On GPU Assisted Polar Decoding : Evaluating the Parallelization of the Successive Cancellation Algorithmusing Graphics Processing Units

Abstract: In telecommunication, messages sent through a wireless medium often experience noise interfering with the signal in a way that corrupts the messages. As the demand for high throughput in the mobile network is increasing, algorithms that can detectand correct these corrupted messages quickly and accurately are of interest to the industry. Polar codes have been chosen by the Third Generation Partnership Project as the error correction code for 5G New Radio control channels. This thesis work aimed to investigate whether the polar code Successive Cancellation (SC) could be parallelized and if a graphics processing unit (GPU) can be utilized to optimize the execution time of the algorithm. The polar code Successive Cancellation was enhanced by implementing tree pruning and support for GPUs to leverage their parallelization. The difference in execution time between the concurrent and sequential versions of the SC algorithm with and without tree pruning was evaluated. The tree pruning SC algorithm almost always offered shorter execution times than the SC algorithm that did not employ treepruning. However, the support for GPUs did not reduce the execution time in these tests. Thus, the GPU is not certain to be able to improve this type of enhanced SC algorithm based on these results.