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A High-Throughput Subspace Pursuit Processor for ECG Recovery in Compressed Sensing Using Square-Root-Free MGS QR Decomposition

机译:使用无平方根MGS QR分解的压缩感知中ECG恢复的高通量子空间追踪处理器

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Sensor nodes in wireless body-area networks are expected to use compressed sensing (CS) for ultralow-power consumption. For signal recovery in CS systems, compared with the software implementation, the hardware processor has superiority on power, real-time ability, and so on. Although orthogonal matching pursuit (OMP) is conventionally selected in hardware implementations, its low recovery performance is still a problem. We propose to use the subspace pursuit (SP) algorithm that can achieve higher recovery performance with less iteration to implement the hardware processor. First, a new SP by a square-root-free method and a computation reuse scheme are proposed to reduce the SP's complexity. Then, the fully paralleled architectures and elaborate pipelines are proposed to improve the processing throughput. Finally, we give an FPGA implementation prototype. Simulations compared with OMP show that the traditional SP can improve more than 8 dB on recovery performance or it can reduce more than 31% sampling paths in sensor nodes. These benefits are kept by our proposed SP because our improvement does not modify the algorithmic nature. Nevertheless, the significant complexity reduction is achieved by our proposal. The proposed SP processor can recover ECG signals with 28.2 dB and achieve the processing throughput of 44.4 K vectors/s.
机译:无线体域网络中的传感器节点有望使用压缩感测(CS)来实现超低功耗。对于CS系统中的信号恢复,与软件实现相比,硬件处理器在功耗,实时性等方面具有优势。尽管通常在硬件实现中选择正交匹配追踪(OMP),但其低恢复性能仍然是一个问题。我们建议使用子空间追踪(SP)算法,以较少的迭代获得更高的恢复性能,以实现硬件处理器。首先,提出了一种新的无平方根方法的SP和计算重用方案,以降低SP的复杂性。然后,提出了完全并行的体系结构和复杂的流水线以提高处理吞吐量。最后,我们给出一个FPGA实现原型。与OMP进行的仿真表明,传统的SP可以将恢复性能提高8 dB以上,或者可以减少31%的传感器节点采样路径。这些优点由我们提出的SP保留,因为我们的改进不会改变算法的性质。但是,通过我们的建议,可以显着降低复杂性。提出的SP处理器可以恢复28.2 dB的ECG信号,并实现44.4 K vectors / s的处理吞吐量。

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