Massive MIMO channel modeling using map-based ray tracing method

Yao, Junliang; Ren, Hang; Liu, Qing

doi:10.1109/mape.2017.8250781

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The computational complexity is particularly challenging for massive MIMO systems because it increases linearly with the number of array elements. At present, the map-based model is proposed to implement the RT method, which can effectively reduce the computational complexity by simplifying the geometry and electromagnetic description of the environment and restricting the sequence of interactions between the rays and objects [177]. The model may have poor accuracy in predicting the channel at specific locations, but it is fully capable of simulating the non-stationarity and transition of large arrays.…”

Section: Massive Mimo Channel Modelingmentioning

confidence: 99%

Trends in Hospitalization and In-Hospital Mortality From VTE, 2007 to 2016, in China

Zhu¹,

Lei²,

Shao³

et al. 2019

Chest

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Section: Massive Mimo Channel Modelingmentioning

confidence: 99%

Trends in Hospitalization and In-Hospital Mortality From VTE, 2007 to 2016, in China

Zhu¹,

Lei²,

Shao³

et al. 2019

Chest

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“…In the literature, we can find different research works that carry out, by means of RT, a characterization of the C-mMIMO [32][33][34][35] and D-mMIMO [36] channel. Comparative studies of both channels focus, in general, on urban environments [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Concentrated and Distributed Massive MIMO Channels at 26 GHz in a Large Indoor Environment Using Ray-Tracing

et al. 2022

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In this paper, a comparative analysis between concentrated and distributed massive multiple-input multiple-output channels (C-mMIMO and D-mMIMO respectively), in an indoor environment using ray-tracing (RT) in the 26 GHz band is presented. The comparison is carried out in a realistic scenario consisting of a floor of a large building. The simulations emulated the up-link channel in an indoor cell in the framework of a time division duplex (TDD) -orthogonal frequency division multiplexing (TDD-OFDM) system. Both base stations, concentrated and distributed, were equipped with an array consisting of 100 antennas, and the maximum number of 20 simultaneously active users is considered. The channels are simulated using a well-tested and rigorous RT software. Using RT channel modeling, this work characterizes the up-link channels with both technologies, estimating the coherence bandwidth of the channels and analyzing the achievable capacity, assuming perfect channel state information (CSI). The results show that the D-mMIMO channel outperforms the C-mMIMO one from the point of view of their behavior in broadband as well as in terms of the obtainable capacity.INDEX TERMS 5G mobile systems, channel capacity, coherence bandwidth, massive MIMO.

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“…In order to design an applicative communication system in tunnel environment and evaluate the system performance, an accurate channel model and a detailed knowledge of its statistical properties are indispensable. There are already many useful models adapted in different environments, such as ray-tracing models [5][6], geometry-based deterministic models (GBDMs) [7], correlation-based stochastic models (CBSMs) [8][9] and geometry-based stochastic models (GBSMs) [10][11]. Because of the research convenience for the statistical characteristics and capacity, GBSMs are most widely used.…”

Section: Introductionmentioning

confidence: 99%

A Neoteric Three-Dimensional Geometry-Based Stochastic Model for Massive MIMO Fading Channels in Subway Tunnels

2019

KSII TIIS

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Wireless mobile communication systems in subway tunnels have been widely researched these years, due to increased demand for the communication applications. As a result, an accurate model is essential to effectively evaluate the communication system performance. Thus, a neoteric three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for the massive multiple-input multiple-output (MIMO) fading channels in tunnel environment. Furthermore, the statistical properties of the channel such as space-time correlation, amplitude and phase probability density are analyzed and compared with those of the traditional two-dimensional (2D) model by numerical simulations. Finally, the ergodic capacity is investigated based on the proposed model. Numerical results show that the proposed model can describe the channel in tunnels more practically.

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Massive MIMO channel modeling using map-based ray tracing method

Cited by 5 publications

References 14 publications

Trends in Hospitalization and In-Hospital Mortality From VTE, 2007 to 2016, in China

Trends in Hospitalization and In-Hospital Mortality From VTE, 2007 to 2016, in China

A Comparison Between Concentrated and Distributed Massive MIMO Channels at 26 GHz in a Large Indoor Environment Using Ray-Tracing

A Neoteric Three-Dimensional Geometry-Based Stochastic Model for Massive MIMO Fading Channels in Subway Tunnels

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