A 2-D Geometry-Based Stochastic Channel Model for 5G Massive MIMO Communications in Real Propagation Environments
| Autor: | Guangliang Ren, Yaping He, Jiandong Li, Lihua Pang, Xijia Li, Yang Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Wavefront
021103 operations research Computer Networks and Communications business.industry Stochastic process Computer science MIMO 0211 other engineering and technologies Markov process Geometry 02 engineering and technology Computer Science Applications Antenna array symbols.namesake Control and Systems Engineering Cluster (physics) symbols Wireless Electrical and Electronic Engineering business 5G Computer Science::Information Theory Information Systems |
| Zdroj: | IEEE Systems Journal. 15:307-318 |
| ISSN: | 2373-7816 1932-8184 |
| DOI: | 10.1109/jsyst.2020.2971062 |
| Popis: | Spherical wavefront and array nonstationarity due to physically large antenna array are two typical characteristics specific to massive multiple-input–multiple-output (M-MIMO) propagation. Unfortunately, traditional MIMO channel models need to be enhanced to better characterize these two new propagation features. In light of this limitation, this article proposes an enhanced two-dimensional geometry-based stochastic channel model for 5G M-MIMO communications in real propagation environments. Specifically, a coordinate-based channel model framework is first introduced. Based on this, the impact of spherical wavefront can be mathematically described with the aid of some spatial geometric manipulations. Moreover, a general Markov process is developed to model the array nonstationarity characteristic due to cluster appearance and disappearance along the array. Meanwhile, the associated approach to derive the state transition probabilities of the proposed Markov process is detailed. Finally, capabilities of the proposed channel model in characterizing spherical wavefront and array nonstationarity of M-MIMO channels are shown by the numerical simulations. Moreover, validity of our proposal is verified by the measurement data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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