Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems

Noh, Song; Zoltowski, M.D.; Sung, Youngchul; Love, David J.

doi:10.1109/jstsp.2014.2327572

Cited by 217 publications

(180 citation statements)

References 54 publications

(118 reference statements)

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“…1 It is worthwhile to mention that channel prediction techniques are used for downlink frequency division duplex (FDD) massive MIMO as well [32]- [34].…”

Section: Channel Predictionmentioning

confidence: 99%

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Papazafeiropoulos

Ratnarajah

2015

IEEE Trans. Wireless Commun.

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Abstract-Delayed channel state information at the transmitter (CSIT) due to time variation of the channel, coming from the users' relative movement with regard to the BS antennas, is an inevitable degrading performance factor in practical systems. Despite its importance, little attention has been paid to the literature of multi-cellular multiple-input massive multiple-output (MIMO) system by investigating only the maximal ratio combining (MRC) receiver and the maximum ratio transmission (MRT) precoder. Hence, the contribution of this work is designated by the performance analysis/comparison of/with more sophisticated linear techniques, i.e., a minimum-mean-square-error (MMSE) detector for the uplink and a regularized zero-forcing (RZF) precoder for the downlink are assessed. In particular, we derive the deterministic equivalents of the signal-to-interference-plus-noise ratios (SINRs), which capture the effect of delayed CSIT, and make the use of lengthy Monte Carlo simulations unnecessary. Furthermore, prediction of the current CSIT after applying a Wiener filter allows to evaluate the mitigation capabilities of MMSE and RZF. Numerical results depict that the proposed achievable SINRs (MMSE/RZF) are more efficient than simpler solutions (MRC/MRT) in delayed CSIT conditions, and yield a higher prediction at no special computational cost due to their deterministic nature. Nevertheless, it is shown that massive MIMO are preferable even in time-varying channel conditions.

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“…1 It is worthwhile to mention that channel prediction techniques are used for downlink frequency division duplex (FDD) massive MIMO as well [32]- [34].…”

Section: Channel Predictionmentioning

confidence: 99%

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Papazafeiropoulos

Ratnarajah

2015

IEEE Trans. Wireless Commun.

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“…denote the DFT matrix, in which ω m = −1+2(m−1)/M, ∀m, and α(ω m ) is defined in (14). Let R = U ΛU H denote the truncated eigenvalue decomposition, where U ∈ C M×r , and Λ ∈ C r×r .…”

Section: Asymptotically Optimal Pilot For Multi-user Scenariosmentioning

confidence: 99%

“…Our simulation results, however, show that, for a finite number of antennas, this covariance estimation approximation is not accurate enough and a MMSE estimator based on this covariance approximation even leads to deteriorated estimation performance. In addition, as indicated in [14], the estimation of the angular power spectrum requires additional training overhead and computational cost.…”

Section: Algorithm 1 Estimated Covariance-assisted Mmse (Ec-mmse)mentioning

confidence: 99%

Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems

Fang

et al. 2017

IEEE Trans. Wireless Commun.

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Abstract-We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low-rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Lastly, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.Index Terms-Massive MIMO systems, downlink training and channel estimation, channel covariance matrix, low rank structure, MMSE estimator.

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“…We now study problem (21) under the channel models (3) and (4). In Section V-A, we consider the fully-separable model in (3), under which we derive a closed-form solution for the pilot sequences S i .…”

Section: Algorithm 1 Magiq -Minimal Gap Iterative Quantizationmentioning

confidence: 99%

Pilot contamination mitigation with reduced RF chains

Ioushua

Eldar

2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Abstract-Massive multiple-input multiple-output (MIMO) communication is a promising technology for increasing spectral efficiency in wireless networks. Two of the main challenges massive MIMO systems face are degraded channel estimation accuracy due to pilot contamination and increase in computational load and hardware complexity due to the massive amount of antennas. In this paper, we focus on the problem of channel estimation in massive MIMO systems, while addressing these two challenges: We jointly design the pilot sequences to mitigate the effect of pilot contamination and propose an analog combiner which maps the high number of sensors to a low number of RF chains, thus reducing the computational and hardware cost. We consider a statistical model in which the channel covariance obeys a Kronecker structure. In particular, we treat two such cases, corresponding to fully-and partially-separable correlations. We prove that with these models, the analog combiner design can be done independently of the pilot sequences. Given the resulting combiner, we derive a closed-form expression for the optimal pilot sequences in the fully-separable case and suggest a greedy sum of ratio traces maximization (GSRTM) method for designing sub-optimal pilots in the partially-separable scenario. We demonstrate via simulations that our pilot design framework achieves lower mean squared error than the common pilot allocation framework previously considered for pilot contamination mitigation.

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Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems

Cited by 217 publications

References 54 publications

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Deterministic Equivalent Performance Analysis of Time-Varying Massive MIMO Systems

Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems

Pilot contamination mitigation with reduced RF chains

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