1 citations · 2 across the 22 of their papers we have counts for
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Learning OTA: A Unified Framework for Edge Sensing, Computation, and Communication
Mehdi Karbalayghareh, David J. Love, Christopher G. Brinton
Most federated learning (FL) studies implicitly assume that training data is readily available at participating edge devices. In practice, this data must first be acquired through…
Distributed Edge Learning under Imperfect Data Sensing
Mehdi Karbalayghareh, David J. Love, Christopher G. Brinton
Distributed learning systems typically assume that local data is already available at clients with fixed quality, while in practice, data is sensed through imperfect physical proce…
AISAC: Closing the Loop Between AI and Integrated Sensing and Communication for 6G
Mehdi Karbalayghareh, Abhishek Rajasekaran, Xiaoyan Ma +2
Integrated sensing and communication (ISAC) and AI-and-communication (AIAC) are identified as separate usage scenarios in the ITU IMT-2030 vision for sixth-generation (6G) networks…
Optimal Multi-RIS Placement: Coverage-Guaranteed Sum Rate Maximization Under Inhomogeneous User Distributions
Abhishek Rajasekaran, Mehdi Karbalayghareh, Xiaoyan Ma +3
Reconfigurable Intelligent Surface (RIS) has emerged as a promising next-generation technology that improves the throughput and coverage of a wireless system. The realization of th…
Unlocking Realism and Interpretability in Wireless Channel Synthesis: A Physics-Guided Generative Approach
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular for wireless communication systems. These require large amounts of data reflecting the behavior of r…
Coherence-Aware Over-the-Air Distributed Learning under Heterogeneous Link Impairments
Mehdi Karbalayghareh, David J. Love, Christopher G. Brinton
Distributed machine learning (ML) over wireless networks hinges on accurate channel state information (CSI) and efficient exchange of high-dimensional model updates. These demands…