5 papers
Decentralized Power Control for Over-the-Air Computation with Phase Noise
Martin Dahl, Erik G. Larsson
Estimation of uplink channels is required for coherent over-the-air computation (OAC). When channel estimation is done using calibrated reciprocity, the estimates are only availabl…
A Unified Framework for Unbiased Non-Coherent Over-the-Air Computation
Martin Dahl, Zheng Chen, Erik G. Larsson
Over-the-Air Computation (OAC) enables efficient data aggregation in large-scale distributed systems by exploiting the superposition property of wireless multiple-access channels.…
Hybrid Channel Estimation with Quantized Phase Feedback for Over-the-Air Computation
Martin Dahl, Erik G. Larsson
To reduce the signaling overhead of over-the-air computation, a hybrid channel estimation scheme is proposed, where reciprocity-based and feedback-based channel estimation are comb…
Over-the-Air Federated Learning with Phase Noise: Analysis and Countermeasures
Martin Dahl, Erik G. Larsson
Wirelessly connected devices can collaborately train a machine learning model using federated learning, where the aggregation of model updates occurs using over-the-air computation…
Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access
Zheng Chen, Martin Dahl, Erik G. Larsson
In this work, we focus on the communication aspect of decentralized learning, which involves multiple agents training a shared machine learning model using decentralized stochastic…