|Author:||P. Andelfinger, J. Mittag, H. Hartenstein||links:||IEEE Xplore|
|Source:||Proceedings of the 19th Annual Meeting of the IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Singapore, July 2011|
In recent years, a trend towards the usage of physical layer models with increased accuracy can be observed within the wireless network community. This trend has several reasons. The consideration of signals - instead of packets - as the smallest unit of a wireless network simulation enables the ability to reflect complex radio propagation characteristics properly, and to study novel PHY/MAC/NET cross-layer optimizations that were not directly possible before, e.g. cognitive radio networks and interference cancelation. Yet, there is a price to pay for the increase of accuracy, namely a significant decrease of runtime performance due to computationally expensive signal processing. In this paper we study whether this price can be reduced - or even eliminated - if GPU-based signal processing is employed. In particular, we present and discuss four different architectures that can be used to exploit GPU-based signal processing in discrete event-based simulations. Our evaluation shows that the runtime costs can not be cut down completely, but significant speedups can be expected compared to a non GPU-based solution.