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The BEAST for maximum-likelihood detection in non-coherent MIMO wireless systems

Publiceringsår: 2010
Språk: Engelska
Publikation/Tidskrift/Serie: [Host publication title missing]
Dokumenttyp: Konferensbidrag


Next generation wireless systems have to be able to efficiently deal with fast fading environments in order to achieve high spectral efficiency. Using multiple-input multiple-output (MIMO) systems and exploiting receive diversity, their spectral efficiency can be greatly increased. Commonly, the channel is estimated via training symbols, before the data detection is carried out based on the previously obtained channel estimate. While this significantly simplifies the process of data detection, it leads in general to suboptimal results. Thereby, a better approach is given by carrying out joint maximum-likelihood (ML) channel estimation and data detection.

In this paper, the BEAST — Bidirectional Efficient Algorithm for Searching code Trees — is proposed as an alternative algo- rithm for joint ML channel estimation and signal detection and its complexity is compared with recently published algorithms in this field.


  • Electrical Engineering, Electronic Engineering, Information Engineering


IEEE International Conference on Communications, ICC 2010
  • Information Theory-lup-obsolete
  • Telecommunication Theory-lup-obsolete

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