Signals for turbo options, Think you have a blown turbo? – Here’s what to look for


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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.

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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.

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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.

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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.