Adaptive quantization on the Grassmann-manifold for limited feedback multi-user MIMO systems

Schwarz, Štefan; Heath, Robert W.; Rupp, Markus

doi:10.1109/icassp.2013.6638617

Cited by 21 publications

(6 citation statements)

References 28 publications

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“…Notice that similar trends are observed with other codebooks as well, e.g., in [1] we simulate the predictive quantizer of [16] in combination with SQBC. Irrespective of the codebook construction, the combiner always generates the effective channel that can be quantized with minimal quantization error.…”

Section: Throughput Analysissupporting

confidence: 66%

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Subspace Quantization Based Combining for Limited Feedback Block-Diagonalization

Schwarz¹,

Rupp²

2013

IEEE Trans. Wireless Commun.

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Spatial multiplexing of multiple users in a multiantenna broadcast system, i.e., multi-user MIMO, is a promising technique for improving the spectral efficiency of cellular networks. Still, practical implementations struggle with the difficulty of obtaining sufficiently accurate channel state information at the transmitter (CSIT), due to restrictions on the capacity of the CSI feedback links from the users. Without accurate CSIT, residual multi-user interference diminishes the potential performance gain of multi-user MIMO, challenging its value for practical realizations. In this work we show that the CSI feedback overhead can be significantly reduced if the number of spatially multiplexed data streams per user is less than the number of receive antennas, and the antenna outputs are appropriately combined. We propose an interference-unaware antenna combining algorithm that minimizes the CSI quantization error, which has the effect of minimizing multi-user interference at the price of reducing the received signal power. We mathematically analyze the performance of the proposed algorithm, substantiating its value for limited feedback systems. Numerical simulations confirm the potential of the proposed algorithm in comparison to a traditional technique that maximizes the received signal power.Index Terms-Limited feedback, quantized feedback, multiuser MIMO, block-diagonalization, antenna combining.

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Section: Throughput Analysissupporting

confidence: 66%

“…Several authors have proposed possible codebook constructions; see, e.g., [12], [13], [14], [15], [16] and references therein. Although these codebooks enable efficient subspace quantization, they are not suitable for analytical performance investigations.…”

Section: Limited Feedback Modelmentioning

confidence: 99%

Subspace Quantization Based Combining for Limited Feedback Block-Diagonalization

Schwarz¹,

Rupp²

2013

IEEE Trans. Wireless Commun.

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“…Specifically, the channel matrices H[k] of dimension n × m = 6 × 2 are from a Rayleigh fading distribution and are temporally correlated according to Clarke's Doppler spectrum [27] by employing the sum-of-sinusoids approach of [34]. The predictive and differential quantizers, as proposed in [21], provide 6 bits of feedback per time instant. For comparison, we also show the performance of memoryless single-stage quantization (without selective CSI update) for 9, 26, 39 and 53 bits of feedback, respectively.…”

Section: Simulationsmentioning

confidence: 99%

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

Schwarz

2020

IEEE Signal Process. Lett.

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In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single-and multiuser MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this paper, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.

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“…In our simulations in Section 6, we do not employ BD precoding in combination with our CSI feedback methods, but we rather assume perfect knowledge of H u or its estimateĤ u . We do this since BD precoding requires phase information which is not provided by our CSI feedback; see, e.g., [33], for an efficient CSI feedback method that can be applied with BD precoding.…”

Section: Block-diagonalization Precodingmentioning

confidence: 99%

“…The phases of multipath components, however, can change very quickly over time, especially for mobile users, and hence coherent beamforming/precoding methods react very sensitive to non-static scenarios [32,33]. The corresponding performance degradation can partly be mitigated by robust coherent beamforming/precoding techniques that account for the CSIT imperfection.…”

Section: Introductionmentioning

confidence: 99%

Robust full-dimension MIMO transmission based on limited feedback angular-domain CSIT

Schwarz

2018

J Wireless Com Network

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In this paper, we propose robust multi-user beamforming and precoding techniques for full-dimension MIMO transmission based on limited feedback. We propose to employ an over-complete basis decomposition in the angular domain to approximate the channel matrices as sums of few dominant specular components, facilitating efficient channel state information (CSI) quantization at the users. The selected expansion vectors of such a sparse approximation, parametrized by azimuth and elevation angles, are relatively robust with respect to channel estimation errors as well as channel variations over time. Based on this CSI feedback, we propose incoherent beamforming/precoding methods that make use only of the azimuth and elevation angles as well as the norm of the expansion coefficients and do not rely on coherent multipath interference to eliminate inter-user interference. Our optimization aims at maximizing the signal power or the achievable rate of a user, while limiting the amount of interference leakage caused to other users. To further improve robustness, we account for uncertainty in the angular channel decomposition in the proposed precoder optimization.

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Adaptive quantization on the Grassmann-manifold for limited feedback multi-user MIMO systems

Cited by 21 publications

References 28 publications

Subspace Quantization Based Combining for Limited Feedback Block-Diagonalization

Subspace Quantization Based Combining for Limited Feedback Block-Diagonalization

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

Robust full-dimension MIMO transmission based on limited feedback angular-domain CSIT

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