A novel algorithm(CS algorithm)for channel estimation and signal detection is proposed to resolve the problem of excessively high sampling rate of the ultra-wideband signals.The algorithm is based on the compressed sensing(CS)theory. The modulated signal is transmitted after random-filtering
and the received signal is sub-sampled. The mathematical model of CS is developed by cyclic convolution of the modulated signals
the random filter and the channel
so the basic pursuit algorithm can be utilized to perform the task of channel estimation and signal detection. Simulation results show that the number of the sampling data required by the proposed algorithm is only one-third or less of the number of the sampling data needed by the least squares algorithm
while the estimation performance is improved by 4.5 dB under the moderate signal-to-noise ratio(15~25 dB)condition
and that the original transmitted signals are correctly detected.
关键词
Keywords
references
BENEDETTO M D, KAISER T, MOLISH A F,et al. UWB communication systems: a comprehensive overview [M]. New York,USA: Hindawi Publishing Corporation, 2006.
QIU R C, SCHOLTZ R A, SHEN X. Guest editorial special section on ultra-wideband wireless communications:a new horizon[J]. IEEE Trans on Veh Technol, 2005, 54(5):1525-1527.
BLAZQUEZ R, LEE F S, WENTZLOFF D D, et al. Digital architecture for an ultra-wideband radio receiver [C]∥Proceedings of IEEE VTC. Piscataway, NJ, USA: IEEE, 2003:1303-1307.
BARANIUK R. Compressive sensing [C]∥Proceedings of Annual Conference on Information Sciences and Systems. Piscataway, NJ, USA: IEEE, 2008: 1289-1306.
PAREDES J L, ARCE G R, WANG Zhongmin. Ultra-wideband compressed sensing: channel estimation[J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(3):383-395.
COHEN A, DAHMEN W, DEVORE R. Compressed sensing and best k-term approximation [J]. Journal of the American Mathematical Society, 2009, 22(1):211-231.
DONOHO D L. For most large underdetermined systems of equations, the minimal l1-norm near-solution approximates the sparsest near-solution [J]. Communications on Pure and Applied Mathematics, 2006, 59(7):907-934.
HAUPT J, NOWAK R. Signal reconstruction from noisy random projections [J]. IEEE Trans on Inform Theory, 2006, 52(9): 4036-4048.
CANDES E J, WAKIN M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2):21-30.
BAJWA W U, HAUPT J, RAZ G, et al. Toeplitz-structured compressed sensing matrices[C]∥Proceedings of IEEE SSP'07.Piscataway, NJ,USA: IEEE, 2007:294-298.
DUARTE M F, DAVENPORT M A, WAKIN M B,et al. Sparse signal detection from incoherent projections[C]∥Proceedings of IEEE ICASSP. Piscataway, NJ, USA: IEEE, 2006: 305-308
DONOHO D. SparseLab [EB/OL]. [2009-04-20]. http:∥sparselab.stanford.edu./SparseLab_files/Download_files/SparseLab21-Core.zip.
MOLISCH A F.IEEE 802.15.4a channel model:final report[EB/OL].[2009-02-28].http:∥www.ieee 802.org/15/pub/TG4a.html.