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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 237061, 8 pages
Research Article

A Fading Channel Simulator Implementation Based on GPU Computing Techniques

1Universidad de Guadalajara, Boulevard Marcelino García Barragán 1421, 44430 Guadalajara, JAL, Mexico
2Universidad de Quintana Roo, Boulevard Bahía s/n, Esquina Ignacio Comonfort, 77019 Chetumal, QRoo, Mexico
3Universidad Autónoma de Yucatán, Avenida Industrias No Contaminantes, S/N, 97310 Mérida, YUC, Mexico

Received 13 September 2014; Revised 11 March 2015; Accepted 11 March 2015

Academic Editor: Chunlin Chen

Copyright © 2015 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.


Channel simulators are powerful tools that permit performance tests of the individual parts of a wireless communication system. This is relevant when new communication algorithms are tested, because it allows us to determine if they fulfill the communications standard requirements. One of these tests consists of evaluating the system performance when a communication channel is considered. In this sense, it is possible to model the channel as an FIR filter with time-varying random coefficients. If the number of coefficients is increased, then a better approach to real scenarios can be achieved; however, in that case, the computational complexity is increased. In order to address this issue, a design methodology for computing the time-varying coefficients of the fading channel simulators using consumer-designed graphic processing units (GPUs) is proposed. With the use of GPUs and the proposed methodology, it is possible for nonspecialized users in parallel computing to accelerate their simulation developments when compared to conventional software. Implementation results show that the proposed approach allows the easy generation of communication channels while reducing the processing time. Finally, GPU-based implementation takes precedence when compared with the CPU-based implementation, due to the scattered nature of the channel.