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

· Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices · Depth: Advanced, quick

Summary

Non-coherent over-the-air (NCOTA) computation facilitates low-latency and bandwidth-efficient decentralized optimization by leveraging the average energy superposition of wireless channels. This technique is effective for consensus-based optimization in fully decentralized systems, offering unbiased consensus estimation without requiring channel state information or transmission scheduling, and scaling efficiently in dense networks. However, NCOTA is vulnerable to external interference, which can bias consensus estimates and degrade optimization algorithm convergence. A novel interference-robust (IR-)NCOTA scheme addresses this by applying a coordinated random rotation of the frame of reference across all nodes and transmitting a pseudo-random pilot signal. This transforms external interference into a circularly symmetric, zero-mean distribution relative to the rotated frame, ensuring unbiased consensus estimates and preserving optimization algorithm convergence. Numerical results on a classification task confirm IR-NCOTA's superior performance compared to baseline NCOTA under external interference.

Key takeaway

For research scientists developing decentralized optimization algorithms in wireless environments, implementing the IR-NCOTA scheme is crucial. This approach ensures unbiased consensus estimates and preserves convergence guarantees even when external interference is present, significantly enhancing system reliability and performance in real-world deployments.

Key insights

IR-NCOTA uses coordinated random rotation and pilot signals to mitigate external interference in decentralized optimization.

Principles

Method

The IR-NCOTA scheme applies a coordinated random rotation of the frame of reference across all nodes and transmits a pseudo-random pilot signal to transform external interference into a circularly symmetric, zero-mean distribution.

In practice

Topics

Best for: Research Scientist, AI Researcher, AI Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.