Abstract
Long duration of the channel impulse response along with limited number of actual paths in orthogonal frequency division multiplexing (OFDM) vehicular wireless communication systems results in a sparse discrete equivalent channel. Implementing different compressed sensing (CS) algorithms enables channel estimation with lower number of pilot subcarriers compared to conventional channel estimation. In this paper, new methods to enhance the performance of the orthogonal matching pursuit (OMP) for CS channel estimation method is proposed. In particular, in a new algorithm dubbed as linear minimum mean square error-OMP (LMMSE-OMP), the OMP is implemented twice: first using the noisy received pilot data as the input and then using a modified received pilot data processed by the outcome of the first estimator. Simulation results show that LMMSE-OMP improves the performance of the channel estimation using the same number of pilot subcarrier. The added computational complexity is studied and several methods are suggested to keep it minimal while still achieving the performance gain provided by the LMMSE-OMP including using compressive sampling matching pursuit (CoSaMP) CS algorithm for the second round and also changing the way the residue is calculated within the algorithm.
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References
ITU: International Telecommunication Union, Guidelines for the Evaluation of Radio Transmission Technologies (RTTs) for IMT-2000, ITU-R Recommendation M.1225. (1997).
Zou, W., & Lam, W. H. A fast LMMSE channel estimation method for OFDM systems. EURASIP Journal on Wireless Communications and Networking, 2009, 752895.
Candes, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.
Karabulut, G. Z., & Yongacoglu, A. (2004). Sparse channel estimation using orthogonal matching pursuit algorithm. Vehicular Technology Conference, 2004. VTC2004-Fall. 2004 I.E. 60th, 2004, pp. 3880–3884, Vol. 6.
He, X., & Song, R. (2010). Pilot pattern optimization for compressed sensing based sparse channel estimation in OFDM systems. Wireless Communications and Signal Processing (WCSP), 2010 International Conference on, Suzhou, pp. 1–5.
Peng, Y., Alexandropoulos, G. C., Zhao, H., Duan, H. (2013). Performance analysis of OMP-based channel estimation for OFDM systems with periodical pilots and virtual subcarriers. Computing, Management and Telecommunications (ComManTel), 2013 International Conference on, Ho Chi Minh City, Vietnam, pp. 11–16.
Qi, C., & Wu, L. (2011). Optimized pilot placement for sparse channel estimation in OFDM systems. IEEE Signal Processing Letters, 18(12), 749–752.
Yuan, W., Zheng, B., Yue, W., Wang, L. (2011). Two-way relay channel estimation based on compressive sensing. Wireless Communications and Signal Processing (WCSP), 2011 International Conference on, Nanjing, pp. 1–5.
Gaur, Y., & Chakka, V. (2012). Performance comparison of OMP and CoSaMP based channel estimation in AF-TWRN scenario. 2012 Third International Conference on Computer and Communication Technology.
Li, X., Jing, X., Sun, S., Huang, H., Chen, N., Lu, Y. (2013). An improved reconstruction method for compressive sensing based OFDM channel estimation. 2013 International Conference on Connected Vehicles and Expo (ICCVE), Las Vegas, pp. 100–105.
Pan, H., Xue, Y., Mei, L. Gao, F. (2015). An improved CoSaMP sparse channel estimation algorithm in OFDM system. Signal Processing, Communications and Computing (ICSPCC), 2015 I.E. International Conference on, Ningbo, pp. 1–4.
Mei, L., Gao, F., Pan, H Xue, Y. (2015). An improved ROMP sparse channel estimation algorithm in OFDM system. Signal Processing, Communications and Computing (ICSPCC), 2015 I.E. International Conference on, Ningbo, pp. 1–4.
Qi, C., & Wu, L. (2011). A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems. 2011 I.E. International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, pp. 3488–3491.
Chisaguano, D. J. R., Hou, Y., & Higashino, T. (2015). Minoru Okada ISDB-T diversity receiver using a 4-element ESPAR antenna with periodically alternating directivity. ITE Transactions on Media Technology and Applications, 3(4), 268–278.
Pati, Y. C., Rezaiifar, R., Krishnaprasad, P. S. (1993). Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Signals, Systems and Computers, 1993. 1993 Conference Record of the Twenty-Seventh Asilomar Conference on, Pacific Grove, pp. 40–44, Vol. 1.
Shao, X., Chen, W., Tao, M., Ren, X. (2014). Statistics-based channel estimation and ici mitigation in OFDM system over high mobility channel. High Mobility Wireless Communications (HMWC), 2014 International Workshop on, Beijing, pp. 151–155.
Candès, E. J., Romberg, J. (2005, January) Practical signal recovery from random projections. In SPIE International Symposium on Electronic Imaging: Computational Imaging III, San Jose.
Tsaig, Y., Donoho, D. L. (2004). Extensions of compressed sensing. Technical report, Department of Statistics, Stanford University.
Tropp, J. A., & Needell, D. (2009). Cosamp:iterative signal recovery from incomplete and inaccurate measurements. Applied and Computational Harmonic Analysis, 301–321.
Satpathi, S., & Chakraborty, M. (2014). On the number of iterations for convergence of CoSaMP and SP algorithm. arXiv preprint arXiv: 1404.4927.
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Vahidi, V., Saberinia, E. (2018). Performance Enhancement of OMP Algorithm for Compressed Sensing Based Sparse Channel Estimation in OFDM Systems. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 558. Springer, Cham. https://doi.org/10.1007/978-3-319-54978-1_1
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DOI: https://doi.org/10.1007/978-3-319-54978-1_1
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