Clustering-FFT Based Doppler-Shift Acquisition for Space Communications

Abstract

For space communications, the transmitted signal generally faces a long-distance transmission and a high-accelerating movement. The long-distance transmission causes a very low signal-to-noise ratio (SNR) and the high-accelerating movement causes a dynamic Doppler-shift. Under a very large acceleration, the long-time accumulation which aims to solve the very low SNR, brings about the serious energy dispersion problem, thus posing a great challenge for Doppler-shift acquisition. Introducing the clustering idea in machine learning, we propose one clustering-fast-Fourier-transform (CFFT) based Doppler-shift acquisition scheme with an affordable computational complexity, to address the energy dispersion problem for space communications. The proposed CFFT scheme includes two algorithms called the Generic CFFT algorithm and the High-order CFFT algorithm, respectively. First, the Generic CFFT algorithm clusters multiple density-reachable signal elements into one signal cluster, thus accumulating the dispersed energy again. Second, the High-order CFFT algorithm, which conducts several rounds of clustering, clusters more density-reachable signal elements into one larger signal cluster. Simulations results show that our proposed CFFT scheme achieves a higher acquisition probability than the existing FFT based schemes and consumes a lower computational complexity than the FRFT scheme.