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Wireless Communications and Mobile Computing
Volume 2018, Article ID 3517489, 9 pages
Research Article

A Triply Selective MIMO Channel Simulator Using GPUs

1Department of Electronic Engineering, UDG-CUCEI, 44430 Guadalajara, JAL, Mexico
2Department of Engineering, Universidad de Quintana Roo, 77019 Chetumal, QROO, Mexico
3Department of Electronics, Systems, and IT, ITESO, 45604 Tlaquepaque, JAL, Mexico
4Department of Mechatronics, Universidad Autónoma de Yucatán, 97000 Mérida, YUC, Mexico

Correspondence should be addressed to R. Carrasco-Alvarez; xm.gdu.socimedaca@ocsarrac.r

Received 29 September 2017; Revised 23 January 2018; Accepted 6 February 2018; Published 5 March 2018

Academic Editor: Neji Youssef

Copyright © 2018 R. Carrasco-Alvarez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A methodology for implementing a triply selective multiple-input multiple-output (MIMO) simulator based on graphics processing units (GPUs) is presented. The resulting simulator is based on the implementation of multiple double-selective single-input single-output (SISO) channel generators, where the multiple inputs and the multiple received signals have been transformed in order to supply the corresponding space correlation of the channel under consideration. A direct consequence of this approach is the flexibility provided, which allows different propagation statistics to each SISO channel to be specified and thus more complex environments to be replicated. It is shown that under some specific constraints, the statistics of the triply selective MIMO simulator are the same as those reported in the state of art. Simulation results show the computational time improvement achieved, up to 650-fold for an 8 8 MIMO channel simulator when compared with sequential implementations. In addition to the computational improvement, the proposed simulator offers flexibility for testing a variety of scenarios in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems.