11 citations · 13 across the 13 of their papers we have counts for
5 papers · 1 filter
A Unified Activity Detection Framework for Massive Access: Beyond the Block-Fading Paradigm
Jianan Bai, Erik G. Larsson
The wireless channel changes continuously with time and frequency and the block-fading assumption, which is popular in many theoretical analyses, never holds true in practical scen…
Delay-Constrained Grant-Free Random Access in MIMO Systems: Distributed Pilot Allocation and Power Control
Jianan Bai, Zheng Chen, Erik. G. Larsson
We study a delay-constrained grant-free random access system with a multi-antenna base station. The users randomly generate data packets with expiration deadlines, which are then t…
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without di…
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…
Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks
Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson
Consensus-based decentralized stochastic gradient descent (D-SGD) is a widely adopted algorithm for decentralized training of machine learning models across networked agents. A cru…