Deep learning improves detection in networks using low-bit messages
Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization
Information Theory
Summary
Detecting important signals across a network with many devices often requires sending short, limited messages to save bandwidth. This can make recognizing signals less accurate, especially when details about noise are unknown. The authors propose a method that uses deep learning combined with mathematical models to design smarter message encoding and combining strategies. Their approach helps networks detect signals more accurately even with such limited communication. Tests show the method works well as networks grow or signals change.
What this means in practice
- •For wireless sensor networks engineers: Design distributed detection systems that maintain accuracy while using very limited bits for sensor communication.
- •For industrial iot developers: Implement cooperative inference algorithms to detect events using quantized sensor data under bandwidth constraints.
Authors
Wei Guo, Meng He, Chuan Huang, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief
Abstract
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 plug-in 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.