Telecommunication Systems

, Volume 64, Issue 4, pp 709–717 | Cite as

A support vector regression approach to detection in large-MIMO systems

Article
First Online:

Abstract

We propose a support vector regression approach for symbol detection in large-MIMO systems employing spatial multiplexing. We explore the applicability of machine learning algorithms, in particular support vector machines, to address one of the recent research problem in communications.The machine learning capability is exploited to achieve fast detection in large dimension systems. The performance of the proposed method is compared with lattice reduction aided detection which is currently the popular choice and the improvement in terms of bit error rate is demonstrated. The sparse formulation of the problem matrix reduces the computational complexity and enables faster detection. The proposed detection algorithm is tailored to address both uncorrelated and correlated channel conditions as well.

Keywords

Large-MIMO detection Spatial correlation Support vector regression Maximum likelihood 
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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication Engineering, Amrita School of EngineeringAmrita Vishwa Vidyapeetham, Amrita UniversityCoimbatoreIndia

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