[2602.12426] Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization

[2602.12426] Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization

arXiv - Machine Learning 3 min read Research

Summary

This paper presents an interference-robust non-coherent over-the-air computation (IR-NCOTA) method for decentralized optimization, enhancing consensus estimation in wireless networks despite external interference.

Why It Matters

As decentralized systems grow in importance, ensuring reliable consensus in the presence of interference is critical. This research offers a solution that enhances the performance of optimization algorithms, making it relevant for applications in wireless communication and distributed computing.

Key Takeaways

  • IR-NCOTA improves decentralized optimization by mitigating external interference.
  • The method employs coordinated random rotation and pseudo-random pilot signals.
  • It maintains unbiased consensus estimates, crucial for algorithm convergence.
  • Numerical results show IR-NCOTA outperforms traditional NCOTA in interference scenarios.
  • This research is significant for applications in wireless networks and multi-agent systems.

Electrical Engineering and Systems Science > Signal Processing arXiv:2602.12426 (eess) [Submitted on 12 Feb 2026] Title:Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization Authors:Nicolò Michelusi View a PDF of the paper titled Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization, by Nicol\`o Michelusi View PDF Abstract:Non-coherent over-the-air (NCOTA) computation enables low-latency and bandwidth-efficient decentralized optimization by exploiting the average energy superposition property of wireless channels. It has recently been proposed as a powerful tool for executing consensus-based optimization algorithms in fully decentralized systems. A key advantage of NCOTA is that it enables unbiased consensus estimation without channel state information at either transmitters or receivers, requires no transmission scheduling, and scales efficiently to dense network deployments. However, NCOTA is inherently susceptible to external interference, which can bias the consensus estimate and deteriorate the convergence of the underlying decentralized optimization algorithm. In this paper, we propose a novel interference-robust (IR-)NCOTA scheme. The core idea is to apply a coordinated random rotation of the frame of reference across all nodes, and transmit a pseudo-random pilot signal, allowing to transform external interference into a circularly symmetric distribution with zero mean relative to the rotated fram...

Related Articles

Machine Learning

[D] Looking for definition of open-world ish learning problem

Hello! Recently I did a project where I initially had around 30 target classes. But at inference, the model had to be able to handle a lo...

Reddit - Machine Learning · 1 min ·
[2603.11687] SemBench: A Universal Semantic Framework for LLM Evaluation
Llms

[2603.11687] SemBench: A Universal Semantic Framework for LLM Evaluation

Abstract page for arXiv paper 2603.11687: SemBench: A Universal Semantic Framework for LLM Evaluation

arXiv - AI · 4 min ·
[2603.11583] UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization
Llms

[2603.11583] UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization

Abstract page for arXiv paper 2603.11583: UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization

arXiv - AI · 3 min ·
[2512.05245] STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
Machine Learning

[2512.05245] STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings

Abstract page for arXiv paper 2512.05245: STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology...

arXiv - Machine Learning · 4 min ·
More in Nlp: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime