953 research outputs found

    A 3D Wideband Geometry-Based Stochastic Model for UAV Air-to-Ground Channels

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    Channel Modeling and Characteristics for 6G Wireless Communications

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    [EN] Channel models are vital for theoretical analysis, performance evaluation, and system deployment of the communication systems between the transmitter and receivers. For sixth-generation (6G) wireless networks, channel modeling and characteristics analysis should combine different technologies and disciplines, such as high-mobil-ity, multiple mobilities, the uncertainty of motion trajectory, and the non-stationary nature of time/frequency/space domains. In this article, we begin with an overview of the salient characteristics in the modeling of 6G wireless channels. Then, we discuss the advancement of channel modeling and characteristics analysis for next-generation communication systems. Finally, we outline the research challenges of channel models and characteristics in 6G wireless communications.This research was supported by the National Key R&D Program of China under grant 2018YFB1801101; the National Nature Science Foundation of China (No. 61771248 and 61971167); the Jiangsu Province Research Scheme of Nature Science for Higher Education Institution (No. 14KJA510001); and the Open Research Fund of the National Mobile Communications Research Laboratory, Southeast University (No. 2020D14).Jiang, H.; Mukherjee, M.; Zhou, J.; Lloret, J. (2021). Channel Modeling and Characteristics for 6G Wireless Communications. IEEE Network. 35(1):296-303. https://doi.org/10.1109/MNET.011.200034829630335

    Multi-Elliptical Geometry of Scatterers in Modeling Propagation Effect at Receiver

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    In the proposed chapter, the authors present a geometric-statistical propagation model that defines three groups of received signal components, i.e., direct path, delayed scattering, and local scattering components. The multi-elliptical propagation model, which represents the geometry of scatterer locations, is the basis for determining the delayed components. For the generation of the local components, a statistical distribution is used. The basis for this model is a power angular spectrum (PAS) of the received signal, which is closely related to a type of propagation environment and transmitter-receiver spatial positions. Therefore, we have an opportunity to evaluate the influence of the environment type and an object motion direction on the basic characteristics such as envelope distribution, PAS, autocorrelation function, and spectral power density. The multi-elliptical model considers the propagation phenomena occurring in the azimuth plane. In the chapter, we will also show the 3D extension of modeling effects of propagation phenomena

    Channel Modeling in small cell and millimeter-wave scenarios

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    One common feature of the research works on future wireless communication technologies is the pursuit of high spectral efficiency while multiple mobile stations access the network. The small cell and the millimter-wave are two key enabling technologies to tackle these challenges. To thoroughly investigate small cell and millimeter-wave, it is essential to have a good understanding of radio-propagation characteristics of transmission path between a base station and an mobile station which are small cell channel model and millimeter-wave channel model

    Classification and comparison of massive MIMO propagation channel models

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    Considering great benefits brought by massive multiple-input multiple-output (MIMO) technologies in Internet of things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical vs. analytical method and deterministic vs. stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical non-predictive channel models, which further clarify and combine the deterministic vs. stochastic and physical vs. analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical non-predictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given

    A Mixed-Bouncing Based Non-Stationarity and Consistency 6G V2V Channel Model with Continuously Arbitrary Trajectory

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    In this paper, a novel three-dimensional (3D) irregularshaped geometry-based stochastic model (IS-GBSM) is proposedfor sixth-generation (6G) millimeter wave (mmWave) massivemultiple-input multiple-output (MIMO) vehicle-to-vehicle(V2V) channels. To investigate the impact of vehicular trafficdensity (VTD) on channel statistics, clusters are divided into staticclusters and dynamic clusters, which are further distinguishedinto static/dynamic single/twin-clusters to capture the mixed bouncingpropagation. A new method, which integrates thevisibility region and birth-death process methods, is developedto model space-time-frequency (S-T-F) non-stationarity of V2Vchannels with time-space (T-S) consistency. The continuouslyarbitrary vehicular movement trajectory (VMT) and soft clusterpower handover are modeled to further ensure channel T-Sconsistency. From the proposed model, key channel statistics arederived. Simulation results show that S-T-F non-stationarity ofchannels with T-S consistency is modeled and the impacts of VTDand VMT on channel statistics are analyzed. The generality ofthe proposed model is validated by comparing simulation resultsand measurement/ray-tracing (RT)-based results
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