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AeroLat: Channel-Aware Latent Space Semantic Communication for Decentralized UAV Swarms

arXiv 2609.16947 ↗First posted 15 Sep 2026cs.NI cs.AIcited by 0

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Abstract (arXiv)

Communication in latent space offers an intriguing alternative to symbolic messages for decentralized autonomous Unmanned Aerial Vehicle (UAV) swarms operating over bandwidth-constrained, time-varying wireless links. However, when homogeneous frozen models are prompted with discretized perceptual inputs, their broadcast states collapse toward the shared prompt template. In view of this, we propose AeroLat, a channel-aware latent semantic communication framework that uses evidence injection. The resulting latent states are then passed through an explicit communication model that encompasses bandwidth-limited serialization, additive noise and information staleness, which facilitates a joint assessment of communication fidelity and swarm-level coordination. Across multi-seed simulations, AeroLat provably remains resilient to codec choice, faults and increasing swarm size. It consistently reproduces the latent-swarm anomaly, while no-whitening controls recover the collapse. In particular, AeroLat is capable of reducing false similarity by 97.5%.

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References · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  11. 2025KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systemscited by 40
  12. 2025Enabling Agents to Communicate Entirely in Latent Spacecited by 34
6 more
  1. 2025Multimodal Chain of Continuous Thought for Latent-Space Reasoning in Vision-Language Modelscited by 32
  2. 2025Thought Communication in Multiagent Collaborationcited by 30
  3. 2021Learning Semantics: An Opportunity for Effective 6G Communicationscited by 20
  4. 2024Semantic Communication Enhanced by Knowledge Graph Representation Learningcited by 5
  5. 2026Representational Collapse in Multi-Agent LLM Committees: Measurement and Diversity-Aware Consensuscited by 3
  6. 2020UAV-to-Ground Communications: Channel Modeling and UAV Selectioncited by 2

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