IEEE Access · 2017 · 76 citations · 49 references
Wireless CommunicationsEngineeringSmart AntennaChannel CharacterizationChannel ModelingNlos EnvironmentsMimo SystemSystems EngineeringMassive MimoWireless SystemsOutdoor LosMultiuser MimoAntennaSignal ProcessingMultipath ComponentsMultipath ClustersChannel ModelChannel EstimationChannel Sounding
The study introduces a measurement campaign to characterize massive MIMO channels in outdoor line‑of‑sight and non‑line‑of‑sight environments and to investigate channel variations across a large receiver array. Measurements were performed at 15 GHz with 4 GHz bandwidth using a virtual 40 × 40 planar receiver array formed by stepping a vertically‑polarized bi‑conical omni‑directional antenna, while a single‑antenna transmitter was used; the 1600‑element array was partitioned into 7 × 7 sub‑arrays and a maximum‑likelihood space‑alternating generalized EM algorithm extracted multipath components, enabling analysis of K‑factor variability, channel spreads, and identification of spatial‑stationary clusters. From hundreds of spatial‑stationary clusters, the authors derived a stochastic model describing their horizontal and vertical life distances, two‑dimensional life regions, and spread variations, which is crucial for massive MIMO channel modeling with two‑dimensional large arrays.
In this paper, a measurement campaign for massive multiple-input multiple-output (MIMO) channel characterization in both line-of-sight (LoS) and non-LoS outdoor environments is introduced. The measurements are conducted at the center frequency of 15 GHz with a bandwidth of 4 GHz. A virtual 40 × 40 planar antenna array formed by stepping a vertically-polarized bi-conical omni-directional antenna (ODA) along regularly-spaced grids is used in the receiver (Rx). The transmitter is equipped with a single ODA. To investigate channel variations over the Rx array, this 1600-element Rx array is split into multiple 7 × 7 sub-arrays, and a maximum-likelihood parameter estimation algorithm implemented using the space-alternating generalized expectation-maximization principle is applied to extracting multipath components (MPCs) from sub-array outputs. The spatial variability of K-factor, composite channel spreads in delay, azimuth, and elevation of arrival are investigated. Based on the estimated MPCs' parameters, multipath clusters are identified and associated across the array to find the so-called spatial-stationary (SS) clusters. From several hundreds of SS-clusters extracted, we establish a stochastic model for their life distances in horizontal and vertical directions, two-dimensional (2-D) life region, and variations of cluster spreads. These findings are important for massive MIMO channel modeling in the cases, where 2-D largescale arrays are considered.
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Jeffrey G. Andrews, Stefano Buzzi, Wan Choi et al. · IEEE Journal on Selected Areas in Communications · 2014 · 8.1K citations
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