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In this paper a exible, high-throughput, low-complexity additive white gaussian noise (AWGN) channel generator is presented. The proposed generator employs a Mersenne-Twister to generate a long random number uniformly distributed sequence and a Box-Muller transformation implementation to derive gaussian noise samples. Emphasis is given on developing a high-throughput approximation unit for the elementary functions required for the transformation. The proposed techniques are shown to lead todoi:10.1109/icassp.2011.5946821 dblp:conf/icassp/ParaskevakosP11 fatcat:qinq4np5qvcm7ezaf4x5ui2gt4