42 citations · 43 across the 16 of their papers we have counts for
17 papers
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…
Multi-Hop RIS ISAC for Target Positioning: A Tensor Decomposition-based Approach
Yirui Luo, Xiaoyan Ma, Yong Liang Guan +2
Reconfigurable intelligent surface (RIS) has demon- strated remarkable potential to enhance the performance of integrated sensing and communication (ISAC), particularly when the li…
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…
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
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 ado…
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…
Optimal RIS Placement in Multi-User MISO Systems with User Randomness
Abhishek Rajasekaran, Mehdi Karbalayghareh, Xiaoyan Ma +2
It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works cons…
Coherence-Aware Distributed Learning under Heterogeneous Downlink Impairments
Mehdi Karbalayghareh, David J. Love, Christopher G. Brinton
The performance of federated learning (FL) over wireless networks critically depends on accurate and timely channel state information (CSI) across distributed devices. This require…
Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Evan Chen, Seyyedali Hosseinalipour, Christopher G. Brinton +1
Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, the…
Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…
Federated Learning for Cyber Physical Systems: A Comprehensive Survey
Minh K. Quan, Pubudu N. Pathirana, Mayuri Wijayasundara +5
The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliabili…
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning
Satyavrat Wagle, Anindya Bijoy Das, David J. Love +1
Augmenting federated learning (FL) with device-to-device (D2D) communications can help improve convergence speed and reduce model bias through local information exchange. However,…
Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis
David R. Nickel, Anindya Bijoy Das, David J. Love +1
Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approach…
Physics-Informed Generative Approaches for Wireless Channel Modeling
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
Seohyun Lee, Wenzhi Fang, Anindya Bijoy Das +3
Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While the…
Sparsity-Preserving Encodings for Straggler-Optimal Distributed Matrix Computations at the Edge
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Matrix computations are a fundamental building-block of edge computing systems, with a major recent uptick in demand due to their use in AI/ML training and inference procedures. Ex…
Constant Modulus Waveform Design with Space-Time Sidelobe Reduction for DFRC Systems
Byunghyun Lee, Anindya Bijoy Das, David Love +2
Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designi…