This paper presents a novel implementation of graphics
processing unit (GPU) based symbol timing recovery using
polyphase interpolators to detect symbol timing error. Symbol
timing recovery is a compute intensive procedure that detects
and corrects the timing error in a coherent receiver. We
provide optimal sample-time timing recovery using a maximum
likelihood (ML) estimator to minimize the timing error.
This is an iterative and adaptive system that relies on
feedback, therefore, we present an accelerated implementation
design by using a GPU for timing error detection (TED),
enabling fast error detection by exploiting the 2D filter structure
found in the polyphase interpolator. We present this hybrid/
heterogeneous CPU and GPU architecture by computing
a low complexity and low noise matched filter (MF) while
simultaneously performing TED. We then compare the performance
of the CPU vs. GPU based timing recovery for different
interpolation rates to minimize the error and improve
the detection by up to a factor of 35. We further improve the
process by utilizing GPU optimization and performing block
processing to improve the throughput even more, all while
maintaining the lowest possible sampling rate.Laboratory for Telecommunications SciencesNational Science Foundation (NSF