Design and simulation of 5G massive MIMO kernel algorithm on SIMD vector processor

Sivakrishna, Sadineni; Yarrabothu, Ravi Sekhar

doi:10.1109/spaces.2018.8316315

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…The frequency bands above 6 GHz promise large bandwidths and are thus desirable for application in fifth generation (5G) networks and beyond [1]. For optimum wireless communication systems' operation, massive multiple-input-multiple-output (MIMO) techniques have been introduced with capabilities to exploit both delay and angular domains to maximize capacity [2]. A successful design and deployment of indoor environment systems such as Femto Access Points, relies on an in-depth knowledge of radio channel propagation in terms of spatiotemporal channel fading [3].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Statistical Channel Model for Indoor Corridor at 14 GHz, 18 GHz, and 22 GHz Bands

Oyie

Afullo

2018

Wireless Communications and Mobile Computing

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Several techniques have been proposed to overcome challenges of meeting demands for higher data rates in wireless communication. Space-time diversity method is proposed to exploit spatiotemporal nature of the channel; hence, a comprehensive knowledge of the spatiotemporal properties of a channel is required. In this paper, a measurement-based channel model that considers both delay and angular domains of an indoor corridor channel for 14 GHz, 18 GHz, and 22 GHz is proposed. A nonparametric Gaussian kernel density estimation method is applied for cluster identification for the three frequency bands. This work proposes a spatiotemporal model that conditions the model parameters on the azimuthal spatial domain. The clusters are modeled on the complete azimuth plane and a Gaussian estimation distribution is fitted onto the empirical data plot. Both clusters and multipath components are modeled and results are compared with Saleh-Valenzuela model parameter values. The results show that both clusters and multipath components can be estimated by probability density functions that follow Gaussian and Laplacian fits on the spatial domain for indoor corridor environment, respectively.

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Section: Introductionmentioning

confidence: 99%