SCF: Sparse channel-state-information feedback using Karhunen-Lo&amp;#x00E8;ve transform

Joung, Jingon; Sun, Sumei

doi:10.1109/glocomw.2014.7063450

Cited by 12 publications

(3 citation statements)

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“…For example, the authors of [5], [6] propose a two-stage precoding scheme termed joint spatial division and multiplexing (JSDM) which, by judiciously exploiting the structure of the correlation of the channel vectors, achieves significant savings in uplink feedback, thus potentially enabling frequency division duplex (FDD) massive MIMO. In the same vein, [7]- [9] suggest feedback compression algorithms which rely on low-rank approximations to the covariance matrix of the channel vectors.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting antenna correlation in measured massive MIMO channels

Flordelis

Rusek

et al. 2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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We investigate antenna correlation of an Mantenna massive multiple-input multiple-output (MIMO) setup with the purpose of obtaining a low-rank representation of the instantaneous massive MIMO channel. Low-rank representation bases using short-term and long-term antenna correlation statistics are defined, and their performance is evaluated with data sets obtained from channel measurements in both indoor and outdoor environments at 2.6 GHz. Our results indicate that the shortterm bases can capture a larger amount of the channel energy compared to the long-term ones, but they have a limited timespan, one coherence time or less. On the other hand, the long-term bases are stable over time-spans of a few seconds. Hence, they can be obtained relatively easily. We also investigate a rank-p vector-scalar LMMSE channel estimator that exploits antenna correlation. Our results show that the investigated estimator can achieve a performance similar to that of full-rank LMMSE at a (2p + 1)/M times lower cost. The investigated estimator may be used in conjunction with estimators that exploit correlation in the frequency and time domains or, alternatively, in situations in which these estimators cannot be used, e.g., when pilot separation is larger than the channel coherence bandwidth or time.

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Section: Introductionmentioning

confidence: 99%

“…However, although techniques that exploit the structure of the spatial correlation of the massive MIMO channel have recently received a fair amount of attention [5]- [9], little [10] seems to have been reported on measurements of such correlations. This paper has two main contributions.…”

Section: Introductionmentioning

confidence: 99%

Exploiting antenna correlation in measured massive MIMO channels

Flordelis

Rusek

et al. 2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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“…The CSIT can be typically realized either by uplink (from receiver (Rx) to Tx) channel estimation at a Tx in timedivision duplex (TDD) systems, e.g., implicit feedback in IEEE 802.11n [8], or by CSI feedback from an Rx to a Tx in frequency-division duplex (FDD) systems, e.g., explicit feedback in 802.11ac [9]. Phase calibration improves the Part of this work has been published in Proceedings of the IEEE Globecom 2014, Austin, TX, USA, December 2014 [1].…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis (PCA)-based Massive-MIMO Channel Feedback

Joung¹,

Kurniawan²,

Sun

2015

Preprint

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Channel-state-information (CSI) feedback methods are considered, especially for massive or very large-scale multipleinput multiple-output (MIMO) systems. To extract essential information from the CSI without redundancy that arises from the highly correlated antennas, a receiver transforms (sparsifies) a correlated CSI vector to an uncorrelated sparse CSI vector by using a Karhunen-Loève transform (KLT) matrix that consists of the eigen vectors of covariance matrix (CM) of CSI vector and feeds back the essential components of the sparse CSI, i.e., a principal component analysis method. A transmitter then recovers the original CSI through the inverse transformation of the feedback vector. Herein, to obtain the CM at transceiver, we derive analytically the CM of spatially correlated Rayleigh fading channels based on its statistics including transmit antennas' and receive antennas' correlation matrices, channel variance, and channel delay profile. With the knowledge of the channel statistics, the transceiver can readily obtain the CM and KLT matrix. Compression feedback error and bit-error-rate performance of the proposed method are analyzed. Numerical results verify that the proposed method is promising, which reduces significantly the feedback overhead of the massive-MIMO systems with marginal performance degradation from full-CSI feedback (e.g., feedback amount reduction by 80%, i.e., 1 5 of original CSI, with spectral efficiency reduction by only 2%). Furthermore, we show numerically that, for a given limited feedback amount, we can find the optimal number of transmit antennas to achieve the largest spectral efficiency, which is a new design framework.

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