Receive antenna selection for mimo flat-fading channels: theory and algorithms

Gorokhov, A.; Gore, D.; Paulraj, A.

doi:10.1109/tit.2003.817458

Cited by 143 publications

(92 citation statements)

References 15 publications

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“…A MIMO system with antenna selection (AS) has been shown to significantly outperform a system exploiting the same number of RF chains without AS. To deal with such challenges, a promising technique referring to antenna subset selection has been proposed in [7]- [10]. The key idea of AS is to use a limited number of RF chains that are adaptively switched to a subset of the available antennas, which can effectively reduce the number of RF chains required, yet preserving the selection diversity gains.…”

Section: International Journal On Smart Sensing and Intelligent Systementioning

confidence: 99%

Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information

Zhuang

2015

International Journal on Smart Sensing and Intelligent Systems

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Section: International Journal On Smart Sensing and Intelligent Systementioning

confidence: 99%

Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information

Zhuang

2015

International Journal on Smart Sensing and Intelligent Systems

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“…However, the operations of eigenvalue decomposition (EVD) still consume a certain complexity. To further reduce the complexity and employ less analog chains at the receivers (Gorokhov et al, 2003), RAB can be replaced by receive antenna selection (RAS). Specifically, for a layer and a candidate user, instead of computing the the eigenvector, we just select the receive antenna whose equivalent channel vector has the maximum Euclidean norm, as shown in Table 1-b.…”

Section: Per-layer Transmit and Receive Filters Designmentioning

confidence: 99%

Analysis and Design of Tomlinson-Harashima Precoding for Multiuser MIMO Systems

Chen¹,

Huang²,

Zhou³

et al. 2011

MIMO Systems, Theory and Applications

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“…A suboptimal algorithm that does not need to perform an exhaustive search over all possible subsets is proposed in [4]. A statistical analysis to relate the capacity when the whole set of antennas is used and the channel capacity with subset selection is reported in [5].…”

Section: Ultiple-input-multiple-outputmentioning

confidence: 99%

MIMO Antenna Selection With Lattice-Reduction-Aided Linear Receivers

Berenguer

Wang

2004

IEEE Trans. Veh. Technol.

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Abstract-A method to improve the performance of multiple-input-multiple-output systems is to employ a large number of antennas and select the optimal subset depending on the specific channel realization. A simple antenna-selection criterion is to choose the antenna subset that maximizes the mutual information. However, when the receiver has finite complexity decoders, this criterion does not necessarily minimize the error rate (ER). Therefore, different selection criteria should be tailored to the specific receiver implementation. In this paper, we develop new antenna-selection criteria to minimize the ER in spatial multiplexing systems with lattice-reduction-aided receivers. We also adapt other known selection criteria, such as maximum mutual information, to this specific receiver. Moreover, we consider adaptive antenna-selection algorithms when the channel is not perfectly known at the receiver but can only be estimated. We present simulation examples to show the ER of the different selection criteria and the convergence of the adaptive algorithms. We also discuss the difference in complexity and performance among them.Index Terms-Adaptive algorithm, antenna selection, lattice reduction, minimum error rate (ER), multiple-input-multiple-output (MIMO).

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Receive antenna selection for mimo flat-fading channels: theory and algorithms

Cited by 143 publications

References 15 publications

Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information

Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information

Analysis and Design of Tomlinson-Harashima Precoding for Multiuser MIMO Systems

MIMO Antenna Selection With Lattice-Reduction-Aided Linear Receivers

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