5 papers indexed
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 processes whose quality depends on modality, resolution, sensing power, and sample size. We model sensing…
Xiaoyan Ma, Shahryar Zehtabi, Yinan Zou, Taejoon Kim, Christopher G. Brinton
Over-the-air (OTA) computation has recently gained significant attentions as an effective approach to enhance the communication efficiency of wireless federated learning (FL). By enabling simultaneous transmission and aggregation of local model updates, OTA-FL can substantially r…
Mehdi Karbalayghareh, Abhishek Rajasekaran, Xiaoyan Ma, David J. Love, Christopher G. Brinton
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. In practice, however, these two directions are already beginning to merge. ISAC gives the network…
Abhishek Rajasekaran, Mehdi Karbalayghareh, Xiaoyan Ma, David J. Love, Christopher G. Brinton
The realization of the full potential of Reconfigurable Intelligent Surfaces (RIS) in a wireless system is tied to their strategic spatial deployment. While existing literature primarily focuses on enabling fairness by maximizing coverage to navigate through obstacles, these appr…
Seyed Mohammad Azimi-Abarghouyi, Christopher G. Brinton
This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS). We adopt the widely studied learning-oriented AirFL formulation, which seeks to maximize the number of sel…