Wireless Broadcast Gossip for Decentralized Drone Swarms: Success Probability, Contraction, and Optimal Aloha

📅 2026-03-19
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🤖 AI Summary
This work addresses the trade-off between communication reliability and convergence speed in decentralized drone swarms achieving consensus via gossip protocols in interference-limited wireless environments. Focusing on slotted Aloha-based wireless gossip, the authors model drone locations as a planar Poisson point process and derive a closed-form signal-to-interference ratio (SIR) success probability under Rayleigh fading channels. By decomposing the dynamics into an ideal mixing term and a wireless sparsity term, they establish a mean-square contraction bound. Building on this analysis, they propose a closed-form optimal transmission probability that jointly optimizes availability and reliability. The theoretical framework integrates stochastic geometry, mean-field approximation, and convex optimization, with the predicted optimal operating point aligning closely with the fastest convergence region observed in simulations, thereby validating the approach.

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📝 Abstract
We study broadcast gossip for decentralized drone swarms over an interference-limited wireless medium. Modeling drone locations as a planar Poisson point process and medium access via slotted Aloha, we derive (i) a closed-form SIR success probability under Rayleigh fading, (ii) a mean-square contraction bound in which the consensus rate factorizes into an ideal mixing term and an explicit wireless thinning term, and (iii) a closed-form access probability that optimizes a sharp availability--reliability proxy. Simulations corroborate the predicted operating point by matching the fastest convergence region.
Problem

Research questions and friction points this paper is trying to address.

Wireless Broadcast Gossip
Decentralized Drone Swarms
Interference-limited Medium
Success Probability
Optimal Aloha
Innovation

Methods, ideas, or system contributions that make the work stand out.

broadcast gossip
decentralized drone swarms
SIR success probability
mean-square contraction
optimal Aloha
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A
Ali Khalesi
Institut Polytechnique des Sciences Avancées (IPSA) and LINCS Lab, Paris, France