Communication Technology (ICCT), pp. 897-903, Nov. 2012.
    Google ScholarLocate open access versionFindings
  • D. Wang and X. Hou, “Compressed MIMO chanel estimation and efficient pilot pattern over Doppler sparse environment,” International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1-5, Nov. 2011.
    Google ScholarLocate open access versionFindings
  • C. Qi and L. Wu, “Optimized pilot placement for sparse channel estimation in OFDM systems,” IEEE Signal Processing Letters, vol. 18, no. 12, pp. 749-752, Dec. 2011.
    Google ScholarLocate open access versionFindings
  • C. Qi and L. Wu, “A study of deterministic pilot allocation for sparse channel estimation in OFDM systems,” IEEE Communications Letters, vol. 16, no. 5, pp. 742-744, May 2012.
    Google ScholarLocate open access versionFindings
  • X. Ren, W. Chen, and Z. Wang, “Low coherence compressed channel estimation for high mobility MIMO OFDM systems,” Global Communications Conference (GLOBECOM), Dec. 2013.
    Google ScholarLocate open access versionFindings
  • X. Xiao, B. Zheng, and C. Wang, “Compressed channel estimation based on optimized measurement matrix,” Wireless Communications and Signal Processing (WCSP), pp. 1-5, Nov. 2011.
    Google ScholarLocate open access versionFindings
  • L. Liu, C. Tao, J. Qiu, H. Chen, L. Yu, W. Dong, and Y. Yuan, “Position-based modeling for wireless channel on high-speed railway under a viaduct at 2.35 GHz,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 4, pp. 834-845, May 2012.
    Google ScholarLocate open access versionFindings
  • R. D. Pascoe and T. N. Eichorn, “What is communication-based train control?,” IEEE Vehicular Technology Magazine, vol. 4, no. 4, pp. 16-21, Dec. 2009.
    Google ScholarLocate open access versionFindings
  • H. Hijazi and L. Ros, “Joint data QR-detection and kalman estimation for OFDM time-varying rayleigh channel complex gains,” IEEE Trans. Commun., vol. 58, no. 1, pp. 170-178, 2010.
    Google ScholarLocate open access versionFindings
  • S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decompostiion by basis pursuit,” SIAM Review, vol. 43, pp. 129-159, 2001.
    Google ScholarLocate open access versionFindings
  • Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” Proceedings of the 27th Annual Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 40-44, Nov. 1993.
    Google ScholarLocate open access versionFindings
  • J. A. Tropp, “Greed is good: algorithmic results for sparse approximation,” IEEE Trans. Inf. Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004.
    Google ScholarLocate open access versionFindings
  • I. Berenguer, X. Wang, and V. Krishnamurthy, “Adaptive MIMO antenna selection via discrete stochastic optimization,” IEEE Trans. Signal Processing, vol. 53, no. 11, pp. 4315-4329, Nov. 2005.
    Google ScholarLocate open access versionFindings
  • S. Andradottir, “A global search method for discrete stochastic optimization,” SIAM J. Optimiz., vol. 6, no. 2, pp. 513-530, 1996.
    Google ScholarLocate open access versionFindings
  • S. Andradottir, “Accelerating the convergence of random search methods for discrete stochastic optimization,” ACM Trans. Modeling and Compu. Simul., vol. 9, no. 4, pp. 349-380, 1999.
    Google ScholarLocate open access versionFindings
  • K. Pekka, M. Juha, H. Lassi, et la., “WINNER II Channel Models. IST-4-027756,” WINNER II, D1.1.2 v1.1, Tech. Rep., Sept. 2007.
    Google ScholarLocate open access versionFindings
Full Text
Your rating :
0

 

Tags
Comments