# Multi-bit Distributed Detection of Sparse Stochastic Signals over Error-Prone Reporting Channels

arXiv: https://arxiv.org/abs/2411.03612 · HTML: https://mlsanity.com/paper/2411.03612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2304.00721/) (2023)
- [Iterative Bayesian Reconstruction of Non-IID Block-Sparse Signals](https://mlsanity.com/paper/1412.2316/) (2014)
- [Performance Analysis and Approximate Message Passing Detection of Orthogonal Time Sequency Multiplexing Modulation](https://mlsanity.com/paper/2307.03028/) (2023)
- [Space-Time Shift Keying Aided OTFS Modulation for Orthogonal Multiple Access](https://mlsanity.com/paper/2309.03771/) (2023)
- [Double-detector for Sparse Signal Detection from One Bit Compressed Sensing Measurements](https://mlsanity.com/paper/1607.00494/) (2016)
- [Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks with Imperfect Channels](https://mlsanity.com/paper/1902.03990/) (2019)
- [Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios](https://mlsanity.com/paper/1908.09656/) (2019)
- [On fully-distributed composite tests with general parametric data distributions in sensor networks](https://mlsanity.com/paper/2003.02490/) (2020)

## Built on by (most cited first, 1 of 1)

- [Joint Extremum Compression and Detection of a Time-Delayed Signal for Distributed Sensing](https://mlsanity.com/paper/2607.10874/) (2026)

## Abstract (arXiv)

We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic signals. To achieve a trade-off between detection performance and system constraints, multi-bit quantizers are employed at local sensors. Then, two quantization strategies, namely raw quantization (RQ) and likelihood ratio quantization (LQ), are examined. The multi-bit quantized signals undergo encoding into binary codewords and are subsequently transmitted to the fusion center via error-prone reporting channels. Upon exploiting the locally most powerful test (LMPT) strategy, we devise two multi-bit LMPT detectors in which quantized raw observations and local likelihood ratios are fused respectively. Moreover, the asymptotic detection performance of the proposed quantized detectors is analyzed, and closed-form expressions for the detection and false alarm probabilities are derived. Furthermore, the multi-bit quantizer design criterion, considering both RQ and LQ, is then proposed to achieve near-optimal asymptotic performance for our proposed detectors. The normalized Fisher information and asymptotic relative efficiency are derived, serving as tools to analyze and compensate for the loss of information introduced by the quantization. Simulation results validate the effectiveness of the proposed detectors, especially in scenarios with low signal-to-noise ratios and poor channel conditions.