Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization
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Abstract (arXiv)
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 frame. This ensures that the consensus estimates remain unbiased, preserving the convergence guarantees of the underlying optimization algorithm. Through numerical results on a classification task, it is demonstrated that IR-NCOTA exhibits superior performance over the baseline NCOTA algorithm in the presence of external interference.
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References · 12
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
- 2013On the Convergence of Decentralized Gradient Descentcited by 158
- 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
- 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
- 2018An Exact Quantized Decentralized Gradient Descent Algorithmcited by 34
- 2021Over-the-Air Decentralized Federated Learningcited by 18
- 2020Decentralized SGD with Over-the-Air Computationcited by 16
- 2022Asynchronous Decentralized Learning over Unreliable Wireless Networkscited by 13
- 2025Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Frameworkcited by 10
- 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
- 2022Decentralized Federated Learning via Non-Coherent Over-the-Air Consensuscited by 4
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