Spatial modulation with partial-CSI at the receiver: optimal detector and performance evaluation

  • Authors:

  • Marco Di Renzo;Harald Haas

  • Affiliations:

  • French National Center for Scientific Research, Laboratory of Signals and Systems, École Supérieure d' Électricité, Gif-sur-Yvette, Paris, France;The University of Edinburgh, Institute for Digital Communications, College of Science and Engineering, Edinburgh, UK

  • Venue:
  • Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
  • Year:
  • 2010

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Abstract

Spatial Modulation (SM) is a novel and recently proposed transmission technology for Multiple-Input-Multiple-Output (MIMO) wireless communication systems, which has been shown to be a promising alternative to several popular MIMO schemes. So far, only optimal or heuristic transceivers with Full Channel State Information (F-CSI) at the receiver have been investigated, and their performance analyzed over fading channels. However, in several circumstances, channel fading might be sufficiently rapid to preclude the availability of the perfect knowledge of CSI at the receiver, and, in particular, the estimation of a stable phase reference. Motivated by this consideration, in this paper we develop the optimal detector for SM with unknown phase reference at the receiver (i.e., Partial-CSI, P-CSI, knowledge), which inevitably leads to a sub-optimal receiver design. An analytical framework will be developed for the accurate performance analysis of this novel detector over fading channels, and its performance will be compared to the optimal receiver design with F-CSI. Numerical results will point out that, unlike ordinary modulation schemes, there is a substantial performance loss when the receiver cannot exploit the phase information for optimal detection operations. This result highlights the importance of accurate and reliable channel estimation mechanisms for the efficient operation of SM over fading channels. Analytical frameworks and theoretical findings will also be substantiated via Monte Carlo simulations.