Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization

Wei Guo

doi:10.1109/jsac.2026.3746433

Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization

2026-10-23

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Jun Zhang (Jun Zhang)

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Khaled B. Letaief

DOI：10.1109/jsac.2026.3746433

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Distributed detection plays a fundamental role in communication-efficient collaborative inference over large-scale networks, where spatially distributed nodes must cooperate using severely quantized messages due to bandwidth constraints. While low-bit quantization is essential for reducing communication overhead, it can significantly degrade detection performance, particularly when prior knowledge of observation noise is unavailable. To address this challenge, we propose a model-driven deep learning framework for distributed detection with low-bit quantization. For fixed local quantizers, we first derive the exact finite-sensor maximum a posteriori (MAP) detection error and prove that, under conditionally independent and identically distributed (i.i.d.) observations, independent local randomizations, and a common quantization-level constraint, identical quantizers preserve the supremum of the average network Chernoff information. Guided by these results, we develop a binary probabilistic quantizer with a trainable neural probability controller and design a tree-structured probabilistic quantizer for scalable multi-bit quantization. The exact finite-sensor MAP error provides the training objective for the binary quantizer, while we derive a computationally tractable Bhattacharyya surrogate objective for training the multi-bit quantizer. For both cases, we derive analytical MAP fusion rules based on the class-conditional probability mass functions (PMFs) of the quantized messages. When the observation statistics are unknown, these PMFs are estimated from held-out labeled data, yielding plugin MAP detectors. Simulation results show that the proposed method approaches centralized-oracle performance at higher quantization resolutions and remains effective under changes in network size and test-time signal amplitude.

Keywords:

Distributed detection

low-bit quantization

maximum a posteriori (MAP) detection

model-driven deep learning (DL)

Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization

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