On the Role of Transmit Correlation Diversity in Multiuser MIMO Systems

Nam, Jeonghun; Caire, Giuseppe; Ko, Young-Jo; Ha, Jeongseok

doi:10.1109/tit.2016.2615627

Cited by 63 publications

(57 citation statements)

References 53 publications

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“…A potential issue of the conventional M -dimensional MMSE channel estimation is ... In terms of computational complexity 2) (Intra-Cell) Non-Orthogonal Pilot Scheme: It is known [3], [8] that the conventional orthogonal pilot scheme may significantly limit the sum-rate performance of MU-MIMO systems unless max K is smaller than T c /2. To overcome this limiting factor, we also consider the non-orthogonal pilot scheme.…”

Section: Low-dimensional Channel Estimationmentioning

confidence: 99%

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes

Nam

Caire

Debbah

et al. 2020

IEEE Trans. Inform. Theory

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This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the "random sparse angular support" model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We note that the achievable rate lower bound based on massive MIMO "channel hardening", widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening. Index TermsLarge-scale MIMO, asymptotic capacity scaling, multiplexing gain, correlated fading channels. arXiv:1812.08898v1 [cs.IT] 21 Dec 2018 K M = 0, i.e., K is finite or at most grows slower than M . For instance, if KL > M , the linear independence of the subspaces of covariance matrices in [12], [13] is never attainable. Likewise, if lim inf M →∞ K M > 0, then the asymptotic linear independence of covariance matrices in [16] does not hold any longer. Therefore, the results based on the linear independence of the signal subspaces or of the covariance matrices cannot capture the capacity scaling with respect to the ratio K M > 0, as both M and K grow large. This poses a fundamental methodological question since in reality we are in the presence of a finite system with given M and K. Which of the following large system analyses will produce the more meaningful prediction of its behavior: considering finite K and letting M → ∞ [1], [2], [16] or considering both K and M → ∞ with fixed ratio equal to the actual ratio K M of the practical finite system [4]? We claim that the latter methodology yields a more relevant asymptotics.

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Section: Low-dimensional Channel Estimationmentioning

confidence: 99%

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes

Nam

Caire

Debbah

et al. 2020

IEEE Trans. Inform. Theory

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“…In stark contrast, a different set of investigations has shown that correlation can enhance MU-MIMO performance [11][12][13][14][15][16][17][18][19][20]. Collectively, a critical observation from these studies is that the departing spread of electromagnetic energy from the BS can arrive at the terminals with substantially different power angular spectra, leading to variations in the channel statistics.…”

Section: Introductionmentioning

confidence: 99%

“…Such models are characterized in terms of the mean direction-of-arrival (DOA) at a terminal, angular spread of departure, as well as the antenna spacing at the BS. The authors of [11,12] utilized the one-ring model to group the terminals with similar correlation characteristics in order to motivate the joint spatial division multiplexing technique. The study in [14] shows that if correlation matrices span orthogonal subspaces, pilot contamination can vanish, allowing the ergodic spectral efficiency to grow without bound with increasing numbers of BS antennas.…”

Section: Introductionmentioning

confidence: 99%

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Channel Correlation Diversity in MU-MIMO Systems – Analysis and Measurements

Tataria

Sangodoyin

Molisch

et al. 2019

2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signalto-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.

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“…When time-division duplexing (TDD) is employed, downlink CSI can be extracted from uplink signals using the channel reciprocity. However, since frequency-division duplexing (FDD) is used in most existing cellular systems, effective CSI feedback schemes need to be developed to minimize the feedback overhead while maintaining high system throughputs [4]- [12].…”

Section: Introductionmentioning

confidence: 99%

System-Level Performance of Limited Feedback Schemes for Massive MIMO

Choi

Lee

Kang

et al. 2016

ETRI J

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To implement high-order multiuser multiple input and multiple output (MU-MIMO) for massive MIMO systems, there must be a feedback scheme that can warrant its performance with a limited signaling overhead. The interference-to-noise ratio can be a basis for a novel form of Codebook (CB)-based MU-MIMO feedback scheme. The objective of this paper is to verify such a scheme's performance under a practical system configuration with a 3D channel model in various radio environments. We evaluate the performance of various CB-based feedback schemes with different types of overhead reduction approaches, providing an experimental ground with which to optimize a CB-based MU-MIMO feedback scheme while identifying the design constraints for a massive MIMO system. Keywords: Massive MIMO, FD-MIMO, 3D-channel model, codebook-based feedback, system level simulation, interference-to-noise ratio, multiuser interference indicator, MUI, INR.

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On the Role of Transmit Correlation Diversity in Multiuser MIMO Systems

Cited by 63 publications

References 53 publications

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes

Channel Correlation Diversity in MU-MIMO Systems – Analysis and Measurements

System-Level Performance of Limited Feedback Schemes for Massive MIMO

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