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SCIENCE CHINA Information Sciences, Volume 59, Issue 6: 062302(2016) https://doi.org/10.1007/s11432-015-5390-y

Adaptive robust Max-SLNR precoder for MU-MIMO-OFDM systems with imperfect CSI

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  • ReceivedMay 13, 2015
  • AcceptedJun 15, 2015
  • PublishedSep 6, 2015

Abstract

The accuracy of channel state information (CSI) available at a base station (BS) has a direct impact on the performance of precoding in wideband multi-user multiple input, multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems and depends on many factors, including: the delay between estimation and beamforming at the BS (also called the CSI delay), Doppler spread, the channel estimation method used, the average transmit power of pilot symbols, and the average number of pilot symbols that must be estimated per channel parameter. In this paper, the coefficient of CSI error needed to adapt to fading channels is modeled as a function of Doppler spread, CSI delay, and signal-to-noise ratio (SNR). In terms of the Gaussian-Markov CSI error model, an adaptive robust maximum signal-to-leakage-and-noise ratio (Max-SLNR) precoder is designed to track the statistical parameters of CSI error. The Doppler spread and SNR can be obtained through real-time estimation based on orthogonal pilot patterns. Simulation results show that, compared to non-adaptive robust and non-robust precoders of Max-SLNR, the proposed adaptive robust Max-SLNR precoder performs much better in terms of bit error rate (BER). Moreover, as either the average number of training symbols per channel parameter or the average transmit power increases, the BER performance of the proposed precoder approaches that of a precoder with ideal CSI.


Acknowledgment

Acknowledgments

This work was supported in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University (Grant No. 2013D02), the Fundamental Research Funds for the Central Universities (Grant No. 30920130122004), and the National Natural Science Foundation of China (Grant Nos. 61271230, 61472190).


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