Training sequence design for MIMO channels: An application-oriented approach

Dimitrios Katselis, Cristian R. Rojas, Mats Bengtsson, Emil Björnson, Xavier Bombois, Nafiseh Shariati, Magnus Jansson, Håkan Hjalmarsson

Research output: Contribution to journalArticlepeer-review


In this paper, the problem of training optimization for estimating a multiple-input multiple-output (MIMO) flat fading channel in the presence of spatially and temporally correlated Gaussian noise is studied in an application-oriented setup. So far, the problem of MIMO channel estimation has mostly been treated within the context of minimizing the mean square error (MSE) of the channel estimate subject to various constraints, such as an upper bound on the available training energy. We introduce a more general framework for the task of training sequence design in MIMO systems, which can treat not only the minimization of channel estimator’s MSE but also the optimization of a final performance metric of interest related to the use of the channel estimate in the communication system. First, we show that the proposed framework can be used to minimize the training energy budget subject to a quality constraint on the MSE of the channel estimator. A deterministic version of the ‘dual’ problem is also provided. We then focus on four specific applications, where the training sequence can be optimized with respect to the classical channel estimation MSE, a weighted channel estimation MSE and the MSE of the equalization error due to the use of an equalizer at the receiver or an appropriate linear precoder at the transmitter. In this way, the intended use of the channel estimate is explicitly accounted for. The superiority of the proposed designs over existing methods is demonstrated via numerical simulations.

Original languageEnglish (US)
JournalEurasip Journal on Wireless Communications and Networking
Issue number1
StatePublished - Oct 17 2013
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computer Networks and Communications


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