Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method

At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of generated symbols on a time scale, resulting in symbol overlap and Inter-symbol Interference (ISI). ISI in the signal must be removed and the signal restores to its original state at the time of transmission or becomes as close to it as possible. Blind equalization is a popular equalization method for recovering transmitted symbols of superimposed noise without any pilot signal. In this work, we propose a concurrent modified constant modulus algorithm (MCMA) and the decision-directed scheme (DDS) with the Barzilai-Borwein (BB) method for the purpose of blind equalization of wireless communications systems (WCS). The BB method, which is two-step gradient method, has been widely employed to solve multidimensional unconstrained optimization problems. Considering the similarity of equalization process and optimization process, the proposed algorithm combines existing blind equalization algorithm and Barzilai-Borwein method, and concurrently operates a MCMA equalizer and a DD equalizer. After that, it modifies the DD equalizer's step size (SS) by the BB method. Theoretical investigation was involved and it demonstrated rapid convergence and improved equalization performance of the proposed algorithm compared with the original one. Additionally, the simulation results were consistent with the proposed technique.

[1]  Namyong Kim,et al.  Blind signal processing for impulsive noise channels , 2012, Journal of Communications and Networks.

[2]  W. Hager,et al.  The cyclic Barzilai-–Borwein method for unconstrained optimization , 2006 .

[3]  Esam Abdel-Raheem,et al.  Joint MCMA and DD blind equalization algorithm with variable-step size , 2009, 2009 IEEE International Conference on Electro/Information Technology.

[4]  Chng Eng Siong,et al.  Concurrent constant modulus algorithm and soft decision directed scheme for fractionally-spaced blind equalization , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[5]  Tzu-Chao Lin,et al.  Equalization and Carrier Phase Recovery of CMA and MMA in Blind Adaptive Receivers , 2010, IEEE Transactions on Signal Processing.

[6]  J. Borwein,et al.  Two-Point Step Size Gradient Methods , 1988 .

[7]  Paulo S. R. Diniz.  The Least-Mean-Square (LMS) Algorithm , 2013 .

[8]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[9]  Shiqian Ma,et al.  Barzilai-Borwein Step Size for Stochastic Gradient Descent , 2016, NIPS.

[10]  Zhongbao Luo,et al.  Adaptive sliding mode control of robot based on fuzzy neural network , 2020, J. Ambient Intell. Humaniz. Comput..

[11]  Yurii Nesterov,et al.  Gradient methods for minimizing composite functions , 2012, Mathematical Programming.

[12]  Jian Yang,et al.  The multimodulus blind equalization and its generalized algorithms , 2002, IEEE J. Sel. Areas Commun..

[13]  Eweda Eweda,et al.  Convergence analysis of self-adaptive equalizers , 1984, IEEE Trans. Inf. Theory.

[14]  Philip Schniter,et al.  Blind equalization using the constant modulus criterion: a review , 1998, Proc. IEEE.

[15]  Wei Shi,et al.  A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates , 2017, IEEE Transactions on Signal Processing.

[16]  Ivan V. Bajic,et al.  Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering , 2015, IEEE Signal Processing Letters.

[17]  Syed Muslim Shah,et al.  Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization , 2017, 1802.09252.

[18]  Abdelmojeb Elsidig,et al.  Rayleigh Fading Channel Equalization using Neural Networks , 2018, 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE).

[19]  Variable Step-size CMA Blind Equalization based on Non-linear Function of Error Signal , 2009, 2009 WRI International Conference on Communications and Mobile Computing.