Skip to Main Content Efficient Hardware for Generalized Turbo Signal Recovery in Compressed Sensing Abstract: Compressed sensing CS is becoming a hot topic in recent years for its advantages such as low-power consumption, low memory requirement, and low sampling frequency. However, high-dimensional nonlinear signals will inevitably introduce notable complexity and low efficiency in signal recovery.
Generalized turbo signal recovery G-Turbo-SR is a cutting-edge method, which efficiently reduces complexity with a partial discrete Fourier transform DFT sensing matrix.
However, in practical applications, G-Turbo-SR still suffers from high complexity for probability computations and matrix signals for turbo options. This article optimizes the algorithm of G-Turbo-SR in scheduling to reduce the matrix multiplications by half.
High-precision numerical approximation method is proposed to replace the complex integral calculation, which efficiently reduces the hardware cost with acceptable performance degradation.
Based on the data-flow graph DFG analysis, detailed hardware architecture is proposed with module designs. Proper quantization scheme is selected according to the mean square error MSE performance.
Pipelining, folding, and variable precision quantization VPQ scheme are employed for higher hardware efficiency. FPGA implementation on Xilinx 7ktffg shows a higher throughput and hardware efficiency compared to existing recovery methods for CS.