Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resource-limited devices, e.g., wireless sensors. Since the information signals are often continuous-valued, digital communication of compressive measurements requires quantization. In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements. We propose a deep encoder-decoder architecture, consisting of an encoder deep neural network (DNN), a quantizer, and a decoder DNN, that realizes low-complexity vector quantization aiming at minimizing the mean-square error of the signal reconstruction for a given quantization rate. We devise a supervised learning method using stochastic gradient descent and backpropagation to train the system blocks. Strategies to overcome the vanishing gradient problem are proposed. Simulation results show that the proposed non-iterative DNN-based QCS method achieves higher rate-distortion performance with lower algorithm complexity as compared to standard QCS methods, conducive to delay-sensitive applications with large-scale signals.

中文翻译：

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通过深度神经网络进行低复杂度矢量量化压缩感知

可以通过压缩感知（CS）来获取在许多无线和信号采集应用中遇到的稀疏信号，以减少计算和传输，这对于资源受限的设备（例如，无线传感器）至关重要。由于信息信号通常是连续值，因此压缩测量的数字通信需要量化。在这样的量化压缩感测（QCS）上下文中，我们通过矢量量化的有噪声压缩测量来解决稀疏源的远程获取。我们提出了一种深度编码器-解码器体系结构，该体系结构由编码器深度神经网络（DNN），量化器和解码器DNN组成，可实现低复杂度矢量量化，旨在最小化给定信号重建的均方误差量化率。我们设计了一种使用随机梯度下降和反向传播训练系统块的监督学习方法。提出了克服梯度消失问题的策略。仿真结果表明，与标准的QCS方法相比，所提出的基于DNN的非迭代QCS方法具有更高的速率失真性能和更低的算法复杂度，有利于对延迟敏感的大规模信号应用。