Baseband Channel Simulation and Communication Receiver Implementation Using GPU Acceleration

The Graphics Processing Unit (GPU) has been evolving for various generations, from a fixed-funcion graphics pipeline to a programmable parallel processor. Due to the fact that GPU has a high memory bandwidth and a high Giga FLoating points Operations Per Second (GFLOPS) performance, it is used to accelerate the computations of non-graphics data, which is also referred as General Purpose Computing on Graphics Processing Units (GPGPU). However, not all algorithms benefit equally from this technique. The GPU computing is only suitable for algorithms with repeating computation and low data dependency, which means with a high degree of parallel computing characteristics. There are mainly two groups of communication applications analyzed and implemented in this thesis, which are all implemented and simulated using general personal computer under the NVIDIA CUDA environment. First, the method and result of the baseband channel simulation implementation using parallel computing concept is presented in the thesis. Then the parallel computing concepts are being used to implement the communication receiver. Both of the speedup performance and the GPU programming experience will be covered in this thesis.