This paper deals with blind equalization of sparse multipath channels. A linear model is built under the framework of Reduced Constellation Algorithm (RCA). And the inherent sparse nature of the equalizer is exploited by employing a sparse promoting prior distribution. Then, the sparse Bayesian learning iterative inference method is applied to the proposed model in order to obtain the optimal sparse equalizer. The new proposed algorithm, which belongs to data recycling equalization algorithm domain, can be applied to short packet data applications. Compared with traditional Stochastic Gradient Descent (SGD) method, the new proposed algorithm performs more steadily under different equalizer order and has superior steady-state Symbol-Error-Rate (SER) performance. The effectiveness of the proposed algorithm is verified by simulations.
RUAN Xiukai, JIANG Xiao, LIU Li, et al. A novel Bussgang category of blind equalization with exponential expanded multi-modulus algorithm[J]. Journal of Electronics & Information Technology, 2013, 35(9): 2188-2193. doi: 10.3724/SP.J.1146.2012.01544.
[2]
YANG J, WERNER J J, and DUMONT G A. The multimodulus blind equalization and its generalized algorithms[J]. IEEE Journal on Selected Areas in Communications, 2002, 20(5): 997-1015.
[3]
HAN H D and DING Z. Steepest descent algorithm implementation for multichannel blind signal recovery[J]. IET Communications, 2012, 6(18): 3196-3203.
[4]
ZHOU F, TAN J, FAN X, et al. A novel method for sparse channel estimation using super-resolution dictionary[J]. EURASIP Journal on Advances in Signal Processing, 2014, 2014(1): 1-11.
[5]
SENOL H. Joint channel estimation and symbol detection for OFDM systems in rapidly time-varying sparse multipath channels[J]. Wireless Personal Communications, 2015, 82(3): 1161-1178.
[6]
GELLER B, CAPELLANO V, BROSSIER J M, et al. Equalizer for video rate transmission in multipath underwater communications: Special issue on acoustic communications[J]. IEEE Journal of Oceanic Engineering, 1996, 21(2): 150-155.
[7]
BERBERDIS K and RONTOGIANNIS A A. Efficient decision feedback equalizer for sparse multipath channels[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Istanbul, Turkey, 2000: 2725-2728.
[8]
LEE F K H and MCLANE P J. Design of nonuniformly- spaced tapped-delay-line equalizers for sparse multipath channels[C]. Global Telecommunications Conference, Mumbai, India, 2001, 2: 1336-1343.
[9]
VLACHOS E, LALOS A S, and BERBERIDIS K. Stochastic gradient pursuit for adaptive equalization of sparse multipath channels[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012, 2(3): 413-423.
[10]
HELMY A, HEDAYAT A, and AL-DHAHIR N. Robust weighted sum-rate maximization for the multi-stream MIMO interference channel with sparse equalization[J]. IEEE Transactions on Communications, 2015, 60(10): 3645-3659.
[11]
SILVA L and GOMES J. Sparse channel estimation and equalization for underwater filtered multitone[C]. OCEANS 2015, Genova, Italy, 2015: 1-8.
[12]
TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research, 2001, 1: 211-244.
[13]
HANSEN T L, BADIU M, FLEURY B H, et al. A sparse Bayesian learning algorithm with dictionary parameter estimation[C]. Sensor Array and Multichannel Signal Processing Workshop, A Coruña, Spain, 2014: 385-388.
[14]
GODARD D N. Method and device for training an adaptive equalizer by means of an unknown data signal in a transmission system using double sideband-quadrature carrier modulation [P]. U.S. Patent 4, 309, 770. 1982.
[15]
WIPF D P. Sparse estimation with structured dictionaries[C]. Advances in Neural Information Processing Systems, Granada, Spain, 2011: 2016-2024.
[16]
KAY S. M. (美), 罗鹏飞, 张文明, 等译. 统计信号处理基础: 估计与检测理论[M]. 北京，电子工业出版社, 2003: 277-334.
[17]
GOMAA A and AL-DHAHIR N. Sparse FIR equalization: a new design framework[C]. Vehicular Technology Conference, Budapest, Hungary, 2011: 1-5.
