# Spatially Distributed Task-Oriented Compression for Multi-Emitter Localization and Characterization with Spectral Overlap

arXiv: https://arxiv.org/abs/2606.01446 · HTML: https://mlsanity.com/paper/2606.01446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 6 of 6)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [DeepMTL Pro: Deep Learning Based MultipleTransmitter Localization and Power Estimation](https://mlsanity.com/paper/2112.13181/) (2021)

## Abstract (arXiv)

Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments. In this work, we propose a task-oriented distributed compression framework for joint multi-emitter localization and characterization using spatially distributed receivers. Each receiver observes a short window of complex IQ samples, converts the observation to a time--frequency representation, and encodes it into a compact latent vector. A central fusion decoder combines the receiver latents to estimate an unordered set of active emitters, including their locations, center-frequency offsets, occupied bandwidths, and waveform families. A permutation-invariant training objective is used to handle the arbitrary ordering of emitters and predictions. Experiments on synthetic multi-emitter scenes with spectral overlap show that even extremely compact receiver-side representations can preserve useful information for emitter counting and waveform-family estimation. However, accurate localization and spectral-parameter regression require larger latent dimensions. Increasing the receiver latent dimension from drx=1 to drx=16 provides the largest improvement, while further increasing to drx=64 gives smaller gains. These results demonstrate the potential of learned task-oriented compression for communication-efficient distributed spectrum awareness.