Classification and Comparison of Massive MIMO Propagation Channel Models

Feng, Ruili; Wang, Cheng-Xiang; Huang, Jie; Gao, Xiqi; Salous, Sana; Haas, Harald

doi:10.1109/jiot.2022.3198690

Cited by 17 publications

(6 citation statements)

References 136 publications

(127 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its importance for various applications, the MIMO channel has been extensively studied and various MIMO channel models have been proposed, most of which are based on measurements. There are many ways to classify channel models [ 33 , 34 , 35 , 36 ], and a useful classification is shown in Figure 1 which includes physical models, analytical models, and standard models.…”

Section: Channel Modelsmentioning

confidence: 99%

“…Due to its importance for various applications, the MIMO channel has been extensively studied and various MIMO channel models have been proposed, most of which are based on measurements. There are many ways to classify channel models [33][34][35][36], and a useful classification is shown in Figure 1 which includes physical models, analytical models, and standard models. The physical channel model characterizes the multipath bidirectional wireless environment where the electromagnetic wave propagates between the transmitter and receivers.…”

Section: Channel Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback

Al-Asadi,

Al-Saedi,

Alwane

et al. 2023

Sensors

View full text Add to dashboard Cite

Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

show abstract

Section: Channel Modelsmentioning

confidence: 99%

“…Due to its importance for various applications, the MIMO channel has been extensively studied and various MIMO channel models have been proposed, most of which are based on measurements. There are many ways to classify channel models [33][34][35][36], and a useful classification is shown in Figure 1 which includes physical models, analytical models, and standard models. The physical channel model characterizes the multipath bidirectional wireless environment where the electromagnetic wave propagates between the transmitter and receivers.…”

Section: Channel Modelsmentioning

confidence: 99%

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback

Al-Asadi,

Al-Saedi,

Alwane

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the literature, stochastic channel models for massive MIMO communications can be classified as geometry-based stochastic models (GBSMs), correlation-based stochastic models (CBSMs) [15], [16], and beam domain channel models (BDCMs) [17]. In [18], a twin-cluster GBSM was proposed, which considered spherical wavefront and spatial nonstationarity.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ultra-Massive MIMO BDCM for 6G Wireless Communication Systems

Zheng,

Wang,

Huang

et al. 2024

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

In this paper, a novel beam domain channel model (BDCM) for sixth generation (6G) ultra-massive multiple-input multiple-output (MIMO) wireless communication systems is proposed by transforming from an existing geometry-based stochastic model (GBSM). Both the GBSM and BDCM consider the special channel characteristics of ultra-massive MIMO, including spherical wavefront and spatial non-stationarity properties. Steering vectors of the spherical wavefront are derived by higherorder Taylor expansion. By sampling the angle and distance rings, steering matrices of the spherical wavefront can be transformed to unitary matrices. This helps to achieve a perfect transformation from the GBSM to the BDCM in the near-field condition. Meanwhile, it opens up a view of beam domain for channel characteristic analysis to reduce the model complexity. Common statistical properties of the GBSM and BDCM in the near-field and simplified far-field conditions are studied, including spatial cross-correlation functions (SCCFs), temporal autocorrelation functions (TACFs), frequency correlation functions (FCFs), etc. Specific statistical properties, such as the root mean square (RMS) angular spread of the GBSM and RMS beam spread of the BDCM, are studied. Channel capacities of the GBSM and BDCM are investigated and compared with measurement data. It turns out that simulated capacities considering nearfield conditions show good agreements with measured capacities. On the other hand, simulated capacities considering far field conditions show a large discrepancy with measured capacities. This indicates that near-field effects need to be included in the channel modeling and performance evaluation of 6G ultramassive MIMO communication systems.

show abstract

“…UWB signals are widely used in low-power short-range wireless communication [ 8 ] due to their large bandwidth, low power consumption, and strong ability to distinguish multipath signals. The Saleh-Valenzuela (S-V) model [ 9 , 10 , 11 ] has been adopted as a UWB system channel model for small-scale fading. In the S-V model, the multipath components are modeled as several rays arriving within different clusters.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Joint Angle of Arrival and Time of Arrival Estimation of Multipath Signal in UWB System

Deng

Tang

et al. 2023

Sensors

View full text Add to dashboard Cite

In an ultra-wideband (UWB) system, the two-dimensional (2D) multiple signal classification (MUSIC) algorithms based on high-precision 2D spectral peak search can jointly estimate the time of arrival (TOA) and angle of arrival (AOA). However, the computational complexity of 2D-MUSIC is very high, and the corresponding data model is only based on the dual antennas. To solve these problems, a low-complexity algorithm for joint AOA and TOA estimation of the multipath ultra-wideband signal is proposed. Firstly, the dual antenna sensing data model is extended to the antenna array case. Then, based on the array-sensing data model, the proposed algorithm transforms the 2D spectral peak search of 2D-MUSIC into a secondary optimization problem to extract the estimation of AOA via only 1D search. Finally, the acquired AOA estimations are brought back, and the TOA estimations are also obtained through a 1D search. Moreover, in the case of an unknown transmitted signal waveform, the proposed method can still distinguish the main path signal based on the time difference of arrival of different paths, which shows wider applications. The simulation results show that the proposed algorithm outperforms the Root-MUSIC algorithm and the estimation of signal parameters using the rotational invariance techniques (ESPRIT) algorithm, and keeps the same estimation accuracy but with greatly reduced computational complexity compared to the 2D-MUSIC algorithm.

show abstract

Classification and Comparison of Massive MIMO Propagation Channel Models

Cited by 17 publications

References 136 publications

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback

A Novel Ultra-Massive MIMO BDCM for 6G Wireless Communication Systems

Low-Complexity Joint Angle of Arrival and Time of Arrival Estimation of Multipath Signal in UWB System

Contact Info

Product

Resources

About