GPGPU Based Parallel Implementation of Spectral Correlation Density Function

In this study, the parallelization of a critical statistical feature of communication signals called the spectral correlation density (SCD) is investigated. The SCD is used for synchronization in OFDM-based systems such as LTE and Wi-Fi, but is also proposed for use in next-generation wireless systems where accurate signal classification is needed even under poor channel conditions. By leveraging cyclostationary theory and classification results, a method for reducing the computational complexity of estimating the SCD for classification purposes by 75% or more using the Quarter SCD (QSCD) is proposed. We parallelize the SCD and QSCD implementations by targeting general purpose graphics processing unit (GPU) through architecture specific optimization strategies. We present experimental evaluations on identifying the parallelization configuration for maximizing the efficiency of the program architecture in utilizing the threading power of the GPU architecture. We show that algorithmic and architecture specific optimization strategies result with improving the throughput of the state of the art GPU based SCD implementation from 120 signals/second to 3300 signals/second.