Abstract
A new semi-blind adaptive beamforming scheme is proposed for multi-input multi-output (MIMO) induced and space-division multiple-access based wireless systems that employ high order phase shift keying signaling. A minimum number of training symbols, very close to the number of receiver antenna elements, are used to provide a rough initial least squares estimate of the beamformer’s weight vector. A novel cost function combining the constant modulus criterion with decision-directed adaptation is adopted to adapt the beamformer weight vector. This cost function can be approximated as a quadratic form with a closed-form solution, based on which we then derive the recursive least squares (RLS) semi-blind adaptive beamforming algorithm. This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated in our simulation study. Our proposed semi-blind RLS beamforming algorithm therefore provides an efficient detection scheme for the future generation of MIMO aided mobile communication systems.
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Recommended by Associate Editor Qing-Long Han
Xia Hong received the B. Sc. and M. Sc. degrees from the National University of Defense Technology, China, in 1984 and 1987, respectively, and received the Ph.D. degree from The University of Sheffield, UK in 1998, all in automatic control. She was a research assistant with the Beijing Institute of Systems Engineering, China, from 1987 to 1993. She was a research fellow with the Department of Electronics and Computer Science, University of Southampton, UK from 1997 to 2001. She is currently a professor with the Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, UK. She has authored over 200 research papers, and co-authored a research book. She received the Donald Julius Groen Prize from IMechE in 1999.
Her research interests include nonlinear systems identification, data modelling, estimation and intelligent control, neural networks, pattern recognition, learning theory and their applications.
Sheng Chen received the B.Eng. degree in control engineering from the East China Petroleum Institute, China in 1982, received the Ph.D. degree in control engineering from City University, UK in 1986, and received the D. Sc. degree from the University of Southampton, UK in 2005. From 1986 to 1999, he held research and academic appointments at The University of Sheffield, UK, the University of Edinburgh, UK, and the University of Portsmouth, UK. Since 1999, he has been with the Electronics and Computer Science, University of Southampton, UK, where he is currently a professor in intelligent systems and signal processing. He has authored over 550 research papers. He is a Fellow of IET, a Distinguished Adjunct Professor with King Abdulaziz University, Saudi Arabia, and an ISI highly cited researcher in engineering in 2004. He was elected to a Fellow of the United Kingdom Royal Academy of Engineering in 2014.
His research interests include adaptive signal processing, wireless communications, modeling and identification of nonlinear systems, neural network and machine learning, intelligent control system design, evolutionary computation methods, and optimisation.
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Hong, X., Chen, S. Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion. Int. J. Autom. Comput. 14, 442–449 (2017). https://doi.org/10.1007/s11633-017-1087-6
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DOI: https://doi.org/10.1007/s11633-017-1087-6