A non-stationary channel model for 5G massive MIMO systems

Chen, Jian-qiao; Zhang, Zhi; Tian, Tao; Huang, Yuzhen

doi:10.1631/fitee.1700028

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…The nonstationary properties associated to the channel clusters can be observed on large antenna arrays due to the possibility of different antenna elements observe different sets of clusters, 12 where the set of antenna elements observed is called VR. In M‐MIMO configurations, it is observed significant nonstationaries across antennas elements when the MTs are close to the antenna array, as reported by measurements in Reference 6, being necessary consider from a spatial perspective 29 .…”

Section: Channel Models For M‐mimo and Xl‐mimomentioning

confidence: 99%

“…The spherical wave channel modeling based on electromagnetic field describes with more accuracy the near‐field propagation if compared with plane‐wave propagation; hence the models present different results in capacity 5,11 . Moreover, the nonstationary properties of the channel clusters can be observed over the large antenna arrays due to the possibility of different antenna elements observe different sets of clusters 12 . This property is paramount in scenarios XL‐MIMO and must be considered from a spatial perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Very recent researches on the nonstationarities channel modeling include 12‐16 . In Reference 13 a channel model was proposed for vehicle‐to‐vehicle communication environments, in which the spherical wave‐front is assumed.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the nonstationarity property is evaluated, however, in a temporal sense. In References 12,14, the spherical wave‐front and the spatial nonstationarity property were evaluated, considering the nonstationarity properties on the array for M‐MIMO channel modeling. For XL‐MIMO systems, in Reference 15, linear receivers are evaluated, where the nonstationarities are included in the correlation matrix from each user.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

Taniguchi

Abrão

2020

Trans Emerging Tel Tech

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In this article, stochastic channel models for massive multiple‐input multiple‐output (M‐MIMO) and extreme large MIMO (XL‐MIMO) system applications are described, evaluated, and systematically compared. This work aims to cover new aspects of M‐MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each M‐MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition, and condition number, one can understand the influence of each characteristic on the modeling and how it differentiates from other models. Moreover, in new XL‐MIMO scenarios, where the near‐field and visibility region effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero‐forcing precoding due to the correlation effect, which have been analyzed from the geometric M‐MIMO channel models.

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Section: Channel Models For M‐mimo and Xl‐mimomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

Taniguchi

Abrão

2020

Trans Emerging Tel Tech

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“…To improve our understanding of the characteristics of UBE wireless fading, this study focused on channel fading for various types of ship motion in a UBE. There have been many studies on wireless propagation fading [6][7][8]. However, there is a significant difference between the wireless propagation in UBE and typical inland waterway scenario, especially in terms of channel characteristics.…”

Section: Introductionmentioning

confidence: 99%

Research on Wireless Fading Characteristic in Urban Bridge Environment of the Inland Waterway Based on Channel Measurement

Zhang¹,

Li²,

Shu

et al. 2019

WCMC

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This paper focuses on the fading characteristics of a wireless channel of an inland waterway in an urban bridge scenario at 5.9 GHz. The measurement area was selected in Wuhan city, which lies on the middle reaches of the Yangtze River's intersection. Due to urban bridges, the fading characteristics of inland waterway channels are highly correlated with the ship motion direction or the distance between the transmitter and receiver and thus have unique properties. We demonstrated that the path loss, K-factor, power delay profile characteristics, and delay spread features significantly varied with the distance between the transmitter and receiver. Path-loss exponents were derived from the measurements and the differences between the Urban Bridge Environment and the line-of-sight was found. In bridge environments, the values of the excess delays change weakly from line-of-sight cases. The study also showed that numerical measurement results can be used to predict small-scale characteristics over any inland waterway with relatively good accuracy. These results will serve as a reference for urban waterways with bridges, as no experimental results have been reported previously.

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Resource efficiency and pilot decontamination in XL‐MIMO double‐scattering correlated channels

Taniguchi

Souza

Guerra

et al. 2021

Trans Emerging Tel Tech

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In extra-large scale massive MIMO (XL-MIMO) systems, due to the large antenna array physical dimensions, the received signal at the base-station (BS) results in spatial non-stationarities and visibility regions (VRs), limited regions of the array which are visible to the users equipments (UEs). In crowded XL-MIMO scenarios, the reuse of pilot sequences is a path to provide massive connectivity, principally when the radio resources are scarce. However, such reuse increases the intra-cell interference, since UEs sharing the same pilot sequences may have overlapped VRs. Thus, despite the minimum mean-squared error (MMSE) combiner having a high computational complexity, its capability in mitigating the pilot contamination, and even in eliminating such effect in asymptotic antenna regime, justify its use to provide massive connectivity with high spectral efficiency (SE). In this work, we evaluate the performance of the MMSE combiner in XL-MIMO systems in terms of the SE, energy efficiency (EE), resource efficiency (RE), and capability of eliminating the intra-cell pilot contamination. The MMSE channel estimator is deployed to estimate the channel vectors. Indeed, the performance of the zero-forcing combiner and the randomized Kaczmarz regularized zero-forcing combiner is evaluated and compared with the MMSE scheme. For modeling the XL-MIMO propagation channel, we use the double-scattering channel model, which considers clusters of scatterers on both the BS-side and the UE-side to model the spatial channel correlation. In parallel, we develop the analysis considering the single-scattering channel model, a useful simplification of the double-scattering channel model.The numerical results reveal that, at the cost of a high computational complexity, the MMSE combiner achieves remarkable SE and EE results. However, such superior performance is limited by the number of antennas, being recommended for cases where the size of the VR (number of active antennas) is reduced.

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A non-stationary channel model for 5G massive MIMO systems

Cited by 12 publications

References 18 publications

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

Stochastic channel models for massive and extreme large multiple‐input multiple‐output systems

Research on Wireless Fading Characteristic in Urban Bridge Environment of the Inland Waterway Based on Channel Measurement

Resource efficiency and pilot decontamination in XL‐MIMO double‐scattering correlated channels

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