The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on the extreme case of one-bit quantizers, and propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements. Our method is a model-aware data-driven architecture based on deep unfolding of first-order optimization iterations. LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest from the one-bit noisy measurements without requiring prior knowledge of the channel matrix through which the one-bit measurements are obtained. The proposed deep detector has much fewer parameters compared to black-box deep networks due to the incorporation of domain-knowledge in the design of its architecture, allowing it to operate in a data-driven fashion while benefiting from the flexibility, versatility, and reliability of model-based optimization methods. LoRD-Net operates in a blind fashion, which requires addressing both the non-linear nature of the data-acquisition system as well as identifying a proper optimization objective for signal recovery. Accordingly, we propose a two-stage training method for LoRD-Net, in which the first stage is dedicated to identifying the proper form of the optimization process to unfold, while the latter trains the resulting model in an end-to-end manner. We numerically evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications and demonstrate that the proposed hybrid methodology outperforms both data-driven and model-based state-of-the-art methods, while utilizing small datasets, on the order of merely $\sim 500$ samples, for training.

中文翻译：

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LoRD-Net：具有低分辨率接收器的展开深度检测网络

在通信和传感中广泛遇到需要从嘈杂的低分辨率量化测量中恢复高维信号。在本文中，我们关注一位量化器的极端情况，并提出了一种名为 LoRD-Net 的深度检测器，用于从一位测量中恢复信息符号。我们的方法是一种基于一阶优化迭代深度展开的模型感知数据驱动架构。LoRD-Net 具有基于任务的架构，专用于从一位噪声测量中恢复感兴趣的潜在信号，而无需事先了解获得一位测量的信道矩阵。由于在其架构设计中结合了领域知识，所提出的深度检测器与黑盒深度网络相比具有更少的参数，允许它以数据驱动的方式运行，同时受益于基于模型的优化方法的灵活性、多功能性和可靠性。LoRD-Net 以盲目的方式运行，这需要解决数据采集系统的非线性特性以及确定适当的信号恢复优化目标。因此，我们为 LoRD-Net 提出了一种两阶段训练方法，其中第一阶段专门用于确定要展开的优化过程的正确形式，而后者以端到端的方式训练生成的模型。我们对无线通信中的一位信号恢复所提出的接收器架构进行了数值评估，并证明所提出的混合方法优于数据驱动和基于模型的最先进方法，$\sim 500$ 样本，用于训练。