LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers

Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian, Yonina C. Eldar

Research output: Contribution to journalArticlepeer-review

18 Scopus citations
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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.

Original languageEnglish
Pages (from-to)5651-5664
Number of pages14
JournalIEEE Transactions on Signal Processing
StatePublished - 4 Oct 2021


  • Model-based deep learning
  • deep unfolding
  • low-resolution signal processing
  • machine learning
  • massive MIMO
  • one-bit quantization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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