Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Han, Yu; Jin, Shi; Wen, Chao-Kai; Ma, Xiaoli

doi:10.1109/lwc.2019.2963877

Cited by 148 publications

(104 citation statements)

References 13 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, there are three ways to suppress PC in a massive MIMO system: efficient channel estimation algorithms, appropriate precoding methods, and better pilot assignment strategies. The channel estimation algorithms enhance the system performance by improving the accuracy of channel state information (CSI) estimation [10][11][12]. Some researchers use the estimated CSI to design a suitable precoding structure of downlink data.…”

Section: Introductionmentioning

confidence: 99%

Pilot Assignment Based on Graph Coloring and Location Information in Multicell Multiuser Massive MIMO Systems

Xiong

Chen

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

A massive multiple-input multiple-output (MIMO) system uses a large number of antennas in the base station (BS) to serve multiple users, which significantly improves the capacity of the system. However, in time division duplex (TDD) mode, the pilot contamination (PC) is inevitable due to the multiplexing of pilots. This paper proposed a pilot assignment based on graph coloring and location information (GC-LI) to improve the performance of users. Specifically, based on graph coloring, the proposed GC-LI algorithm combines location information like the angle of arrival (AoA), distance, and correlation to construct an interference graph. Then, we calculate the interference between any two users and use the postprocessing discrete Fourier transform (DFT) filtering process to effectively distinguish the users with nonoverlapping AoAs. Finally, according to the interference graph, the GC-LI algorithm is proposed to mitigate the intercell interference (ICI) between users with the same pilot by assigning different pilots to connected users with high ICI metrics based on some regulation. Simulation results show that the GC-LI algorithm is suitable for various types of cells. In addition, compared with the existing pilot assignment algorithms based on graph coloring, users’ average signal-to-interference-plus-noise ratio (SINR) and uplink achievable sum rate (ASR) are significantly improved.

show abstract

Section: Introductionmentioning

confidence: 99%

Pilot Assignment Based on Graph Coloring and Location Information in Multicell Multiuser Massive MIMO Systems

Xiong

Chen

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The spatial non-stationarity represents a difference of received signal between visibility regions (VRs), while the spherical wave-front considers a difference signal between the antennas elements even inside of the VR. Thus, the works [1][2][3][4][5] address the channel modeling, the channel estimation, the receiver design, and the precoding design considering the cited characteristics. These research topics are considered open problems.…”

Section: Introductionmentioning

confidence: 99%

“…Recent works on the XL-MIMO systems include. 2,4,5,9,10 In Reference 9, we provide an extensive review of channel models for massive MIMO and XL-MIMO. Therein, we discuss the main characteristics of the stationary and non-stationary channels modeled by stochastic correlation-based and geometric-based models.…”

Section: Introductionmentioning

confidence: 99%

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

Taniguchi

Souza

Guerra

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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.

show abstract

“…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%

“…For XL‐MIMO systems, in Reference 15, linear receivers are evaluated, where the nonstationarities are included in the correlation matrix from each user. In Reference 16, the near‐field propagation considering the spherical wave‐front was considered, including the spatial nonstationarity properties for XL‐MIMO systems. However, the methodology to channel modeling considered only LoS paths even with the presence of scatterers.…”

Section: Introductionmentioning

confidence: 99%

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

Taniguchi

Abrão

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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.

show abstract

Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Cited by 148 publications

References 13 publications

Pilot Assignment Based on Graph Coloring and Location Information in Multicell Multiuser Massive MIMO Systems

Pilot Assignment Based on Graph Coloring and Location Information in Multicell Multiuser Massive MIMO Systems

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

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

Contact Info

Product

Resources

About