A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond

Bian, Jin; Wang, Cheng-Xiang; Gao, Xiqi; You, Xiaohu; Zhang, Minggao

doi:10.1109/twc.2020.3047973

Cited by 123 publications

(73 citation statements)

References 45 publications

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“…A modified non-stationary shallow water GBSM. [18]. When b + s = 1 is satisfied, the MB propagation is reduced to SB propagation.…”

Section: A Modified Non-stationary Gbsm For Shallow Water Uwa Communi...mentioning

confidence: 99%

A 2D Non-Stationary Channel Model for Underwater Acoustic Communication Systems

Zhu¹,

Wang²,

Ma³

2021

Preprint

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Underwater acoustic (UWA) communication plays a key role in the process of exploring and studying the ocean. In this paper, a modified non-stationary wideband channel model for UWA communication in shallow water scenarios is proposed. In this geometry-based stochastic model (GBSM), multiple motion effects, time-varying angles, distances, clusters' locations with the channel geometry, and the ultra-wideband property are considered, which makes the proposed model more realistic and capable of supporting long time/distance simulations. Some key statistical properties are investigated, including temporal autocorrelation function (ACF), power delay profile (PDP), average delay, and root mean square (RMS) delay spread. The impacts of multiple motion factors on temporal ACFs are analyzed. Simulation results show that the proposed model can mimic the non-stationarity of UWA channels. Finally, the proposed model is validated with measurement data.

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“…A modified non-stationary shallow water GBSM. [18]. When b + s = 1 is satisfied, the MB propagation is reduced to SB propagation.…”

Section: A Modified Non-stationary Gbsm For Shallow Water Uwa Communi...mentioning

confidence: 99%

A 2D Non-Stationary Channel Model for Underwater Acoustic Communication Systems

Zhu¹,

Wang²,

Ma³

2021

Preprint

Self Cite

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“…e fifth-generation (5G) wireless communication systems will encounter many emerging applications such as ultrahigh definition video, connected healthcare, and smart cities [1,2]. Stimulated by these applications for massive connectivity, 5G wireless communication systems are expected to meet high capacity and high-data-rate transmission demands in different deployment scenarios [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Measurements and Characterization for Millimeter-Wave Massive MIMO Channel in High-Speed Railway Station Environment at 28 GHz

Liu

Lin

et al. 2021

International Journal of Antennas and Propagation

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The millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measured datasets extracted from the channel measurements can provide insight into the characteristics of radio channels in different scenarios. Therefore, mmWave massive MIMO channel measurements, simulation, and modeling are carried out in the high-speed railway waiting hall environments at 28 GHz. The multipath components (MPCs) parameters are estimated for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios based on the space-alternating generalized expectation-maximization (SAGE) algorithm. Delay spread (DS), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) are analyzed. And they are processed by using the K-mean algorithm. In addition, propagation characteristics are simulated based on the improved ray tracing method of shooting and bouncing ray tracing/image (SBR/IM). The correctness of the improved ray tracing method is verified by comparing the measured results with the simulated results. The large-scale path loss (PL) is characterized based on close-in (CI) free-space reference distance model and the floating-intercept (FI) path loss model. Furthermore, statistical distributions for root-mean-square delay spread (RMS DS) are investigated. The Gaussian distribution best fits the measured data of RMS delay spread. Finally, multipath clustering is identified using the multipath component distance (MCD). The analysis of these results from mmWave massive MIMO channel measurements and simulation may be instructive for the deployment of the 5G or B5G wireless communications systems at 28 GHz.

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“…One of ITS' critical functions is to predict the future condition of traffic 4 and support various vehicle‐to‐everything (V2X) applications that improve road safety. Gradually, as a key branch of the IoT, the Internet of vehicles (IoV) has become an indispensable part of modern transportation and plays an important role in some emerging scenarios, such as autonomous driving and natural language processing 5 . Along with the diversifications of the vehicles applications such as compute‐intensive tasks and delay‐sensitive tasks, how to satisfy the quality of service (QoS) requirements of the vehicles' tasks has become a challenge in the vehicular networks 6–8 …”

Section: Introductionmentioning

confidence: 99%

Stackelberg game‐based task offloading in vehicular edge computing networks

Liu

Tian

Deng

et al. 2021

Int J Communication

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Summary With the emergence of intelligent vehicles, how to satisfy the demands of the vehicles with computing‐intensive and delay‐sensitive tasks has become a challenging issue. Vehicular edge computing (VEC) is proposed as an advanced paradigm to improve the service of vehicles through offloading the task to the VEC servers. Nevertheless, VEC servers always have limited computation resources and do not satisfy the offloading requirements of vehicles. To this end, in this paper, we propose a more flexible offloading scheme by jointly considering the offloading strategies and the price strategy. In the proposed scheme, where the task can be dynamically divided into two parts parallel executed at the vehicles and VEC servers. A multi‐leader and multi‐follower Stackelberg game ‐based distributed algorithm is proposed to maximize the utilities of the vehicles and the VEC servers under the delay constraint. Finally, the game equilibrium is analyzed and achieved. Extensive experiments demonstrate that the proposed offloading scheme converges fast and always outperforms the existing schemes in terms of the vehicular utility under different network conditions. For instance, the proposed scheme achieves the utility improvement over 56.62% compared to the fixed selection strategy and achieves the utility improvement up to 161.0% compared to the complete offloading with fixed price strategy when the number of vehicles is 10. Additionally, the effects of key parameters such as the offloading strategies and, the price strategy, and the computation resource on the average utility of vehicles are also discussed and analyzed based on the simulation results.

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A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond

Cited by 123 publications

References 45 publications

A 2D Non-Stationary Channel Model for Underwater Acoustic Communication Systems

A 2D Non-Stationary Channel Model for Underwater Acoustic Communication Systems

Measurements and Characterization for Millimeter-Wave Massive MIMO Channel in High-Speed Railway Station Environment at 28 GHz

Stackelberg game‐based task offloading in vehicular edge computing networks

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