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Spatial multiplexing and channel statistics-based RF pre-processing for antenna selection

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Author

  • P Sudarshan
  • N B Mehta
  • Andreas Molisch
  • J Zhang

Summary, in English

For a multiple input multiple output system, antenna selection reduces complexity at the expense of performance. In this paper, we propose two novel RF pre-processing architectures that significantly improve the performance of antenna selection, while marginally increasing the complexity. These architectures introduce an RF pre-processing matrix, M, that multiplies the vector of incoming signals prior to downconversion. The elements of M use only the knowledge of the channel statistics. In the first architecture, M outputs a reduced number of streams - an explicit selection algorithm is therefore not required. In the second architecture, the number of output streams equals the number of input streams, and the reduction of the number of streams is achieved by a selection switch that uses instantaneous channel state information. We show that the optimal pre-processing receiver projects the received signal along the eigenvectors of the correlation matrix. In a correlated channel, both these architectures significantly outperform conventional antenna selection. We also develop a beam-pattern based intuition and compare the performance of our scheme to other RF preprocessing schemes previously proposed in the literature.

Publishing year

2004

Language

English

Pages

3947-3951

Publication/Series

IEEE Global Telecommunications Conference, GLOBECOM '04.

Volume

6

Document type

Conference paper

Topic

  • Electrical Engineering, Electronic Engineering, Information Engineering

Keywords

  • MIMO systems
  • antenna radiation patterns
  • array signal processing
  • correlation methods
  • diversity reception
  • eigenvalues and eigenfunctions
  • multiplexing

Conference name

IEEE Global Communications Conference (GLOBECOM), 2004

Conference date

2004-11-29 - 2004-12-03

Conference place

Dallas, TX, United States

Status

Published

ISBN/ISSN/Other

  • ISBN: 0-7803-8794-5